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Course Detail

Course Name Data Analytics for Security
Course Code 18SN716
Program M. Tech. in Cyber Security Systems & Networks
Credits Amritapuri
Year Taught 2018


Course Syllabus

Introduction: Introduction to Information Security, Introduction to Data Mining for Information Security Network Intrusion Detection: Signature-based solutions (Snort, etc), Data-mining-based solutions (supervised and unsupervised); Deep Packet Inspection: Alert aggregation for web security, One-class Multi-classifier systems for packet payload modeling and network intrusion detection , Host Intrusion Detection: Analysis of shell command sequences, system call sequences, and audit trails, Introduction to Insider threats, Masquerader/Impersonator/Insider threat detection strategies, Web Security: Anomaly detection of web-based attacks using web server logs, Anomaly detection in web proxy logs Email: Spam detection, Phishing email detection, phishing website detection Social network security: Detecting compromised accounts, detecting social network spam, Authentication: Anomaly detection of Single Sign On (Kerberos, Active Directory), Detecting Pass-the-Hash and Pass-the-Ticket attacks, Behavioural Biometrics: Active authentication using behavioural and cognitive biometrics, Mouse dynamics analysis for active authentication, touch and swipe pattern analysis for mobile active authentication, Automated correlation: Attack trees, Building attack scenarios from individual alerts, Issues: Privacy issues, Adversarial machine learning: Overview of Multi-classifier systems (MCS), advantages of MCS in security analytics, security of machine learning, Other potential topics: Fraud detection, IoT/Infrastructure security, Mobile/Wireless security, Machine Learning for Security: Challenges in applying machine learning (ML) to security, guidelines for applying ML to security, Current and future trends in security.

Text Books / References

  1. Daniel Barbara and SushilJajodia, “Applications of Data Mining in Computer Security”, Vol. 6. Springer Science & Business Media, 2002
  2. Marcus A. Maloof, “Machine Learning and Data Mining for Computer Security”, Springer Science & Business Media, 2006
  3. V RaoVemuri, “Enhancing Computer Security with Smart Technology”, Auerbach Publications, 2005
  4. S. Stolfo, S. Bellovin, S. Hershkop, A. Keromytis, S. Sinclair, S. Smith, “Insider Attack and Cyber Security: Beyond the Hacker”, Vol. 39. Springer Science & Business Media, 2008
  5. Dhruba K. Bhattacharyya, Jugal K. Kalita, “Network Anomaly Detection: A Machine Learning Perspective”, Crc Press, 2013
  6. AnoopSinghal, “Data Warehousing and Data Mining Techniques for Cyber Security”, Vol. 31. Springer Science & Business Media, 2007
  7. Markus Jakobsson and ZulfikarRamzan, “Crimeware, Understanding New Attacks and Defenses”, Addison-Wesley Professional, 2008.


“Data Analytics for Security” is an elective course offered in M. Tech. in Cyber Security Systems & Networks program at School of Engineering, Amrita Vishwa Vidyapeetham, Amritapuri.

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