• Data - Intrusion - DNB - Minimal

    --------------------- Artist: Data Title: Intrusion Release: Making Simple Things Complex Label: Date: 2012 Style: Drum & Bass - Techstep - Minimal Location: London and South East, United Kingdom CAT#: ------------------- Artist Links: Soundcloud: http://soundcloud.com/data Facebook: https://www.facebook.com/pages/Data/322382158258 Darknesshides: http://darknesshides.com/portfolio/data -- This video was published on YouTube with the authorization of the artist/label. If you want to request a delete of this video, please contact http://www.darknesshides.com

    published: 25 Feb 2012
  • Intrusion Detection based on KDD Cup Dataset

    Final Presentation for Big Data Analysis

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

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

    published: 05 May 2015
  • Intrusion Detection in Action: How do we monitor and safeguard your data in Office 365?

    The rigor and discipline required to test ourselves continuously to keep your data safe within the service, is by nature an operation of tremendous scale especially when you consider the terabytes of data flowing daily through the service. And it’s the blue team’s job, to literally find that potential needle in the haystack of activity that may signify anomalous behavior and to then take action. Watch this insider's view on how we go about intrusion detection.

    published: 28 Oct 2014
  • 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
  • DATA STREAM BASED INTRUSION DETECTION SYSTEM

    SPIRO SOLUTIONS PRIVATE LIMITED For ECE,EEE,E&I, E&C & Mechanical,Civil, Bio-Medical #1, C.V.R Complex, Singaravelu St, T.Nagar, Chennai - 17, (Behind BIG BAZAAR)Tamilnadu,India Mobile : +91-9962 067 067, +91-9176 499 499 Landline : 044-4264 1213 Email: info@spiroprojects.com For IT, CSE, MSC, MCA, BSC(CS)B.COM(cs) #78, 3rd Floor, Usman Road, T.Nagar, Chennai-17. (Upstair Hotel Saravana Bhavan) Tamilnadu,India Mobile: +91-9791 044 044, +91-9176 644 044 E-Mail: info1@spiroprojects.com

    published: 07 Nov 2015
  • Machine Learning for Intrusion Detectors from attacking data

    published: 05 May 2015
  • Spearphishing data intrusion

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

    published: 30 Sep 2013
  • Hacking Academy - Monitoring Transmitted Data - Lecture 8: Intrusion detection

    IT Security Academy Training Course: Monitoring Transmitted Data with Wireshark Lecture 8: Intrusion detection Find more interesting knowledge and become CISS - Certified IT Security Specialist Visit http://secacademy.com for more details.

    published: 07 Jul 2015
  • KDD99 - Machine Learning for Intrusion Detectors from attacking data

    Machine Learning for Intrusion Detectors from attacking data

    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
  • Intrusion Detection (IDS) Best Practices

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

    published: 24 Nov 2015
  • Intrusion Detection System Tutorial: Setup Security Onion

    In this video, I'll show you how to setup Security Onion, an open-source intrusion detection system packaged into a Linux distro. SecOnion is perfect for getting an intrusion detection system up and running quickly, and has some cool additional features like HIDS, SIEM, root kit detection, and file integrity monitoring. For this to work, you will need a switch capable of SPANing/mirroring network traffic to a specific port. I will release a video/information about this process. For a small home network, I'd recommend the following: https://www.amazon.com/NETGEAR-ProSAFE-Gigabit-Managed-GS108E-300NAS/dp/B00M1C0186/ref=sr_1_sc_1?ie=UTF8&qid=1470783563&sr=8-1-spell&keywords=netgear+prosafe+plsu+8+port I'm also going to upload a video about utilizing SecOnion and Splunk to ingest and correl...

    published: 09 Aug 2016
  • 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
  • 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
  • Intrusion Detection Learning on KDD CUP 99 dataset using RHadoop

    BIG DATA

    published: 05 May 2015
  • Using Genetic Algorithms for Network Intrusion Detection and Integration into nProbe

    SNORT is popular Network Intrusion Detection System (NIDS) tool that currently uses a custom rule based system to identify attacks. This presentation emphasizes on writing the algorithm to write generate the rules through GA and the integration of them into nProbe, a similar network monitoring tool written by Luca Deri with a plug-in architecture. Genetic Algorithms are dependent upon identifying attributes to describe a problem and evolving a desired population. In this case, the problem is an attack through the network and identifying the attack through connection property attributes. Genetic Algorithms depends upon training data. DARPA datasets provide training data, in categorized format (attack vs. normal) along with a corresponding raw network recorded format called tcpdump. nProbe ...

    published: 13 Sep 2010
  • 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
  • Paper Data Mining for Network Intrusion Detection

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

    published: 17 Nov 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
  • Going Dark or Getting Personal? The Battle Between Data, Privacy & Intrusion

    Guests: Bruce Schneier–internationally renowned cyber-security expert, a Fellow at the Berkman Center for Internet & Society & Kennedy School at Harvard University; & Attorney David O’BrienSenior Researcher at Harvard University’s Berkman Center for Internet & Society. Mr. Schneier has author of 12 books–including the New York Times best-sellerData and Goliath: The Hidden Battles to Collect Your Data and Control Your World” and is Board Member of the Electronic Frontier Foundation, and an Advisory Board member of the Electronic Privacy Information Center. Attorney O'Brien has authored numerous articles, white-papers, and reports on cyber-security and privacy for the Berkman Klein Center at Harvard University, and serves on the advisory board for Harvard’s Open Data Assistance Progra...

    published: 25 Jun 2016
developed with YouTube
Data - Intrusion - DNB - Minimal

Data - Intrusion - DNB - Minimal

  • Order:
  • Duration: 3:27
  • Updated: 25 Feb 2012
  • views: 2923
videos
--------------------- Artist: Data Title: Intrusion Release: Making Simple Things Complex Label: Date: 2012 Style: Drum & Bass - Techstep - Minimal Location: London and South East, United Kingdom CAT#: ------------------- Artist Links: Soundcloud: http://soundcloud.com/data Facebook: https://www.facebook.com/pages/Data/322382158258 Darknesshides: http://darknesshides.com/portfolio/data -- This video was published on YouTube with the authorization of the artist/label. If you want to request a delete of this video, please contact http://www.darknesshides.com
https://wn.com/Data_Intrusion_Dnb_Minimal
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
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
Intrusion Detection in Action: How do we monitor and safeguard your data in Office 365?

Intrusion Detection in Action: How do we monitor and safeguard your data in Office 365?

  • Order:
  • Duration: 4:42
  • Updated: 28 Oct 2014
  • views: 13995
videos
The rigor and discipline required to test ourselves continuously to keep your data safe within the service, is by nature an operation of tremendous scale especially when you consider the terabytes of data flowing daily through the service. And it’s the blue team’s job, to literally find that potential needle in the haystack of activity that may signify anomalous behavior and to then take action. Watch this insider's view on how we go about intrusion detection.
https://wn.com/Intrusion_Detection_In_Action_How_Do_We_Monitor_And_Safeguard_Your_Data_In_Office_365
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
DATA STREAM BASED INTRUSION DETECTION SYSTEM

DATA STREAM BASED INTRUSION DETECTION SYSTEM

  • Order:
  • Duration: 2:49
  • Updated: 07 Nov 2015
  • views: 23
videos
SPIRO SOLUTIONS PRIVATE LIMITED For ECE,EEE,E&I, E&C & Mechanical,Civil, Bio-Medical #1, C.V.R Complex, Singaravelu St, T.Nagar, Chennai - 17, (Behind BIG BAZAAR)Tamilnadu,India Mobile : +91-9962 067 067, +91-9176 499 499 Landline : 044-4264 1213 Email: info@spiroprojects.com For IT, CSE, MSC, MCA, BSC(CS)B.COM(cs) #78, 3rd Floor, Usman Road, T.Nagar, Chennai-17. (Upstair Hotel Saravana Bhavan) Tamilnadu,India Mobile: +91-9791 044 044, +91-9176 644 044 E-Mail: info1@spiroprojects.com
https://wn.com/Data_Stream_Based_Intrusion_Detection_System
Machine Learning for Intrusion Detectors from attacking data

Machine Learning for Intrusion Detectors from attacking data

  • Order:
  • Duration: 30:46
  • Updated: 05 May 2015
  • views: 647
videos
https://wn.com/Machine_Learning_For_Intrusion_Detectors_From_Attacking_Data
Spearphishing data intrusion

Spearphishing data intrusion

  • Order:
  • Duration: 2:18
  • Updated: 30 Sep 2013
  • views: 50
videos
Virus prevention - http://www.afxsearch.com/
https://wn.com/Spearphishing_Data_Intrusion
Hacking Academy - Monitoring Transmitted Data - Lecture 8:  Intrusion detection

Hacking Academy - Monitoring Transmitted Data - Lecture 8: Intrusion detection

  • Order:
  • Duration: 7:49
  • Updated: 07 Jul 2015
  • views: 240
videos
IT Security Academy Training Course: Monitoring Transmitted Data with Wireshark Lecture 8: Intrusion detection Find more interesting knowledge and become CISS - Certified IT Security Specialist Visit http://secacademy.com for more details.
https://wn.com/Hacking_Academy_Monitoring_Transmitted_Data_Lecture_8_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: 2217
videos https://wn.com/Kdd99_Machine_Learning_For_Intrusion_Detectors_From_Attacking_Data
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
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
Intrusion Detection System Tutorial: Setup Security Onion

Intrusion Detection System Tutorial: Setup Security Onion

  • Order:
  • Duration: 9:53
  • Updated: 09 Aug 2016
  • views: 17433
videos
In this video, I'll show you how to setup Security Onion, an open-source intrusion detection system packaged into a Linux distro. SecOnion is perfect for getting an intrusion detection system up and running quickly, and has some cool additional features like HIDS, SIEM, root kit detection, and file integrity monitoring. For this to work, you will need a switch capable of SPANing/mirroring network traffic to a specific port. I will release a video/information about this process. For a small home network, I'd recommend the following: https://www.amazon.com/NETGEAR-ProSAFE-Gigabit-Managed-GS108E-300NAS/dp/B00M1C0186/ref=sr_1_sc_1?ie=UTF8&qid=1470783563&sr=8-1-spell&keywords=netgear+prosafe+plsu+8+port I'm also going to upload a video about utilizing SecOnion and Splunk to ingest and correlate the data/alerts your Intrusion detection system will generate. SecOnion comes with ELSA, which you could use (along with Kibana) to display, visualize and create alerts. Finally, i'll upload a video detailing the install and integration of the Collective Intelligence framework with your IDS/SIEM. Expect these videos within the next couple weeks. Links for this video: VirtualBox: https://www.virtualbox.org/wiki/Downloads Security Onion: https://github.com/Security-Onion-Solutions/security-onion/blob/master/Verify_ISO.md
https://wn.com/Intrusion_Detection_System_Tutorial_Setup_Security_Onion
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
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

  • Order:
  • 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
Intrusion Detection Learning on KDD CUP 99 dataset using RHadoop

Intrusion Detection Learning on KDD CUP 99 dataset using RHadoop

  • Order:
  • Duration: 9:34
  • Updated: 05 May 2015
  • views: 1596
videos https://wn.com/Intrusion_Detection_Learning_On_Kdd_Cup_99_Dataset_Using_Rhadoop
Using Genetic Algorithms for Network Intrusion Detection and Integration into nProbe

Using Genetic Algorithms for Network Intrusion Detection and Integration into nProbe

  • Order:
  • Duration: 5:38
  • Updated: 13 Sep 2010
  • views: 5280
videos
SNORT is popular Network Intrusion Detection System (NIDS) tool that currently uses a custom rule based system to identify attacks. This presentation emphasizes on writing the algorithm to write generate the rules through GA and the integration of them into nProbe, a similar network monitoring tool written by Luca Deri with a plug-in architecture. Genetic Algorithms are dependent upon identifying attributes to describe a problem and evolving a desired population. In this case, the problem is an attack through the network and identifying the attack through connection property attributes. Genetic Algorithms depends upon training data. DARPA datasets provide training data, in categorized format (attack vs. normal) along with a corresponding raw network recorded format called tcpdump. nProbe has a plug-in architecture allowing for customization. This presentation explains original code in C to evolve rules. It uses the same chromosome attributes used by Gong. The development verifies and contrasts against the research performed by Gong. It also presents the code for integration into nProbe.
https://wn.com/Using_Genetic_Algorithms_For_Network_Intrusion_Detection_And_Integration_Into_Nprobe
Intrusion Detection System Using Machine Learning Models

Intrusion Detection System Using Machine Learning Models

  • Order:
  • Duration: 19:13
  • Updated: 16 Jul 2015
  • views: 3719
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: 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)
Paper Data Mining for Network Intrusion Detection

Paper Data Mining for Network Intrusion Detection

  • Order:
  • Duration: 8:08
  • Updated: 19 May 2014
  • views: 104
videos
https://wn.com/Paper_Data_Mining_For_Network_Intrusion_Detection
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: 475
videos
https://wn.com/Predictive_Model_For_Intrusion_Detection_System_Dataset_Kdd_Cup_1999
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: 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
Going Dark or Getting Personal? The Battle Between Data, Privacy & Intrusion

Going Dark or Getting Personal? The Battle Between Data, Privacy & Intrusion

  • Order:
  • Duration: 31:30
  • Updated: 25 Jun 2016
  • views: 655
videos
Guests: Bruce Schneier–internationally renowned cyber-security expert, a Fellow at the Berkman Center for Internet & Society & Kennedy School at Harvard University; & Attorney David O’BrienSenior Researcher at Harvard University’s Berkman Center for Internet & Society. Mr. Schneier has author of 12 books–including the New York Times best-sellerData and Goliath: The Hidden Battles to Collect Your Data and Control Your World” and is Board Member of the Electronic Frontier Foundation, and an Advisory Board member of the Electronic Privacy Information Center. Attorney O'Brien has authored numerous articles, white-papers, and reports on cyber-security and privacy for the Berkman Klein Center at Harvard University, and serves on the advisory board for Harvard’s Open Data Assistance Program. Discussion on the new report issued by the Berkman Center for Internet & Society at Harvard University on the issue of “Going Dark,” and the role of law enforcement and privacy rights under scrutiny, revelations of government spying, and analysis of the Apple iPhone Encryption litigation and its progeny unfolding in the Federal Courts. This is one program for those interested in law enforcement, cryptography and personal privacy rights won’t want to miss.
https://wn.com/Going_Dark_Or_Getting_Personal_The_Battle_Between_Data,_Privacy_Intrusion
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