• Intrusion Detection based on KDD Cup Dataset

    Final Presentation for Big Data Analysis

    published: 05 May 2015
  • chongshm Destroy All Illegal network intrusions with big data techs

    KDDCUP 99 by Chongshen Ma, Carnegie Mellon University.

    published: 05 May 2015
  • Zac Brown, Hidden Treasure: Detecting Intrusions with ETW, Ops Track

    For more information on ACoD and it's mission of building a conference for defense ("Art Into Science"): http://artintoscience.com/ Talk abstract: Today, defenders consume the Windows Event Log to detect intrusions. While useful, audit logs don't capture the full range of data needed for detection and response. ETW (Event Tracing for Windows) is an additional source of real-time events that defenders can leverage to make post-breach activity more visible in Windows. In this talk, we’ll discuss: * what new sources of data ETW gives you * how to capture ETW events at scale using the open-source krabsetw library we developed in Office 365 * what detections we were able to build with ETW data

    published: 17 Apr 2017
  • Final Year Projects | Effective Analysis of KDD data for Intrusion Detection

    Final Year Projects | Effective Analysis of KDD data for Intrusion Detection More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get...

    published: 28 May 2013
  • Intrusion Detection System Introduction, Types of Intruders in Hindi with Example

    Intrusion Detection System Introduction, Types of Intruders in Hindi with Example Like FB Page - https://www.facebook.com/Easy-Engineering-Classes-346838485669475/ Complete Data Structure Videos - https://www.youtube.com/playlist?list=PLV8vIYTIdSna11Vc54-abg33JtVZiiMfg Complete Java Programming Lectures - https://www.youtube.com/playlist?list=PLV8vIYTIdSnbL_fSaqiYpPh-KwNCavjIr Previous Years Solved Questions of Java - https://www.youtube.com/playlist?list=PLV8vIYTIdSnajIVnIOOJTNdLT-TqiOjUu Complete DBMS Video Lectures - https://www.youtube.com/playlist?list=PLV8vIYTIdSnYZjtUDQ5-9siMc2d8YeoB4 Previous Year Solved DBMS Questions - https://www.youtube.com/playlist?list=PLV8vIYTIdSnaPiMXU2bmuo3SWjNUykbg6 SQL Programming Tutorials - https://www.youtube.com/playlist?list=PLV8vIYTIdSnb7av...

    published: 06 Dec 2016
  • What is INTRUSION DETECTION SYSTEM? What does INTRUSION DETECTION SYSTEM mean?

    What is INTRUSION DETECTION SYSTEM? What does INTRUSION DETECTION SYSTEM mean? INTRUSION DETECTION SYSTEM meaning - INTRUSION DETECTION SYSTEM definition - INTRUSION DETECTION SYSTEM explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. An intrusion detection system (IDS) is a device or software application that monitors a network or systems for malicious activity or policy violations. Any detected activity or violation is typically reported either to an administrator or collected centrally using a security information and event management (SIEM) system. A SIEM system combines outputs from multiple sources, and uses alarm filtering techniques to distinguish malicious activity from false alarms. There is a wide spectrum of IDS,...

    published: 30 Mar 2017
  • Machine Learning for Real-Time Anomaly Detection in Network Time-Series Data - Jaeseong Jeong

    Real-time anomaly detection plays a key role in ensuring that the network operation is under control, by taking actions on detected anomalies. In this talk, we discuss a problem of the real-time anomaly detection on a non-stationary (i.e., seasonal) time-series data of several network KPIs. We present two anomaly detection algorithms leveraging machine learning techniques, both of which are able to adaptively learn the underlying seasonal patterns in the data. Jaeseong Jeong is a researcher at Ericsson Research, Machine Learning team. His research interests include large-scale machine learning, telecom data analytics, human behavior predictions, and algorithms for mobile networks. He received the B.S., M.S., and Ph.D. degrees from Korea Advanced Institute of Science and Technology (KAIST)...

    published: 01 Dec 2016
  • Data Mining for Network Intrusion Detection

    Data Mining for Network Intrusion Detection: Experience with KDDCup’99 Data set

    published: 05 May 2015
  • KDD99 - Machine Learning for Intrusion Detectors from attacking data

    Machine Learning for Intrusion Detectors from attacking data

    published: 05 May 2015
  • Final Year Projects | SELF CONFIGURING INTRUSION DETECTION SYSTEM

    Final Year Projects | SELF CONFIGURING INTRUSION DETECTION SYSTEM More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ ...

    published: 11 May 2013
  • Detecting Network Intrusions With Machine Learning Based Anomaly Detection Techniques

    Machine learning techniques used in network intrusion detection are susceptible to “model poisoning” by attackers. The speaker will dissect this attack, analyze some proposals for how to circumvent such attacks, and then consider specific use cases of how machine learning and anomaly detection can be used in the web security context. Author: Clarence Chio More: http://www.phdays.com/program/tech/40866/

    published: 27 Jul 2015
  • Detection of Cyber-Physical Faults and Intrusions from Physical Correlations

    Author: Andrey Lokhov, Los Alamos National Laboratory More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/

    published: 10 Nov 2016
  • IDS - Statistical Anomaly (threshold, profile based) and Rule Based Detection, Honeypots(Hindi)

    Intrusion Detection System – Statistical Anomaly (threshold, profile based) and Rule Based Detection, Honeypots Like FB Page - https://www.facebook.com/Easy-Engineering-Classes-346838485669475/ Complete Data Structure Videos - https://www.youtube.com/playlist?list=PLV8vIYTIdSna11Vc54-abg33JtVZiiMfg Complete Java Programming Lectures - https://www.youtube.com/playlist?list=PLV8vIYTIdSnbL_fSaqiYpPh-KwNCavjIr Previous Years Solved Questions of Java - https://www.youtube.com/playlist?list=PLV8vIYTIdSnajIVnIOOJTNdLT-TqiOjUu Complete DBMS Video Lectures - https://www.youtube.com/playlist?list=PLV8vIYTIdSnYZjtUDQ5-9siMc2d8YeoB4 Previous Year Solved DBMS Questions - https://www.youtube.com/playlist?list=PLV8vIYTIdSnaPiMXU2bmuo3SWjNUykbg6 SQL Programming Tutorials - https://www.youtube.com/...

    published: 06 Dec 2016
  • Catchr - Secretly Detect Intrusions

    App Store Link: http://bit.ly/GetCatchrI App Page Link: http://www.getcatchr.com ••••• Special launch price -- 33% off for a limited time ••••• Catchr provides the opportunity to subtly detect if somebody else has been going through your phone while it was out of sight. It detects this by monitoring applications that have been started or terminated while also recording the duration of the actions that took place during the owner's absence. This makes it a personal "privacy guardian", ensuring that private stuff stays private.

    published: 10 Feb 2014
  • Data Science Capstone Project "Network Intrusion Detection"

    Contributed by Ho Fai Wong, Joseph Wang, Radhey Shyam, & Wanda Wang. They enrolled in the NYC Data Science Academy 12-Week Data Science Bootcamp taking place between April 11th to July 1st, 2016. This post is based on their final class project - Capstone, due on the 12th week of the program. Network intrusions have become commonplace today, with enterprises and governmental organizations fully recognizing the need for accurate and efficient network intrusion detection, while balancing network security and network reliability. Our Capstone project tackled exactly this challenge: applying machine learning models for network intrusion detection. Learn more: http://blog.nycdatascience.com/r/network-intrusion-detection/

    published: 03 Aug 2016
  • Hindi- Intrusion Detection Systems IDS and its Types (Network + Host Based)

    Intrusion Detection Systems (IDS) and its Types (Network + Host Based) in Hindi Intro An intrusion detection system (IDS) is a device or software application that monitors a network or systems for malicious activity or policy violations. Any detected activity or violation is typically reported either to an administrator or collected centrally using a security information and event management (SIEM) system. A SIEM system combines outputs from multiple sources, and uses alarm filtering techniques to distinguish malicious activity from false alarms. There is a wide spectrum of IDS, varying from antivirus software to hierarchical systems that monitor the traffic of an entire backbone network.[citation needed] The most common classifications are network intrusion detection systems (NIDS) and h...

    published: 29 Mar 2017
  • Threat Stack Demo

    Learn how Threat Stack helps you protect your cloud from intrusions & data loss by continuously monitoring and providing insights into your system activity.

    published: 07 Dec 2015
  • Analysis of Intrusion Detection from KDD Cup 99 Dataset both Labelled and Unlabelled

    Title: Analysis of Intrusion Detection from KDD Cup 99 Dataset both Labelled and Unlabelled Domain: Data Mining Description: Intrusion Detection is one of the high priorities & the challenging tasks for network administrators & security experts. Intrusion detection system is employed to protect the data integrity, confidentiality and system availability from attacks. Data mining has been used extensively and broadly by several network organizations. IDS use the data mining techniques to analyze the resources from the database over a network. It is also necessary to develop a robust algorithm to generate effective rules for detecting the attacks. In this paper a flexible architectural system is proposed that uses Associative Classification (AC) method called Multi-label Classifier based ...

    published: 05 Jul 2015
  • Intrusion Detection (IDS) Best Practices

    Learn the top intrusion detection best practices. In network security no other tool is as valuable as intrusion detection. The ability to locate and identify malicious activity on your network by examining network traffic in real time gives you visibility unrivaled by any other detective control. More about intrusion detection with AlienVault: https://www.alienvault.com/solutions/intrusion-detection-system First be sure you are using the right tool for the right job. IDS are available in Network and Host forms. Host intrusion detection is installed as an agent on a machine you wish to protect and monitor. Network IDS examines the traffic between hosts - looking for patterns, or signatures, of nefarious behavior. Let’s examine some best practices for Network IDS: • Baselining or Profil...

    published: 24 Nov 2015
  • Intrusion Detection System Using Machine Learning Models

    published: 16 Jul 2015
  • Building an intrusion detection system using a filter-based feature selection algorithm

    Building an intrusion detection system using a filter-based feature selection algorithm in Java TO GET THIS PROJECT IN ONLINE OR THROUGH TRAINING SESSIONS CONTACT: Chennai Office: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai – 83. Landmark: Next to Kotak Mahendra Bank / Bharath Scans. Landline: (044) - 43012642 / Mobile: (0)9952649690 Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry – 9. Landmark: Opp. To Thattanchavady Industrial Estate & Next to VVP Nagar Arch. Landline: (0413) - 4300535 / Mobile: (0)8608600246 / (0)9952649690 Email: jpinfotechprojects@gmail.com, Website: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com Redundant and irrelevant features in data have caused a long-term problem in networ...

    published: 15 Dec 2016
  • How to Improve the Performance of SVM Classifier | Data Mining

    Data Mining Algorithm and intrusion detection system IDS algorithm is being tested in NSL-KDD data-set. I have applied an adaptive learning technique to optimise the output this time... For making our system more efficient and able to generate more accurate result, it is necessary to improve the performance of SVM classifier. Because all the result’s accuracy depend upon data which is generated by SVM Classifier. So when performance of SVM classifier will improve then our results will be closer to the facts automatically. Intrusion detection is used to detect all type of attack caused by data processing over the network. It is fully based on classification of various types of data. So for batter performance you have to improve the performance of SVM classifier. Same as in data mining ...

    published: 05 Jul 2016
  • Intrusion Detection and Response - The Game Between Attacker and Defender

    Originally aired on September 3, 2014. In this webcast, Michael Collins will give you an amazing piece of technology: a real-time intrusion detection system which, if you're monitoring a /16 or larger, has a 100% true positive rate. Are you ready? You will be scanned on ports 22, 25, 80, 135 and 443. Intrusion detection systems are very good at providing a large stream of useless information. Built in an era when attackers built hand-crafted exploits in the backyard woodshed and tested them on systems over slow and extensive periods, they were never really built to handle an Internet where attackers effectively harvest networks for hosts. Michael will discuss building actionable notifications out of intrusion detection systems, the base-rate fallacy, the core statistical problem that li...

    published: 24 Jan 2015
  • 2000-10-11 CERIAS - Developing Data Mining Techniques for Intrusion Detection: A Progress Report

    Recorded: 10/11/2000 CERIAS Security Seminar at Purdue University Developing Data Mining Techniques for Intrusion Detection: A Progress Report Wenke Lee, North Carolina State University Intrusion detection (ID) is an important component of infrastructure protection mechanisms. Intrusion detection systems (IDSs) need to be accurate, adaptive, extensible, and cost-effective. These requirements are very challenging because of the complexities of today's network environments and the lack of IDS development tools. Our research aims to systematically improve the development process of IDSs. In the first half of the talk, I will describe our data mining framework for constructing ID models. This framework mines activity patterns from system audit data and extracts predictive features from t...

    published: 09 Sep 2013
developed with YouTube
Intrusion Detection based on KDD Cup Dataset

Intrusion Detection based on KDD Cup Dataset

  • Order:
  • Duration: 18:41
  • Updated: 05 May 2015
  • views: 4669
videos https://wn.com/Intrusion_Detection_Based_On_Kdd_Cup_Dataset
chongshm Destroy All Illegal network intrusions with big data techs

chongshm Destroy All Illegal network intrusions with big data techs

  • Order:
  • Duration: 26:50
  • Updated: 05 May 2015
  • views: 11
videos
KDDCUP 99 by Chongshen Ma, Carnegie Mellon University.
https://wn.com/Chongshm_Destroy_All_Illegal_Network_Intrusions_With_Big_Data_Techs
Zac Brown, Hidden Treasure: Detecting Intrusions with ETW, Ops Track

Zac Brown, Hidden Treasure: Detecting Intrusions with ETW, Ops Track

  • Order:
  • Duration: 21:37
  • Updated: 17 Apr 2017
  • views: 448
videos
For more information on ACoD and it's mission of building a conference for defense ("Art Into Science"): http://artintoscience.com/ Talk abstract: Today, defenders consume the Windows Event Log to detect intrusions. While useful, audit logs don't capture the full range of data needed for detection and response. ETW (Event Tracing for Windows) is an additional source of real-time events that defenders can leverage to make post-breach activity more visible in Windows. In this talk, we’ll discuss: * what new sources of data ETW gives you * how to capture ETW events at scale using the open-source krabsetw library we developed in Office 365 * what detections we were able to build with ETW data
https://wn.com/Zac_Brown,_Hidden_Treasure_Detecting_Intrusions_With_Etw,_Ops_Track
Final Year Projects | Effective Analysis of KDD data for Intrusion Detection

Final Year Projects | Effective Analysis of KDD data for Intrusion Detection

  • Order:
  • Duration: 9:16
  • Updated: 28 May 2013
  • views: 3803
videos
Final Year Projects | Effective Analysis of KDD data for Intrusion Detection More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: info@clickmyproject.com
https://wn.com/Final_Year_Projects_|_Effective_Analysis_Of_Kdd_Data_For_Intrusion_Detection
Intrusion Detection System Introduction, Types of Intruders in Hindi with Example

Intrusion Detection System Introduction, Types of Intruders in Hindi with Example

  • Order:
  • Duration: 9:07
  • Updated: 06 Dec 2016
  • views: 25683
videos
Intrusion Detection System Introduction, Types of Intruders in Hindi with Example Like FB Page - https://www.facebook.com/Easy-Engineering-Classes-346838485669475/ Complete Data Structure Videos - https://www.youtube.com/playlist?list=PLV8vIYTIdSna11Vc54-abg33JtVZiiMfg Complete Java Programming Lectures - https://www.youtube.com/playlist?list=PLV8vIYTIdSnbL_fSaqiYpPh-KwNCavjIr Previous Years Solved Questions of Java - https://www.youtube.com/playlist?list=PLV8vIYTIdSnajIVnIOOJTNdLT-TqiOjUu Complete DBMS Video Lectures - https://www.youtube.com/playlist?list=PLV8vIYTIdSnYZjtUDQ5-9siMc2d8YeoB4 Previous Year Solved DBMS Questions - https://www.youtube.com/playlist?list=PLV8vIYTIdSnaPiMXU2bmuo3SWjNUykbg6 SQL Programming Tutorials - https://www.youtube.com/playlist?list=PLV8vIYTIdSnb7av5opUF2p3Xv9CLwOfbq PL-SQL Programming Tutorials - https://www.youtube.com/playlist?list=PLV8vIYTIdSnadFpRMvtA260-3-jkIDFaG Control System Complete Lectures - https://www.youtube.com/playlist?list=PLV8vIYTIdSnbvRNepz74GGafF-777qYw4
https://wn.com/Intrusion_Detection_System_Introduction,_Types_Of_Intruders_In_Hindi_With_Example
What is INTRUSION DETECTION SYSTEM? What does INTRUSION DETECTION SYSTEM mean?

What is INTRUSION DETECTION SYSTEM? What does INTRUSION DETECTION SYSTEM mean?

  • Order:
  • Duration: 5:09
  • Updated: 30 Mar 2017
  • views: 2973
videos
What is INTRUSION DETECTION SYSTEM? What does INTRUSION DETECTION SYSTEM mean? INTRUSION DETECTION SYSTEM meaning - INTRUSION DETECTION SYSTEM definition - INTRUSION DETECTION SYSTEM explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. An intrusion detection system (IDS) is a device or software application that monitors a network or systems for malicious activity or policy violations. Any detected activity or violation is typically reported either to an administrator or collected centrally using a security information and event management (SIEM) system. A SIEM system combines outputs from multiple sources, and uses alarm filtering techniques to distinguish malicious activity from false alarms. There is a wide spectrum of IDS, varying from antivirus software to hierarchical systems that monitor the traffic of an entire backbone network. The most common classifications are network intrusion detection systems (NIDS) and host-based intrusion detection systems (HIDS). A system that monitors important operating system files is an example of a HIDS, while a system that analyzes incoming network traffic is an example of a NIDS. It is also possible to classify IDS by detection approach: the most well-known variants are signature-based detection (recognizing bad patterns, such as malware) and anomaly-based detection (detecting deviations from a model of "good" traffic, which often relies on machine learning). Some IDS have the ability to respond to detected intrusions. Systems with response capabilities are typically referred to as an intrusion prevention system. Though they both relate to network security, an IDS differs from a firewall in that a firewall looks outwardly for intrusions in order to stop them from happening. Firewalls limit access between networks to prevent intrusion and do not signal an attack from inside the network. An IDS evaluates a suspected intrusion once it has taken place and signals an alarm. An IDS also watches for attacks that originate from within a system. This is traditionally achieved by examining network communications, identifying heuristics and patterns (often known as signatures) of common computer attacks, and taking action to alert operators. A system that terminates connections is called an intrusion prevention system, and is another form of an application layer firewall. Some systems may attempt to stop an intrusion attempt but this is neither required nor expected of a monitoring system. Intrusion detection and prevention systems (IDPS) are primarily focused on identifying possible incidents, logging information about them, and reporting attempts. In addition, organizations use IDPSes for other purposes, such as identifying problems with security policies, documenting existing threats and deterring individuals from violating security policies. IDPSes have become a necessary addition to the security infrastructure of nearly every organization. IDPSes typically record information related to observed events, notify security administrators of important observed events and produce reports. Many IDPSes can also respond to a detected threat by attempting to prevent it from succeeding. They use several response techniques, which involve the IDPS stopping the attack itself, changing the security environment (e.g. reconfiguring a firewall) or changing the attack's content. Intrusion prevention systems (IPS), also known as intrusion detection and prevention systems (IDPS), are network security appliances that monitor network or system activities for malicious activity. The main functions of intrusion prevention systems are to identify malicious activity, log information about this activity, report it and attempt to block or stop it.. Intrusion prevention systems are considered extensions of intrusion detection systems because they both monitor network traffic and/or system activities for malicious activity. The main differences are, unlike intrusion detection systems, intrusion prevention systems are placed in-line and are able to actively prevent or block intrusions that are detected. IPS can take such actions as sending an alarm, dropping detected malicious packets, resetting a connection or blocking traffic from the offending IP address. An IPS also can correct cyclic redundancy check (CRC) errors, defragment packet streams, mitigate TCP sequencing issues, and clean up unwanted transport and network layer options..
https://wn.com/What_Is_Intrusion_Detection_System_What_Does_Intrusion_Detection_System_Mean
Machine Learning for Real-Time Anomaly Detection in Network Time-Series Data - Jaeseong Jeong

Machine Learning for Real-Time Anomaly Detection in Network Time-Series Data - Jaeseong Jeong

  • Order:
  • Duration: 17:45
  • Updated: 01 Dec 2016
  • views: 5762
videos
Real-time anomaly detection plays a key role in ensuring that the network operation is under control, by taking actions on detected anomalies. In this talk, we discuss a problem of the real-time anomaly detection on a non-stationary (i.e., seasonal) time-series data of several network KPIs. We present two anomaly detection algorithms leveraging machine learning techniques, both of which are able to adaptively learn the underlying seasonal patterns in the data. Jaeseong Jeong is a researcher at Ericsson Research, Machine Learning team. His research interests include large-scale machine learning, telecom data analytics, human behavior predictions, and algorithms for mobile networks. He received the B.S., M.S., and Ph.D. degrees from Korea Advanced Institute of Science and Technology (KAIST) in 2008, 2010, and 2014, respectively.
https://wn.com/Machine_Learning_For_Real_Time_Anomaly_Detection_In_Network_Time_Series_Data_Jaeseong_Jeong
Data Mining for Network Intrusion Detection

Data Mining for Network Intrusion Detection

  • Order:
  • Duration: 7:47
  • Updated: 05 May 2015
  • views: 766
videos https://wn.com/Data_Mining_For_Network_Intrusion_Detection
KDD99 - Machine Learning for Intrusion Detectors from attacking data

KDD99 - Machine Learning for Intrusion Detectors from attacking data

  • Order:
  • Duration: 45:56
  • Updated: 05 May 2015
  • views: 2515
videos https://wn.com/Kdd99_Machine_Learning_For_Intrusion_Detectors_From_Attacking_Data
Final Year Projects | SELF CONFIGURING INTRUSION DETECTION SYSTEM

Final Year Projects | SELF CONFIGURING INTRUSION DETECTION SYSTEM

  • Order:
  • Duration: 8:58
  • Updated: 11 May 2013
  • views: 547
videos
Final Year Projects | SELF CONFIGURING INTRUSION DETECTION SYSTEM More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: info@clickmyproject.com
https://wn.com/Final_Year_Projects_|_Self_Configuring_Intrusion_Detection_System
Detecting Network Intrusions With Machine Learning Based Anomaly Detection Techniques

Detecting Network Intrusions With Machine Learning Based Anomaly Detection Techniques

  • Order:
  • Duration: 49:38
  • Updated: 27 Jul 2015
  • views: 6512
videos
Machine learning techniques used in network intrusion detection are susceptible to “model poisoning” by attackers. The speaker will dissect this attack, analyze some proposals for how to circumvent such attacks, and then consider specific use cases of how machine learning and anomaly detection can be used in the web security context. Author: Clarence Chio More: http://www.phdays.com/program/tech/40866/
https://wn.com/Detecting_Network_Intrusions_With_Machine_Learning_Based_Anomaly_Detection_Techniques
Detection of Cyber-Physical Faults and Intrusions from Physical Correlations

Detection of Cyber-Physical Faults and Intrusions from Physical Correlations

  • Order:
  • Duration: 21:55
  • Updated: 10 Nov 2016
  • views: 127
videos
Author: Andrey Lokhov, Los Alamos National Laboratory More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
https://wn.com/Detection_Of_Cyber_Physical_Faults_And_Intrusions_From_Physical_Correlations
IDS - Statistical Anomaly (threshold, profile based) and Rule Based Detection, Honeypots(Hindi)

IDS - Statistical Anomaly (threshold, profile based) and Rule Based Detection, Honeypots(Hindi)

  • Order:
  • Duration: 12:27
  • Updated: 06 Dec 2016
  • views: 7923
videos
Intrusion Detection System – Statistical Anomaly (threshold, profile based) and Rule Based Detection, Honeypots Like FB Page - https://www.facebook.com/Easy-Engineering-Classes-346838485669475/ Complete Data Structure Videos - https://www.youtube.com/playlist?list=PLV8vIYTIdSna11Vc54-abg33JtVZiiMfg Complete Java Programming Lectures - https://www.youtube.com/playlist?list=PLV8vIYTIdSnbL_fSaqiYpPh-KwNCavjIr Previous Years Solved Questions of Java - https://www.youtube.com/playlist?list=PLV8vIYTIdSnajIVnIOOJTNdLT-TqiOjUu Complete DBMS Video Lectures - https://www.youtube.com/playlist?list=PLV8vIYTIdSnYZjtUDQ5-9siMc2d8YeoB4 Previous Year Solved DBMS Questions - https://www.youtube.com/playlist?list=PLV8vIYTIdSnaPiMXU2bmuo3SWjNUykbg6 SQL Programming Tutorials - https://www.youtube.com/playlist?list=PLV8vIYTIdSnb7av5opUF2p3Xv9CLwOfbq PL-SQL Programming Tutorials - https://www.youtube.com/playlist?list=PLV8vIYTIdSnadFpRMvtA260-3-jkIDFaG Control System Complete Lectures - https://www.youtube.com/playlist?list=PLV8vIYTIdSnbvRNepz74GGafF-777qYw4
https://wn.com/Ids_Statistical_Anomaly_(Threshold,_Profile_Based)_And_Rule_Based_Detection,_Honeypots(Hindi)
Catchr - Secretly Detect Intrusions

Catchr - Secretly Detect Intrusions

  • Order:
  • Duration: 1:07
  • Updated: 10 Feb 2014
  • views: 37057
videos
App Store Link: http://bit.ly/GetCatchrI App Page Link: http://www.getcatchr.com ••••• Special launch price -- 33% off for a limited time ••••• Catchr provides the opportunity to subtly detect if somebody else has been going through your phone while it was out of sight. It detects this by monitoring applications that have been started or terminated while also recording the duration of the actions that took place during the owner's absence. This makes it a personal "privacy guardian", ensuring that private stuff stays private.
https://wn.com/Catchr_Secretly_Detect_Intrusions
Data Science Capstone Project "Network Intrusion Detection"

Data Science Capstone Project "Network Intrusion Detection"

  • Order:
  • Duration: 29:30
  • Updated: 03 Aug 2016
  • views: 226
videos
Contributed by Ho Fai Wong, Joseph Wang, Radhey Shyam, & Wanda Wang. They enrolled in the NYC Data Science Academy 12-Week Data Science Bootcamp taking place between April 11th to July 1st, 2016. This post is based on their final class project - Capstone, due on the 12th week of the program. Network intrusions have become commonplace today, with enterprises and governmental organizations fully recognizing the need for accurate and efficient network intrusion detection, while balancing network security and network reliability. Our Capstone project tackled exactly this challenge: applying machine learning models for network intrusion detection. Learn more: http://blog.nycdatascience.com/r/network-intrusion-detection/
https://wn.com/Data_Science_Capstone_Project_Network_Intrusion_Detection
Hindi- Intrusion Detection Systems IDS and its Types (Network + Host Based)

Hindi- Intrusion Detection Systems IDS and its Types (Network + Host Based)

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  • Duration: 6:39
  • Updated: 29 Mar 2017
  • views: 6302
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Intrusion Detection Systems (IDS) and its Types (Network + Host Based) in Hindi Intro An intrusion detection system (IDS) is a device or software application that monitors a network or systems for malicious activity or policy violations. Any detected activity or violation is typically reported either to an administrator or collected centrally using a security information and event management (SIEM) system. A SIEM system combines outputs from multiple sources, and uses alarm filtering techniques to distinguish malicious activity from false alarms. There is a wide spectrum of IDS, varying from antivirus software to hierarchical systems that monitor the traffic of an entire backbone network.[citation needed] The most common classifications are network intrusion detection systems (NIDS) and host-based intrusion detection systems (HIDS). A system that monitors important operating system files is an example of a HIDS, while a system that analyzes incoming network traffic is an example of a NIDS. It is also possible to classify IDS by detection approach: the most well-known variants are signature-based detection (recognizing bad patterns, such as malware) and anomaly-based detection (detecting deviations from a model of "good" traffic, which often relies on machine learning). Some IDS have the ability to respond to detected intrusions. Systems with response capabilities are typically referred to as an intrusion prevention system. Network intrusion detection systems Network intrusion detection systems (NIDS) are placed at a strategic point or points within the network to monitor traffic to and from all devices on the network. It performs an analysis of passing traffic on the entire subnet, and matches the traffic that is passed on the subnets to the library of known attacks. Once an attack is identified, or abnormal behavior is sensed, the alert can be sent to the administrator. An example of an NIDS would be installing it on the subnet where firewalls are located in order to see if someone is trying to break into the firewall. Ideally one would scan all inbound and outbound traffic, however doing so might create a bottleneck that would impair the overall speed of the network. OPNET and NetSim are commonly used tools for simulation network intrusion detection systems. NID Systems are also capable of comparing signatures for similar packets to link and drop harmful detected packets which have a signature matching the records in the NIDS. When we classify the design of the NIDS according to the system interactivity property, there are two types: on-line and off-line NIDS, often referred to as inline and tap mode, respectively. On-line NIDS deals with the network in real time. It analyses the Ethernet packets and applies some rules, to decide if it is an attack or not. Off-line NIDS deals with stored data and passes it through some processes to decide if it is an attack or not. Host intrusion detection systems Host intrusion detection systems (HIDS) run on individual hosts or devices on the network. A HIDS monitors the inbound and outbound packets from the device only and will alert the user or administrator if suspicious activity is detected. It takes a snapshot of existing system files and matches it to the previous snapshot. If the critical system files were modified or deleted, an alert is sent to the administrator to investigate. An example of HIDS usage can be seen on mission critical machines, which are not expected to change their configurations. Intrusion detection systems can also be system-specific using custom tools and honeypots. Find More Info at https://goo.gl/L2XzQg Like Facebook Page https://www.facebook.com/genrontech Follow Twitter Page https://twitter.com/GenronTech Follow Google Pag https://plus.google.com/+Genrontechdotcom Follow Pinterest https://in.pinterest.com/genrontech
https://wn.com/Hindi_Intrusion_Detection_Systems_Ids_And_Its_Types_(Network_Host_Based)
Threat Stack Demo

Threat Stack Demo

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  • Duration: 15:27
  • Updated: 07 Dec 2015
  • views: 1152
videos
Learn how Threat Stack helps you protect your cloud from intrusions & data loss by continuously monitoring and providing insights into your system activity.
https://wn.com/Threat_Stack_Demo
Analysis of Intrusion Detection from KDD Cup 99 Dataset both Labelled and Unlabelled

Analysis of Intrusion Detection from KDD Cup 99 Dataset both Labelled and Unlabelled

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  • Duration: 3:16
  • Updated: 05 Jul 2015
  • views: 1834
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Title: Analysis of Intrusion Detection from KDD Cup 99 Dataset both Labelled and Unlabelled Domain: Data Mining Description: Intrusion Detection is one of the high priorities & the challenging tasks for network administrators & security experts. Intrusion detection system is employed to protect the data integrity, confidentiality and system availability from attacks. Data mining has been used extensively and broadly by several network organizations. IDS use the data mining techniques to analyze the resources from the database over a network. It is also necessary to develop a robust algorithm to generate effective rules for detecting the attacks. In this paper a flexible architectural system is proposed that uses Associative Classification (AC) method called Multi-label Classifier based Associative Classification (MCAC) to get better results in terms of accuracy, false alarm rate, efficiency, capability to detect new type of attacks. For more details contact: E-Mail: lightsoftomorrowtechnologies@gmail.com Buy Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 ieee projects 2. latest ieee projects in java 3. latest ieee projects in cloud computing 4. 2017 – 2018 cloud computing projects 5. 2017 – 2018 cloud simulation projects 6. 2017 – 2018 cloud sim projects 7. 2017 – 2018 best project center in Chennai 8. Ieee cloud simulation projects in Chennai 9. 2017 – 2018 real time cloud hosting 10. 2017 – 2018 aws console ieee projects 11. aws console real time ieee projects 12. ieee projects deployment in aws console 13. cloud computing projects in amazon cloud server 14. ieee projects in Amazon cloud server 15. amazon cloud server ieee projects 16. 2017 ieee real time cloud projects 17. Cloud sim projects in cloud computing 18. ieee projects in green computing 19. ieee projects in cloud computing 20. green computing ieee projects 21. ieee projects in big data 22. ieee projects in hadoop 23. ieee projects in mango db 24. mango db ieee projects 25. hadoop projects in cloud computing 26. cloud sim projects for final year b.e students 27. 2017 – 2018 java projects in Chennai, Coimbatore, Bangalore, and Mysore 28. 2017 – 2018 b.e projects 29. 2017 – 2018 m.e projects 30. 2017 – 2018 final year projects 31. Affordable final year projects 32. Latest final year projects 33. Best project center in Chennai, Coimbatore, Bangalore, and Mysore 34. 2017 Best ieee project titles 35. Best projects in java domain 36. Free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 37. 2017 – 2018 ieee base paper free download 38. 2017 – 2018 ieee titles free download 39. best ieee projects in affordable cost 40. ieee projects free download 41. 2017 cloud sim projects 42. 2017 ieee projects on cloud sim 43. 2017 final year cloud sim projects 44. 2017 cloud sim projects for b.e 45. 2017 cloud sim projects for m.e 46. 2017 latest cloud sim projects 47. latest cloud sim projects 48. latest cloud sim projects in java 49. cloud computing cloud sim projects 50. green computing projects in cloud sim 51. mini projects on big data 52. 2017 mini projects on big data 53. final year project on big data 54. big data topics for project 55. ieee projects based on big data 56. big data project ieee ideas for students 57. big data projects for engineering students 58. 2017 ieee big data projects 59. ieee projects on big data 2017 60. cloud computing projects java source code 61. cloud computing projects with source code 62. 2017 mini project on cloud computing 63. cloud computing mini projects 64. mini projects in java 65. ieee projects on cloud computing 66. projects based on cloud computing 67. best cloud computing projects 68. cloud computing project topics 69. major projects on cloud computing 70. final year projects for cse 71. ieee cloud computing projects for cse 72. cloud computing mini project 73. cloud computing related projects 74. cloud computing based projects for final year 75. cloud computing based project ideas 76. cloud computing application projects 77. cloud computing project topics in java 78. cloud computing projects for engineering students
https://wn.com/Analysis_Of_Intrusion_Detection_From_Kdd_Cup_99_Dataset_Both_Labelled_And_Unlabelled
Intrusion Detection (IDS) Best Practices

Intrusion Detection (IDS) Best Practices

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  • Duration: 2:55
  • Updated: 24 Nov 2015
  • views: 5218
videos
Learn the top intrusion detection best practices. In network security no other tool is as valuable as intrusion detection. The ability to locate and identify malicious activity on your network by examining network traffic in real time gives you visibility unrivaled by any other detective control. More about intrusion detection with AlienVault: https://www.alienvault.com/solutions/intrusion-detection-system First be sure you are using the right tool for the right job. IDS are available in Network and Host forms. Host intrusion detection is installed as an agent on a machine you wish to protect and monitor. Network IDS examines the traffic between hosts - looking for patterns, or signatures, of nefarious behavior. Let’s examine some best practices for Network IDS: • Baselining or Profiling normal network behavior is a key process for IDS deployment. Every environment is different and determining what’s “normal” for your network allows you to focus better on anomalous and potentially malicious behavior. This saves time and brings real threats to the surface for remediation. • Placement of the IDS device is an important consideration. Most often it is deployed behind the firewall on the edge of your network. This gives the highest visibility but it also excludes traffic that occurs between hosts. The right approach is determined by your available resources. Start with the highest point of visibility and work down into your network. • Consider having multiple IDS installations to cover intra-host traffic • Properly size your IDS installation by examining the amount of data that is flowing in BOTH directions at the area you wish to tap or examine. Add overhead for future expansion. • False positives occur when your IDS alerts you to a threat that you know is innocuous. • An improperly tuned IDS will generate an overwhelming number of False Positives. Establishing a policy that removes known False Positives will save time in future investigations and prevent unwarranted escalations. • Asset inventory and information go hand in hand with IDS. Knowing the role, function, and vulnerabilities of an asset will add valuable context to your investigations Next, let’s look at best practices for Host IDS: • The defaults are not enough. • The defaults for HIDS usually only monitor changes to the basic operating system files. They may not have awareness of applications you have installed or proprietary data you wish to safeguard. • Define what critical data resides on your assets and create policies to detect changes in that data • If your company uses custom applications, be sure to include the logs for them in your HIDS configuration • As with Network IDS removing the occurrence of False Positives is critical Finally, let’s examine best practices for WIDS: • Like physical network detection, placement of WIDS is also paramount. • Placement should be within the range of existing wireless signals • Record and Inventory existing Access Point names and whitelist them AlienVault Unified Security Management (USM) includes built-in network, host and wireless IDS’s. In addition to IDS, USM also includes Security Information and Event Management (SIEM), vulnerability management, behavioral network monitoring, asset discovery and more. Please download USM here to see for yourself: https://www.alienvault.com/free-trial
https://wn.com/Intrusion_Detection_(Ids)_Best_Practices
Intrusion Detection System Using Machine Learning Models

Intrusion Detection System Using Machine Learning Models

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  • Duration: 19:13
  • Updated: 16 Jul 2015
  • views: 4394
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https://wn.com/Intrusion_Detection_System_Using_Machine_Learning_Models
Building an intrusion detection system using a filter-based feature selection algorithm

Building an intrusion detection system using a filter-based feature selection algorithm

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  • Duration: 9:43
  • Updated: 15 Dec 2016
  • views: 2564
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Building an intrusion detection system using a filter-based feature selection algorithm in Java TO GET THIS PROJECT IN ONLINE OR THROUGH TRAINING SESSIONS CONTACT: Chennai Office: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai – 83. Landmark: Next to Kotak Mahendra Bank / Bharath Scans. Landline: (044) - 43012642 / Mobile: (0)9952649690 Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry – 9. Landmark: Opp. To Thattanchavady Industrial Estate & Next to VVP Nagar Arch. Landline: (0413) - 4300535 / Mobile: (0)8608600246 / (0)9952649690 Email: jpinfotechprojects@gmail.com, Website: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com Redundant and irrelevant features in data have caused a long-term problem in network traffic classification. These features not only slow down the process of classification but also prevent a classifier from making accurate decisions, especially when coping with big data. In this paper, we propose a mutual information based algorithm that analytically selects the optimal feature for classification. This mutual information based feature selection algorithm can handle linearly and nonlinearly dependent data features. Its effectiveness is evaluated in the cases of network intrusion detection. An Intrusion Detection System (IDS), named Least Square Support Vector Machine based IDS (LSSVM-IDS), is built using the features selected by our proposed feature selection algorithm. The performance of LSSVM-IDS is evaluated using three intrusion detection evaluation datasets, namely KDD Cup 99, NSL-KDD and Kyoto 2006+ dataset. The evaluation results show that our feature selection algorithm contributes more critical features for LSSVM-IDS to achieve better accuracy and lower computational cost compared with the state-of-the-art methods.
https://wn.com/Building_An_Intrusion_Detection_System_Using_A_Filter_Based_Feature_Selection_Algorithm
How to Improve the Performance of SVM Classifier | Data Mining

How to Improve the Performance of SVM Classifier | Data Mining

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  • Duration: 14:28
  • Updated: 05 Jul 2016
  • views: 3787
videos
Data Mining Algorithm and intrusion detection system IDS algorithm is being tested in NSL-KDD data-set. I have applied an adaptive learning technique to optimise the output this time... For making our system more efficient and able to generate more accurate result, it is necessary to improve the performance of SVM classifier. Because all the result’s accuracy depend upon data which is generated by SVM Classifier. So when performance of SVM classifier will improve then our results will be closer to the facts automatically. Intrusion detection is used to detect all type of attack caused by data processing over the network. It is fully based on classification of various types of data. So for batter performance you have to improve the performance of SVM classifier. Same as in data mining there is large amount of data which we have to compute very rapidly. And for this purpose we use SVM Classifier in artificial intelligence in data mining. And in this video tutorial it is clearly shown that how you can improve the performance of SVM Classifier in Data mining and Intrusion detection. And this time I’ve made adaptive my favorite classifier SVM How you will able to improve the performance of SVM up to 37%... For more related work in Artificial Intelligence, Machine Learning and Deep learning please visit our websites. We have customised services portal for different users. For Thesis guidance and dissertation please visit www.researchinfinitesolutions.com www.infinitesolution.in Mail us at : info@researchinfinitesolutions.com For projects please visit our Business to Business portal www.webtunix.com You can also email us at info@webtunix.com Want to trade in Stocks please visit www.deepstocks.net You can email us at info@deepstocks.net Want to use and know about our API please visit www.webtunix.ai You can email us at info@webtunix.ai Want to learn Python and Deep Learning in effective way, go for www.mindcreature.com mail us at info@mindcreature.com This is Ajay Jatav, if you have any further query you can mail me at: ajayjatav@webtunix.com For quick response contact us on skype: live:webtunix
https://wn.com/How_To_Improve_The_Performance_Of_Svm_Classifier_|_Data_Mining
Intrusion Detection and Response - The Game Between Attacker and Defender

Intrusion Detection and Response - The Game Between Attacker and Defender

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  • Duration: 1:08:08
  • Updated: 24 Jan 2015
  • views: 798
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Originally aired on September 3, 2014. In this webcast, Michael Collins will give you an amazing piece of technology: a real-time intrusion detection system which, if you're monitoring a /16 or larger, has a 100% true positive rate. Are you ready? You will be scanned on ports 22, 25, 80, 135 and 443. Intrusion detection systems are very good at providing a large stream of useless information. Built in an era when attackers built hand-crafted exploits in the backyard woodshed and tested them on systems over slow and extensive periods, they were never really built to handle an Internet where attackers effectively harvest networks for hosts. Michael will discuss building actionable notifications out of intrusion detection systems, the base-rate fallacy, the core statistical problem that limits all intrusion detection, the game between attacker and defender, and methods for modifying signature and anomaly-based detection systems to provide more effective detection and analysis. About Michael Collins Michael Collins is the chief scientist for RedJack, LLC., a Network Security and Data Analysis company located in the Washington D.C. area. Prior to his work at RedJack, Dr. Collins was a member of the technical staff at the CERT/Network Situational Awareness group at Carnegie Mellon University. His primary focus is on network instrumentation and traffic analysis, in particular on the analysis of large traffic datasets. Dr. Collins graduated with a PhD in Electrical Engineering from Carnegie Mellon University in 2008, he holds Master's and Bachelor's Degrees from the same institution. - Don't miss an upload! Subscribe! http://goo.gl/szEauh - Stay Connected to O'Reilly Media. Visit http://oreillymedia.com Sign up to one of our newsletters - http://goo.gl/YZSWbO Follow O'Reilly Media: http://plus.google.com/+oreillymedia https://www.facebook.com/OReilly https://twitter.com/OReillyMedia http://www.oreilly.com/webcasts
https://wn.com/Intrusion_Detection_And_Response_The_Game_Between_Attacker_And_Defender
2000-10-11 CERIAS - Developing Data Mining Techniques for Intrusion Detection: A Progress Report

2000-10-11 CERIAS - Developing Data Mining Techniques for Intrusion Detection: A Progress Report

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  • Duration: 1:00:27
  • Updated: 09 Sep 2013
  • views: 1514
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Recorded: 10/11/2000 CERIAS Security Seminar at Purdue University Developing Data Mining Techniques for Intrusion Detection: A Progress Report Wenke Lee, North Carolina State University Intrusion detection (ID) is an important component of infrastructure protection mechanisms. Intrusion detection systems (IDSs) need to be accurate, adaptive, extensible, and cost-effective. These requirements are very challenging because of the complexities of today's network environments and the lack of IDS development tools. Our research aims to systematically improve the development process of IDSs. In the first half of the talk, I will describe our data mining framework for constructing ID models. This framework mines activity patterns from system audit data and extracts predictive features from the patterns. It then applies machine learning algorithms to the audit records, which are processed according to the feature definitions, to generate intrusion detection rules. This framework is a "toolkit" (rather than a "replacement") for the IDS developers. I will discuss the design and implementation issues in utilizing expert domain knowledge in our framework. In the second half of the talk, I will give an overview of our current research efforts, which include: cost-sensitive analysis and modeling techniques for intrusion detection; information-theoretic approaches for anomaly detection; and correlation analysis techniques for understanding attack scenarios and early detection of intrusions. Wenke Lee is an Assistant Professor in the Computer Science Department at North Carolina State University. He received his Ph.D. in Computer Science from Columbia University and B.S. in Computer Science from Zhongshan University, China. His research interests include network security, data mining, and workflow management. He is a Principle Investigator (PI) for research projects in intrusion detection and network management, with funding from DARPA, North Carolina Network Initiatives, Aprisma Management Technologies, and HRL Laboratories. He received a Best Paper Award (applied research category) at the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-99), and Honorable Mention (runner-up) for Best Paper Award (applied research category) at both KDD-98 and KDD-97. He is a member of ACM and IEEE. (Visit: www.cerias.purdue.edu)
https://wn.com/2000_10_11_Cerias_Developing_Data_Mining_Techniques_For_Intrusion_Detection_A_Progress_Report
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