Interesting papers, thesis etc. - PSJoshi/Notes GitHub Wiki

Intrusion detection - use and misuse - https://inst.eecs.berkeley.edu/~cs161/fa17/lectures/lec20_monitoring_abusive.pdf
Analysis and evaluation of NIDS Snort and BRO - http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.462.969&rep=rep1&type=pdf
CS 161 : Computer Security Fall 2017 course - https://inst.eecs.berkeley.edu/~cs161/fa17/
Network anomaly detection based on long-term statistical model - https://watermark.silverchair.com/jzw051.pdf
Cyber threat intelligence for malware and network traffic analysis - https://spectrum.library.concordia.ca/981284/1/Boukhtouta_PhD_S2016.pdf
Phishing email analysis - http://www.ijcset.excelingtech.co.uk/vol2issue1/05-vol2issue1.pdf
Detecting suspicious objects using WiFi signals - http://www.winlab.rutgers.edu/~yychen/papers/Towards%20In%20baggage%20Suspicious%20Object%20Detection%20Using%20Commodity%20WiFi.pdf
ML based malware detection Machine learning has long been used to aid the automatic detection of new malware variants. Given the wide use of obfuscation in malware, this isn’t trivial. Researchers from Trend Micro and the Federation University Australia have published a paper in which they propose a machine learning model that uses adversarial autoencoder and semantic hashing to detect new malware. More details - https://blog.trendmicro.com/trendlabs-security-intelligence/a-machine-learning-model-to-detect-malware-variants/
DNS abuse A good article by paloalto networks about the possible DNS mis-uses. More details - https://unit42.paloaltonetworks.com/dns-tunneling-how-dns-can-be-abused-by-malicious-actors/
USB devices tracking USB Detective incorporates many features to identify and correlate USB device artifacts. More details - https://usbdetective.com/community-download/