• Intrusion Detection based on KDD Cup Dataset

    Final Presentation for Big Data Analysis

    published: 05 May 2015
  • 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
  • Data Mining for Network Intrusion Detection

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

    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
  • KDD99 - Machine Learning for Intrusion Detectors from attacking data

    Machine Learning for Intrusion Detectors from attacking data

    published: 05 May 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
  • "We Watch You While You Sleep". TV signal intrusion 1975 (Scarfnada TV)

    http://scarfolk.blogspot.com/2014/02/we-watch-you-while-you-sleep-tv-signal.html Here is a rare video from the Scarfolk archives. In 1975 there was a series of anonymous signal intrusions on the Scarfnada TV network. Many believed that the council itself was directly responsible for the illegal broadcasts, though this was never confirmed. However, In 1976 a BBC TV documentary revealed that the council had surreptitiously introduced tranquillisers to the water supply and employed council mediums to sing lullabies outside the bedroom windows of suspect citizens. Once a suspect had fallen asleep, the medium would break into their bedroom and secrete themselves in a wardrobe or beneath the bed. From these vantage points the mediums could record the suspect's dreams and nocturnal mumblings ...

    published: 19 Feb 2014
  • 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
  • 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
  • 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
  • Predictive model for Intrusion Detection System Dataset KDD Cup 1999

    published: 17 Nov 2015
  • Intrusion Detection System Using Machine Learning Models

    published: 16 Jul 2015
  • Attacks in a Network Intrusion Detection System on Artificial Neural Networks (ANN Backup)

    Nowadays with the dramatic growth in communication and computer networks, security has become a critical subject for computer systems. A good way to detect the algorithms, methods and applications are created and implemented to solve the problem of detecting the attacks in intrusion detection systems. Most methods detect attacks and categorize in two groups, normal or threat. This paper presents a new approach of intrusion detection system based on neural network. In this paper, we have a Multi Layer Perceptron (MLP) is used for intrusion detection system. The results show that our implemented and designed system detects the attacks and classify them in 6 groups with the approximately 90.78% accuracy with the two hidden layers of neurons in the neural network.

    published: 04 Oct 2014
  • Uber next in series of database intrusions

    Reports indicated that Uber fell victim to a data breach. Find out how this breach affected customer data.

    published: 19 Mar 2015
  • 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
  • Counteracting Data-Only Malware with Code Pointer Examination

    published: 24 Dec 2015
  • 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
  • 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
  • Wireshark and Recognizing Exploits, HakTip 138

    This week on HakTip, Shannon pinpoints an exploitation using Wireshark. Working on the shoulders of last week's episode, this week we'll discuss what exploits look like in Wireshark. The example I'm sharing is from Practical Packet Analysis, a book by Chris Sanders about Wireshark. Our example packet shows what happens when a user visits a malicious site using a bad version of IE. This is called spear phishing. First, we have HTTP traffic on port 80. We notice there is a 302 moved response from the malicious site and the location is all sorts of weird. Then a bunch of data gets transferred from the new site to the user. Click Follow TCP Stream. If you scroll down, you see some weird gibberish that doesn't make sense and an iframe script. In this case, it's the exploit being sent to the...

    published: 12 Mar 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
  • 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
  • Stay Smart Online - Protect Your Computer - Stop Intrusions

    Description

    published: 17 Sep 2014
  • Optical Encryption: Is your data fully protected?

    Protecting company and customer data is a core concern of every organization today. Ciena’s WaveLogic Encryption solution provides wire-speed transport-layer optical encryption that is always-on, enabling a highly secure fiber network infrastructure that safeguards all of your in-flight data from illicit intrusions, all of the time. With our industry-leading coherent optics and dedicated end-user key management tool, encryption is made simple. Is your data fully protected? Learn more at: http://www.ciena.com/solutions/wavelogic-encryption/

    published: 20 Jan 2016
  • External authentication using AAA - Video By Sikandar Shaik || Dual CCIE (RS/SP) # 35012

    Protect your data from malware, intrusions, denial-of-service attacks, and advanced threats. Cisco routers work together to extend corporate security to your branch and defend your network. With integrated security you get protection against sophisticated threats, while maintaining outstanding performance and lowering costs.

    published: 24 Jan 2017
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: 3510
videos https://wn.com/Intrusion_Detection_Based_On_Kdd_Cup_Dataset
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: 3389
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
Data Mining for Network Intrusion Detection

Data Mining for Network Intrusion Detection

  • Order:
  • Duration: 7:47
  • Updated: 05 May 2015
  • views: 586
videos https://wn.com/Data_Mining_For_Network_Intrusion_Detection
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
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: 1725
videos https://wn.com/Kdd99_Machine_Learning_For_Intrusion_Detectors_From_Attacking_Data
Intrusion Detection (IDS) Best Practices

Intrusion Detection (IDS) Best Practices

  • Order:
  • Duration: 2:55
  • Updated: 24 Nov 2015
  • views: 4613
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
"We Watch You While You Sleep". TV signal intrusion 1975 (Scarfnada TV)

"We Watch You While You Sleep". TV signal intrusion 1975 (Scarfnada TV)

  • Order:
  • Duration: 0:43
  • Updated: 19 Feb 2014
  • views: 56761
videos
http://scarfolk.blogspot.com/2014/02/we-watch-you-while-you-sleep-tv-signal.html Here is a rare video from the Scarfolk archives. In 1975 there was a series of anonymous signal intrusions on the Scarfnada TV network. Many believed that the council itself was directly responsible for the illegal broadcasts, though this was never confirmed. However, In 1976 a BBC TV documentary revealed that the council had surreptitiously introduced tranquillisers to the water supply and employed council mediums to sing lullabies outside the bedroom windows of suspect citizens. Once a suspect had fallen asleep, the medium would break into their bedroom and secrete themselves in a wardrobe or beneath the bed. From these vantage points the mediums could record the suspect's dreams and nocturnal mumblings into a specially designed device called a 'Night Mary', named after the woman who invented it. The data would then be assessed by a local judge who could meter out the appropriate punishments. Many subconscious criminals were caught this way and the numbers of dream crimes plummeted. Literally overnight.
https://wn.com/We_Watch_You_While_You_Sleep_._Tv_Signal_Intrusion_1975_(Scarfnada_Tv)
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: 1203
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
Intrusion Detection and Response - The Game Between Attacker and Defender

Intrusion Detection and Response - The Game Between Attacker and Defender

  • Order:
  • Duration: 1:08:08
  • Updated: 24 Jan 2015
  • views: 768
videos
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
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: 5089
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
Predictive model for Intrusion Detection System Dataset KDD Cup 1999

Predictive model for Intrusion Detection System Dataset KDD Cup 1999

  • Order:
  • Duration: 10:50
  • Updated: 17 Nov 2015
  • views: 435
videos
https://wn.com/Predictive_Model_For_Intrusion_Detection_System_Dataset_Kdd_Cup_1999
Intrusion Detection System Using Machine Learning Models

Intrusion Detection System Using Machine Learning Models

  • Order:
  • Duration: 19:13
  • Updated: 16 Jul 2015
  • views: 2906
videos
https://wn.com/Intrusion_Detection_System_Using_Machine_Learning_Models
Attacks in a Network Intrusion Detection System on Artificial Neural Networks (ANN Backup)

Attacks in a Network Intrusion Detection System on Artificial Neural Networks (ANN Backup)

  • Order:
  • Duration: 4:01
  • Updated: 04 Oct 2014
  • views: 1271
videos
Nowadays with the dramatic growth in communication and computer networks, security has become a critical subject for computer systems. A good way to detect the algorithms, methods and applications are created and implemented to solve the problem of detecting the attacks in intrusion detection systems. Most methods detect attacks and categorize in two groups, normal or threat. This paper presents a new approach of intrusion detection system based on neural network. In this paper, we have a Multi Layer Perceptron (MLP) is used for intrusion detection system. The results show that our implemented and designed system detects the attacks and classify them in 6 groups with the approximately 90.78% accuracy with the two hidden layers of neurons in the neural network.
https://wn.com/Attacks_In_A_Network_Intrusion_Detection_System_On_Artificial_Neural_Networks_(Ann_Backup)
Uber next in series of database intrusions

Uber next in series of database intrusions

  • Order:
  • Duration: 1:01
  • Updated: 19 Mar 2015
  • views: 8
videos
Reports indicated that Uber fell victim to a data breach. Find out how this breach affected customer data.
https://wn.com/Uber_Next_In_Series_Of_Database_Intrusions
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: 13641
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
Counteracting Data-Only Malware with Code Pointer Examination

Counteracting Data-Only Malware with Code Pointer Examination

  • Order:
  • Duration: 26:16
  • Updated: 24 Dec 2015
  • views: 43
videos
https://wn.com/Counteracting_Data_Only_Malware_With_Code_Pointer_Examination
Data Science Capstone Project "Network Intrusion Detection"

Data Science Capstone Project "Network Intrusion Detection"

  • Order:
  • Duration: 29:30
  • Updated: 03 Aug 2016
  • views: 191
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
Catchr - Secretly Detect Intrusions

Catchr - Secretly Detect Intrusions

  • Order:
  • Duration: 1:07
  • Updated: 10 Feb 2014
  • views: 36047
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
Wireshark and Recognizing Exploits, HakTip 138

Wireshark and Recognizing Exploits, HakTip 138

  • Order:
  • Duration: 6:07
  • Updated: 12 Mar 2015
  • views: 27397
videos
This week on HakTip, Shannon pinpoints an exploitation using Wireshark. Working on the shoulders of last week's episode, this week we'll discuss what exploits look like in Wireshark. The example I'm sharing is from Practical Packet Analysis, a book by Chris Sanders about Wireshark. Our example packet shows what happens when a user visits a malicious site using a bad version of IE. This is called spear phishing. First, we have HTTP traffic on port 80. We notice there is a 302 moved response from the malicious site and the location is all sorts of weird. Then a bunch of data gets transferred from the new site to the user. Click Follow TCP Stream. If you scroll down, you see some weird gibberish that doesn't make sense and an iframe script. In this case, it's the exploit being sent to the user. Scroll down to packet 21 and take a look at the .gif GET request. Lastly, Follow packet 25's TCP Stream. This shows us a windows command shell, and the attacker gaining admin priveledges to view our user's files. FREAKY. But now a network admin could use their intrusion detection system to set up a new alarm whenever an attack of this nature is seen. If someone is trying to do a MITM attack on a user, it might look like our next example packet. 54 and 55 are just ARP packets being sent back and forth, but in packet 56 the attacker sends another ARP packet with a different MAC address for the router, thereby sending the user's data to the attacker then to the router. Compare 57 to 40, and you see the same IP address, but different macs for the destination. This is ARP cache Poisoning. Let me know what you think. Send me a comment below or email us at tips@hak5.org. And be sure to check out our sister show, Hak5 for more great stuff just like this. I'll be there, reminding you to trust your technolust. -~-~~-~~~-~~-~- Please watch: "Bash Bunny Primer - Hak5 2225" https://www.youtube.com/watch?v=8j6hrjSrJaM -~-~~-~~~-~~-~-
https://wn.com/Wireshark_And_Recognizing_Exploits,_Haktip_138
Building an intrusion detection system using a filter-based feature selection algorithm

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

  • Order:
  • Duration: 9:43
  • Updated: 15 Dec 2016
  • views: 1844
videos
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
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

  • Order:
  • Duration: 1:00:27
  • Updated: 09 Sep 2013
  • views: 1481
videos
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
Stay Smart Online - Protect Your Computer - Stop Intrusions

Stay Smart Online - Protect Your Computer - Stop Intrusions

  • Order:
  • Duration: 1:52
  • Updated: 17 Sep 2014
  • views: 834
videos https://wn.com/Stay_Smart_Online_Protect_Your_Computer_Stop_Intrusions
Optical Encryption: Is your data fully protected?

Optical Encryption: Is your data fully protected?

  • Order:
  • Duration: 2:02
  • Updated: 20 Jan 2016
  • views: 1229
videos
Protecting company and customer data is a core concern of every organization today. Ciena’s WaveLogic Encryption solution provides wire-speed transport-layer optical encryption that is always-on, enabling a highly secure fiber network infrastructure that safeguards all of your in-flight data from illicit intrusions, all of the time. With our industry-leading coherent optics and dedicated end-user key management tool, encryption is made simple. Is your data fully protected? Learn more at: http://www.ciena.com/solutions/wavelogic-encryption/
https://wn.com/Optical_Encryption_Is_Your_Data_Fully_Protected
External authentication using AAA - Video By Sikandar Shaik || Dual CCIE (RS/SP) # 35012

External authentication using AAA - Video By Sikandar Shaik || Dual CCIE (RS/SP) # 35012

  • Order:
  • Duration: 7:53
  • Updated: 24 Jan 2017
  • views: 1767
videos
Protect your data from malware, intrusions, denial-of-service attacks, and advanced threats. Cisco routers work together to extend corporate security to your branch and defend your network. With integrated security you get protection against sophisticated threats, while maintaining outstanding performance and lowering costs.
https://wn.com/External_Authentication_Using_Aaa_Video_By_Sikandar_Shaik_||_Dual_Ccie_(Rs_Sp)_35012