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