I am currently taking a special topics course at Texas A&M (CSCE 689 - ML for Cyber Defenses). Throughout this course each student presents a seminar covering the specific paper assigned to the lecture. This blog consists of summaries for each seminar, enjoy!
Posts
DeepSign: Deep Learning for Automatic Malware Signature Generation and Classification (Seminar 12.2)
Automatic Yara Rule Generation Using Biclustering (Seminar 12.1)
Passphrase and keystroke dynamics authentication: Usable security (Seminar 11.2)
Online Binary Models are Promising for Distinguishing Temporally Consistent Computer Usage Profiles (Seminar 11.1)
Lost at C: A User Study on the Security Implications of Large Language Model Code Assistants (Seminar 10.2)
Examining Zero-Shot Vulnerability Repair with Large Language Models (Seminar 10.1)
TrojanPuzzle: Covertly Poisoning Code-Suggestion Models (Seminar 9.2)
Pop Quiz! Can a Large Language Model Help With Reverse Engineering? (Seminar 9.1)
Adversarial Machine Learning in Image Classification: A Survey Toward the Defender’s Perspective (Seminar 8.2)
EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection (Seminar 8.1)
Mal-LSGAN: An Effective Adversarial Malware Example Generation Model (Seminar 7.2)
Functionality-Preserving Black-Box Optimization of Adversarial Windows Malware (Seminar 7.1)
No Need to Teach New Tricks to Old Malware: Winning an Evasion Challenge with XOR-based Adversarial Samples (Seminar 6.2)
Shallow Security: on the Creation of Adversarial Variants to Evade Machine Learning-Based Malware Detectors (Seminar 6.1)
Transcending TRANSCEND: Revisiting Malware Classification in the Presence of Concept Drift (Seminar 5.2)
Transcend: Detecting Concept Drift in Malware Classification Models (Seminar 5.1)
DroidEvolver: Self-Evolving Android Malware Detection System (Seminar 4.2)
Fast & Furious: On the modelling of malware detection as an evolving data stream (Seminar 4.1)
Dos and Don’ts of Machine Learning in Computer Security (Seminar 3.2)
Dos and Don’ts of Machine Learning in Computer Security (Seminar 3.1)
Machine Learning (In) Security: A Stream of Problems Part 2 (Seminar 2.2)
Machine Learning (In) Security: A Stream of Problems Part 1 (Seminar 2.1)
Malware Detection on Highly Imbalanced Data through Sequence Modeling (Seminar 1.2)
Machine Learning for Malware Detection (Seminar 1.1)
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