Suggested Reads

Posted on January 01, 2015 in misc • 5 min read

Read these first

Papers

You can find all of these on Google Scholar

  • Computer security threat monitoring and surveillance (1980)
  • Requirements and model for IDES - a real-time intrusion detection expert system (1985)
  • An intrusion-detection model (1987) - Denning
  • The SRI IDES statistical anomaly detector (1991) - Javitz and Valdes
  • USTAT: A real-time intrusion detection system for UNIX (1993)
  • Self-nonself discrimination in a computer (1994)
  • Next-generation intrusion detection expert system (NIDES): A summary (1995)
  • A sense of self for unix processes (1996) - Forrest
  • Role-based access control models (1996) - Sandhu
  • The base-rate fallacy and its implications for the difficulty of intrusion detection (1999) - Axelsson
  • Bro: a system for detecting network intruders in real-time (1999) - Paxson
  • Intrusion detection via static analysis (2001) - Wagner and Dean
  • How to Own the Internet in Your Spare Time (2002) - Staniford
  • Mimicry attacks on host-based intrusion detection systems (2002)
  • "Why 6?" Defining the operational limits of stide, an anomaly-based intrusion detector (2002)
  • Optical time-domain eavesdropping risks of CRT displays (2002)
  • Formalizing sensitivity in static analysis for intrusion detection (2004) - Feng
  • Polygraph: Automatically Generating Signatures for Polymorphic Worms (2005)
  • Semantics-aware malware detection (2005)
  • Automating mimicry attacks using static binary analysis (2005)
  • Modeling Botnet Propagation Using Time Zones (2006) - Dagon
  • Polymorphic Blending Attacks (2006) - Fogla
  • Misleading worm signature generators using deliberate noise injection (2006)
  • Paragraph: Thwarting signature learning by training maliciously (2006)
  • Allergy attack against automatic signature generation (2006)
  • A taxonomy of botnet structures (2007)
  • Exploring multiple execution paths for malware analysis (2007)
  • Limits of static analysis for malware detection (2007)
  • BotMiner: Clustering Analysis of Network Traffic for Protocol-and Structure-Independent Botnet Detection (2008) - Gu
  • All your iframes point to us (2008)
  • Increased DNS Forgery Resistance Through 0x20-Bit Encoding (2008)
  • Impeding Malware Analysis Using Conditional Code Obfuscation (2008)
  • BitBlaze: A new approach to computer security via binary analysis (2008)
  • Ether: malware analysis via hardware virtualization extensions (2008)
  • Active botnet probing to identify obscure command and control channels (2009)
  • Effective and Efficient Malware Detection at the End Host (2009)
  • Emulating emulation-resistant malware (2009)
  • Scalable, Behavior-Based Malware Clustering (2009)
  • Outside the Closed World: On Using Machine Learning for Network Intrusion Detection (2010)
  • Synthesizing near-optimal malware specifications from suspicious behaviors (2010)
  • Efficient Detection of Split Personalities in Malware (2010)
  • Identifying dormant functionality in malware programs (2010)
  • Bitshred: Fast, scalable malware triage (2010)
  • Behavioral Clustering of HTTP-Based Malware and Signature Generation Using Malicious Network Traces (2010)
  • Detecting environment-sensitive malware (2011)
  • Outside the Closed World: On Using Machine Learning for Network Intrusion Detection (2010)
  • Detecting Malware Domains at the Upper DNS Hierarchy (2011)
  • GQ: Practical containment for measuring modern malware systems (2011)
  • The power of procrastination: detection and mitigation of execution-stalling malicious code (2011)
  • Impeding Automated Malware Analysis with Environment-sensitive Malware (2012)
  • From Throw-Away Traffic to Bots: Detecting the Rise of DGA-Based Malware (2012)
  • Scalable fine-grained behavioral clustering of HTTP-based malware (2013)
  • A11y Attacks: Exploiting Accessibility in Operating Systems (2014)
  • Gyrus: A framework for user-intent monitoring of text-based networked applications (2014)
  • Barecloud: bare-metal analysis-based evasive malware detection (2014)
  • Guilt by association: large scale malware detection by mining file-relation graphs (2014)
  • Needles in a Haystack: Mining Information from Public Dynamic Analysis Sandboxes for Malware Intelligence (2015)
  • WebWitness: Investigating, Categorizing, and Mitigating Malware Download Paths (2015)
  • Towards Making Systems Forget with Machine Unlearning (2015)
  • Automatically Evading Classifiers: A Case Study on PDF Malware Classifiers (2016)
  • Helping johnny to analyze malware: A usability-optimized decompiler and malware analysis user study (2016)
  • Towards evaluating the robustness of neural networks (2017)
  • Feature Squeezing Mitigates and Detects Carlini/Wagner Adversarial Examples (2017)
  • Spotless Sandboxes: Evading Malware Analysis Systems using Wear-and-Tear Artifacts (2017)
  • The Battle for New York: A Case Study of Applied Digital Threat Modeling at the Enterprise Level (2018)
  • Enforcing unique code target property for control-flow integrity (2018)
  • TESSERACT: Eliminating experimental bias in malware classification across space and time (2019)
  • When Malware is Packin' Heat; Limits of Machine Learning Classifiers Based on Static Analysis Features (2020)
  • An Observational Investigation of Reverse Engineers’ Processes (2020)
  • When Malware Changed Its Mind: An Empirical Study of Variable Program Behaviors in the Real World (2021)
  • Arbitrar: User-guided api misuse detection (2021)
  • An Inside Look into the Practice of Malware Analysis (2021)
  • BODMAS: An Open Dataset for Learning based Temporal Analysis of PE Malware (2021)
  • CADE: Detecting and Explaining Concept Drift Samples for Security Applications (2021)
  • Proof-of-Learning: Definitions and Practice (2021)
  • RE-Mind: a First Look Inside the Mind of a Reverse Engineer (2022)
  • DEEPDI: Learning a Relational Graph Convolutional Network Model on Instructions for Fast and Accurate Disassembly (2022)
  • Transcending transcend: Revisiting malware classification in the presence of concept drift (2022)
  • 99% False Positives: A Qualitative Study of SOC Analysts' Perspectives on Security Alarms (2022)
  • Dos and Don'ts of Machine Learning in Computer Security (2022)
  • DnD: A Cross-Architecture Deep Neural Network Decompiler (2022)
  • Ground Truth for Binary Disassembly is Not Easy (2022)
  • Everybody’s Got ML, Tell Me What Else You Have: Practitioners' Perception of ML-Based Security Tools and Explanations (2023)
  • Humans vs. Machines in Malware Classification (2023)
  • No One Drinks From the Firehose: How Organizations Filter and Prioritize Vulnerability Information (2023)

Academic cybersecurity conferences

Tier 1: source1 & source2

Talks

Blog Posts

Machine Learning

Machine Learning for Security

Software Supply Chain

Mailing lists

RSS feeds

Books

  • Practical Malware Analysis (Sikorski and Honig)
  • Linkers & Loaders (Levine)
  • Rootkits (Butler and Hoglund)
  • Machine Learning (Mitchell)
  • Deep Learning with Python (Chollet)
  • Introduction to Modern Cryptography (Katz and Lindell)
  • Computer Networking (Kurose and Ross)
  • Introduction to the Theory of Computation (Sipser)
  • Compilers: Principles, Techniques, and Tools (Aho, Lam, Sethi, Ullman)
  • Qualitative Analysis: Constructing Grounded Theory (Charmaz)
  • The Shellcoder's Handbook: Discovering and Exploiting Security Holes (Anley, Heasman, Linder, Richarte)
  • The Hacker Playbook 3: Practical Guide to Penetration Testing (Kim)
  • Style: Lessons in Clarity and Grace (Colomb)
  • The Lean Startup (Eric Ries)
  • Pro Git (Chacon and Straub)
  • Fluent Python (Ramalho)
  • Mini Farming: Self-Sufficiency on 1/4 Acre (Brett Markham)