• Intrusion Detection based on KDD Cup Dataset

    Final Presentation for Big Data Analysis

    published: 05 May 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
  • Data Mining for Network Intrusion Detection

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

    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
  • Intrusion Detection Approaches

    This video is part of the Udacity course "Intro to Information Security". Watch the full course at https://www.udacity.com/course/ud459

    published: 06 Jun 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
  • chongshm Destroy All Illegal network intrusions with big data techs

    KDDCUP 99 by Chongshen Ma, Carnegie Mellon University.

    published: 05 May 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
  • 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
  • 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
  • Anomaly Detection in Telecommunications Using Complex Streaming Data | Whiteboard Walkthrough

    In this Whiteboard Walkthrough Ted Dunning, Chief Application Architect at MapR, explains in detail how to use streaming IoT sensor data from handsets and devices as well as cell tower data to detect strange anomalies. He takes us from best practices for data architecture, including the advantages of multi-master writes with MapR Streams, through analysis of the telecom data using clustering methods to discover normal and anomalous behaviors. For additional resources on anomaly detection and on streaming data: Download free pdf for the book Practical Machine Learning: A New Look at Anomaly Detection by Ted Dunning and Ellen Friedman https://www.mapr.com/practical-machine-learning-new-look-anomaly-detection Watch another of Ted’s Whiteboard Walkthrough videos “Key Requirements for Stre...

    published: 19 Oct 2016
  • 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
  • Wazuh - Automatic log data analysis for intrusion detection

    Wazuh agents read operating system and application logs, and securely forward them to a central manager for rule-based analysis and storage. The Wazuh rules help bring to your attention application or system errors, misconfigurations, attempted and/or successful malicious activities, policy violations and a variety of other security and operational issues. This video shows an example of how Wazuh is used to detect a Shellshock vulnerability exploitation attempt. Join our mailing list at: wazuh+subscribe@googlegroups.com https://wazuh.com @wazuh

    published: 28 May 2017
  • Machine Learning for Intrusion Detectors from attacking data

    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 System Using Machine Learning Models

    published: 16 Jul 2015
  • Stay Smart Online - Protect Your Computer - Stop Intrusions

    Description

    published: 17 Sep 2014
  • KDD Cupset Intrusion Detection DataSet Import to MYSQL Database - Simpleway How to use KDD Cupset

    This tutorial tells you how to import KDD Cupset in MYSQL Database INTRUSION DETECTOR LEARNING http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html www.mysqldumper.net/‎ This is the data set used for The Third International Knowledge Discovery and Data Mining Tools Competition, which was held in conjunction with KDD-99 The Fifth International Conference on Knowledge Discovery and Data Mining. The competition task was to build a network intrusion detector, a predictive model capable of distinguishing between ``bad'' connections, called intrusions or attacks, and ``good'' normal connections. This database contains a standard set of data to be audited, which includes a wide variety of intrusions simulated in a military network environment.

    published: 09 Dec 2013
  • 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
  • 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
  • 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
  • An Internal Intrusion Detection and Protection System by Using Data Mining and Forensic Techniques

    Including Packages ======================= * Base Paper * 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

    published: 20 Jul 2016
  • 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
  • Spearphishing data intrusion

    Virus prevention - http://www.afxsearch.com/

    published: 30 Sep 2013
  • 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
  • Do more with Opera Mini mobile browser

    Opera Mini – A mobile browser with built-in newsfeed and video downloader! Opera Mini is a so much more than just a browser – get ready to read news, download video and more – all the while saving data! Get it for free: http://opr.as/2kszRcG Got the new Opera Mini browser? Opera Mini is one of the world’s most popular mobile browsers. This fast mobile browser blocks ads and saves you data. The unique compression technology lets you load pages faster and download more video – for free. Browse faster! With Opera Mini you get one of the fastest mobile browsers on the market, developed to run at high speed on any internet connection. So, whether you’re on a secluded island in the Philippines or in urban London, you’ll be browsing at top speed, much thanks to the unique compression techno...

    published: 27 Jan 2017
  • The World's Most Secure Printers | HP LaserJets | HP

    New HP enterprise-class LaserJets, the world’s most secure printers, come with built-in self-healing security features including: HP Sure Start, Whitelisting and Run-time intrusion detection. Defend your network with the deepest device, data, and document security. Learn More: http://www.hp.com/go/PrintersThatProtect SUBSCRIBE: http://bit.ly/2mGfXhF SHOP NOW: http://store.hp.com/us/en/ About HP: HP Inc. creates technology that makes life better for everyone everywhere — every person, every organization, and every community around the globe. Through our portfolio of printers, PCs, mobile devices, solutions, and services, we engineer experiences that amaze. Connect with HP: Visit HP WEBSITE: http://www.hp.com Like HP on FACEBOOK: https://www.facebook.com/HP Follow HP on TWITTER: https:...

    published: 28 Sep 2015
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: 4133
videos https://wn.com/Intrusion_Detection_Based_On_Kdd_Cup_Dataset
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: 5850
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
Data Mining for Network Intrusion Detection

Data Mining for Network Intrusion Detection

  • Order:
  • Duration: 7:47
  • Updated: 05 May 2015
  • views: 685
videos https://wn.com/Data_Mining_For_Network_Intrusion_Detection
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: 3660
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 Approaches

Intrusion Detection Approaches

  • Order:
  • Duration: 0:51
  • Updated: 06 Jun 2016
  • views: 195
videos
This video is part of the Udacity course "Intro to Information Security". Watch the full course at https://www.udacity.com/course/ud459
https://wn.com/Intrusion_Detection_Approaches
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: 2463
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: 4520
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
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
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: 1423
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)
Catchr - Secretly Detect Intrusions

Catchr - Secretly Detect Intrusions

  • Order:
  • Duration: 1:07
  • Updated: 10 Feb 2014
  • views: 36521
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
Intrusion Detection (IDS) Best Practices

Intrusion Detection (IDS) Best Practices

  • Order:
  • Duration: 2:55
  • Updated: 24 Nov 2015
  • views: 4972
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
Anomaly Detection in Telecommunications Using Complex Streaming Data | Whiteboard Walkthrough

Anomaly Detection in Telecommunications Using Complex Streaming Data | Whiteboard Walkthrough

  • Order:
  • Duration: 13:50
  • Updated: 19 Oct 2016
  • views: 2895
videos
In this Whiteboard Walkthrough Ted Dunning, Chief Application Architect at MapR, explains in detail how to use streaming IoT sensor data from handsets and devices as well as cell tower data to detect strange anomalies. He takes us from best practices for data architecture, including the advantages of multi-master writes with MapR Streams, through analysis of the telecom data using clustering methods to discover normal and anomalous behaviors. For additional resources on anomaly detection and on streaming data: Download free pdf for the book Practical Machine Learning: A New Look at Anomaly Detection by Ted Dunning and Ellen Friedman https://www.mapr.com/practical-machine-learning-new-look-anomaly-detection Watch another of Ted’s Whiteboard Walkthrough videos “Key Requirements for Streaming Platforms: A Microservices Advantage” https://www.mapr.com/blog/key-requirements-streaming-platforms-micro-services-advantage-whiteboard-walkthrough-part-1 Read technical blog/tutorial “Getting Started with MapR Streams” sample programs by Tugdual Grall https://www.mapr.com/blog/getting-started-sample-programs-mapr-streams Download free pdf for the book Introduction to Apache Flink by Ellen Friedman and Ted Dunning https://www.mapr.com/introduction-to-apache-flink
https://wn.com/Anomaly_Detection_In_Telecommunications_Using_Complex_Streaming_Data_|_Whiteboard_Walkthrough
Wireshark and Recognizing Exploits, HakTip 138

Wireshark and Recognizing Exploits, HakTip 138

  • Order:
  • Duration: 6:07
  • Updated: 12 Mar 2015
  • views: 29575
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
Wazuh - Automatic log data analysis for intrusion detection

Wazuh - Automatic log data analysis for intrusion detection

  • Order:
  • Duration: 3:42
  • Updated: 28 May 2017
  • views: 1133
videos
Wazuh agents read operating system and application logs, and securely forward them to a central manager for rule-based analysis and storage. The Wazuh rules help bring to your attention application or system errors, misconfigurations, attempted and/or successful malicious activities, policy violations and a variety of other security and operational issues. This video shows an example of how Wazuh is used to detect a Shellshock vulnerability exploitation attempt. Join our mailing list at: wazuh+subscribe@googlegroups.com https://wazuh.com @wazuh
https://wn.com/Wazuh_Automatic_Log_Data_Analysis_For_Intrusion_Detection
Machine Learning for Intrusion Detectors from attacking data

Machine Learning for Intrusion Detectors from attacking data

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  • Duration: 30:46
  • Updated: 05 May 2015
  • views: 647
videos
https://wn.com/Machine_Learning_For_Intrusion_Detectors_From_Attacking_Data
KDD99 - Machine Learning for Intrusion Detectors from attacking data

KDD99 - Machine Learning for Intrusion Detectors from attacking data

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  • Duration: 45:56
  • Updated: 05 May 2015
  • views: 2217
videos https://wn.com/Kdd99_Machine_Learning_For_Intrusion_Detectors_From_Attacking_Data
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: 3719
videos
https://wn.com/Intrusion_Detection_System_Using_Machine_Learning_Models
Stay Smart Online - Protect Your Computer - Stop Intrusions

Stay Smart Online - Protect Your Computer - Stop Intrusions

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  • Duration: 1:52
  • Updated: 17 Sep 2014
  • views: 859
videos https://wn.com/Stay_Smart_Online_Protect_Your_Computer_Stop_Intrusions
KDD Cupset Intrusion Detection DataSet Import to MYSQL Database - Simpleway How to use KDD Cupset

KDD Cupset Intrusion Detection DataSet Import to MYSQL Database - Simpleway How to use KDD Cupset

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  • Duration: 13:52
  • Updated: 09 Dec 2013
  • views: 2990
videos
This tutorial tells you how to import KDD Cupset in MYSQL Database INTRUSION DETECTOR LEARNING http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html www.mysqldumper.net/‎ This is the data set used for The Third International Knowledge Discovery and Data Mining Tools Competition, which was held in conjunction with KDD-99 The Fifth International Conference on Knowledge Discovery and Data Mining. The competition task was to build a network intrusion detector, a predictive model capable of distinguishing between ``bad'' connections, called intrusions or attacks, and ``good'' normal connections. This database contains a standard set of data to be audited, which includes a wide variety of intrusions simulated in a military network environment.
https://wn.com/Kdd_Cupset_Intrusion_Detection_Dataset_Import_To_Mysql_Database_Simpleway_How_To_Use_Kdd_Cupset
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: 3850
videos
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)
Intrusion Detection System Introduction, Types of Intruders in Hindi with Example

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

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  • Duration: 9:07
  • Updated: 06 Dec 2016
  • views: 19403
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
Data Science Capstone Project "Network Intrusion Detection"

Data Science Capstone Project "Network Intrusion Detection"

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  • Duration: 29:30
  • Updated: 03 Aug 2016
  • views: 207
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
An Internal Intrusion Detection and Protection System by Using Data Mining and Forensic Techniques

An Internal Intrusion Detection and Protection System by Using Data Mining and Forensic Techniques

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  • Duration: 10:22
  • Updated: 20 Jul 2016
  • views: 390
videos
Including Packages ======================= * Base Paper * 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/An_Internal_Intrusion_Detection_And_Protection_System_By_Using_Data_Mining_And_Forensic_Techniques
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: 2017
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
Spearphishing data intrusion

Spearphishing data intrusion

  • Order:
  • Duration: 2:18
  • Updated: 30 Sep 2013
  • views: 49
videos
Virus prevention - http://www.afxsearch.com/
https://wn.com/Spearphishing_Data_Intrusion
Data Mining for Network Intrusion Detection

Data Mining for Network Intrusion Detection

  • Order:
  • Duration: 7:47
  • Updated: 05 May 2015
  • views: 498
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: 1405
videos https://wn.com/Kdd99_Machine_Learning_For_Intrusion_Detectors_From_Attacking_Data
Do more with Opera Mini mobile browser

Do more with Opera Mini mobile browser

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  • Duration: 0:31
  • Updated: 27 Jan 2017
  • views: 1266828
videos
Opera Mini – A mobile browser with built-in newsfeed and video downloader! Opera Mini is a so much more than just a browser – get ready to read news, download video and more – all the while saving data! Get it for free: http://opr.as/2kszRcG Got the new Opera Mini browser? Opera Mini is one of the world’s most popular mobile browsers. This fast mobile browser blocks ads and saves you data. The unique compression technology lets you load pages faster and download more video – for free. Browse faster! With Opera Mini you get one of the fastest mobile browsers on the market, developed to run at high speed on any internet connection. So, whether you’re on a secluded island in the Philippines or in urban London, you’ll be browsing at top speed, much thanks to the unique compression technology built into the browser. Our speed tests show that Opera Mini is much faster than other mobile browsers like Chrome and the UC browser. Opera Mini in extreme savings mode loads web pages 72% faster than Chrome, and 64% faster than the UC browser. Are you wondering how we made the browser this fast? In addition to the unique compression technology, we built an ad blocker into the browser. Without ads, web pages are so much lighter – it takes no time to load them. It is all win, win, right? You get the content you want – just faster! If you want a speedy mobile browser for Android, it’s time to give Opera Mini a try. Read more about it on http://www.opera.com/mobile/mini/android. Get the news Opera Mini brings the news that’s important to you directly to the browser. The news feed notices what kind of content you like and gives you more of it. Swipe through a range of news channels within the browser, subscribe to your favorite channels, and save stories to read later. You’ll find this mobile browser makes catching up with the world an exciting journey – no more shuffling past the the boring stuff. Download videos Opera Mini’s download manager provides our mobile users with more power and control while downloading web content, including pictures and videos. Also, people are increasingly downloading more video to their phones, as the average phone now has a higher memory. Mobile users want a browser that handles video well, putting mobile browsers that do one step ahead of the crowd. With Opera Mini’s download manager you can: - Control the number of files you download simultaneously. - Get alerts when downloading large files, over 15MB. - Select the download location. Block ads Online ads take up precious screen space, slow down the browsing and adds to the user’s data bill. We added an ad blocker to Opera Mini because it offers our mobile users a better browsing experience. Browsing with Opera Mini now means users can surf at a higher speed, skip extra data charges and stretch their internet packages even further by blocking in the browser intrusive and data-wasting ads and heavy tracking. Opera Mini now loads web pages 40% faster than with the ad blocker disabled. Cost-conscious users will also be pleased to hear that removing online ads has a positive effect on the data bill, saving users lots of data. By blocking ads, Opera Mini users can achieve up to an additional 14% in data savings on top of the default data savings compression mode, so that less is deducted from the user's mobile data plan. Get a mobile browser with ad blocker to browse faster and save data, for free: http://opr.as/2kszRcG Extend your data Opera Mini’s unique compression technology saves you large amounts of data everyday by shrinking web page content data to 10% of the original size, for any web page you request. Choosing one of two modes, users can optimize their data compression for different network conditions. Our data savings mode compresses web pages without affecting the page display, making it the perfect mode for surfing the web on 3G or Wi-Fi networks. The extreme mode compresses web pages extensively, giving users a very high-speed internet experience while using very little mobile data. This mode is ideal for when users are experiencing slow network conditions, or just want to make their data plans last longer. There’s more in Opera Mini – get it for free on Google Play. Video script: Got the new Opera Mini browser? Browse faster! Get the news Download videos Block ads Extend your data There’s more in Opera Mini Download now for free Get in on Google Play Download on the App Store
https://wn.com/Do_More_With_Opera_Mini_Mobile_Browser
The World's Most Secure Printers | HP LaserJets | HP

The World's Most Secure Printers | HP LaserJets | HP

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  • Duration: 1:11
  • Updated: 28 Sep 2015
  • views: 2958955
videos
New HP enterprise-class LaserJets, the world’s most secure printers, come with built-in self-healing security features including: HP Sure Start, Whitelisting and Run-time intrusion detection. Defend your network with the deepest device, data, and document security. Learn More: http://www.hp.com/go/PrintersThatProtect SUBSCRIBE: http://bit.ly/2mGfXhF SHOP NOW: http://store.hp.com/us/en/ About HP: HP Inc. creates technology that makes life better for everyone everywhere — every person, every organization, and every community around the globe. Through our portfolio of printers, PCs, mobile devices, solutions, and services, we engineer experiences that amaze. Connect with HP: Visit HP WEBSITE: http://www.hp.com Like HP on FACEBOOK: https://www.facebook.com/HP Follow HP on TWITTER: https://twitter.com/HP Follow HP on INSTAGRAM: https://www.instagram.com/hp Follow HP on LINKEDIN: https://www.linkedin.com/company/hp The World's Most Secure Printers | HP LaserJets | HP https://www.youtube.com/user/HP
https://wn.com/The_World's_Most_Secure_Printers_|_Hp_Laserjets_|_Hp
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