36 citations · 93 across the 25 of their papers we have counts for
31 papers
NetGuise: Adversarial Traffic Generation for Early-Stage Classifier Evasion
Binghui Wu, Dinil Mon Divakaran, Mohan Gurusamy
Early-stage encrypted traffic classifiers make decisions from only the first few packets of a connection, enabling low-latency classification but raising robustness concerns under…
DarKnight: Retrieval-Guided Agentic Framework for Breaking Dark Web CAPTCHA Gates
Binghui Wu, Yeo Zi Yi, Darren Tan +3
Cyber Threat Intelligence (CTI) investigators who monitor dark web marketplaces for leaked credentials and breach listings must pass the diverse CAPTCHAs that operators deploy to b…
The Fragility of Trigger-Tag Mechanisms for Misuse Detection in Open-Weight LLMs
Toluwani Aremu, Manit Baser, Mohan Gurusamy +2
Open-weight language models can be downloaded, modified, and deployed beyond their developers' control, limiting the effectiveness of centrally enforced safeguards. Recent work has…
The Tokens Remember: When Tokenization Bypasses Knowledge Editing and Unlearning
Manit Baser, Aditya Nawal, Dinil Mon Divakaran +1
Open-weight LLMs give downstream users control over the inference stack, but this flexibility can undermine post-release guarantees that sensitive knowledge has been modified or re…
Drishti: AI-Led Human-Directed Vulnerability Auditing for 5G Cores
Sriram Ramachandran, Levente Csikor, Dinil Mon Divakaran
Candidate generation for open-source vulnerabilities is no longer scarce. AI-assisted code review now produces defect candidates cheaply, and industry programs pair them with exper…
CHISEL-ing Back Source Code with AI-enabled Iterative Recovery
Varun Kohli, N Raghava, Biplab Sikdar +1
Decompilation aims to recover high-level, compilable, and semantically equivalent code from binaries. Traditional decompilers produce pseudo-C that is difficult to read and does no…
MATRA: Modeling the Attack Surface of Agentic AI Systems -- OpenClaw Case Study
Tim Van hamme, Thomas Vissers, Javier Carnerero-Cano +4
LLMs are increasingly deployed as autonomous agents with access to tools, databases, and external services, yet practitioners (across different sectors) lack systematic methods to…
CLaRE-ty Amid Chaos: Quantifying Representational Entanglement to Predict Ripple Effects in LLM Editing
Manit Baser, Alperen Yildiz, Dinil Mon Divakaran +1
The static knowledge representations of large language models (LLMs) inevitably become outdated or incorrect over time. While model-editing techniques offer a promising solution by…
RECTor: Robust and Efficient Correlation Attack on Tor
Binghui Wu, Dinil Mon Divakaran, Levente Csikor +1
Tor is a widely used anonymity network that conceals user identities by routing traffic through encrypted relays, yet it remains vulnerable to traffic correlation attacks that dean…
Step-by-Step Reasoning Attack: Revealing 'Erased' Knowledge in Large Language Models
Yash Sinha, Manit Baser, Murari Mandal +2
Knowledge erasure in large language models (LLMs) is important for ensuring compliance with data and AI regulations, safeguarding user privacy, mitigating bias, and misinformation.…
ThinkEval: Practical Evaluation of Knowledge Leakage in LLM Editing using Thought-based Knowledge Graphs
Manit Baser, Dinil Mon Divakaran, Mohan Gurusamy
Robust model-editing techniques are essential for deploying large language models (LLMs) in practical applications, as they enable cost-effective ways to deal with challenges such…
Benchmarking LLMs and LLM-based Agents in Practical Vulnerability Detection for Code Repositories
Alperen Yildiz, Sin G. Teo, Yiling Lou +3
Large Language Models (LLMs) have shown promise in software vulnerability detection, particularly on function-level benchmarks like Devign and BigVul. However, real-world detection…
AI-based Traffic Modeling for Network Security and Privacy: Challenges Ahead
Dinil Mon Divakaran
Network traffic analysis using AI (machine learning and deep learning) models made significant progress over the past decades. Traffic analysis addresses various challenging proble…
UniNet: A Unified Multi-granular Traffic Modeling Framework for Network Security
Binghui Wu, Dinil Mon Divakaran, Mohan Gurusamy
As modern networks grow increasingly complex--driven by diverse devices, encrypted protocols, and evolving threats--network traffic analysis has become critically important. Existi…
EagleEye: Attention to Unveil Malicious Event Sequences from Provenance Graphs
Philipp Gysel, Candid Wüest, Kenneth Nwafor +3
Securing endpoints is challenging due to the evolving nature of threats and attacks. With endpoint logging systems becoming mature, provenance-graph representations enable the crea…
Multimodal Large Language Models for Phishing Webpage Detection and Identification
Jehyun Lee, Peiyuan Lim, Bryan Hooi +1
To address the challenging problem of detecting phishing webpages, researchers have developed numerous solutions, in particular those based on machine learning (ML) algorithms. Amo…
From ML to LLM: Evaluating the Robustness of Phishing Webpage Detection Models against Adversarial Attacks
Aditya Kulkarni, Vivek Balachandran, Dinil Mon Divakaran +1
Phishing attacks attempt to deceive users into stealing sensitive information, posing a significant cybersecurity threat. Advances in machine learning (ML) and deep learning (DL) h…
LLMs for Cyber Security: New Opportunities
Dinil Mon Divakaran, Sai Teja Peddinti
Large language models (LLMs) are a class of powerful and versatile models that are beneficial to many industries. With the emergence of LLMs, we take a fresh look at cyber security…
Mitigating Bias in Machine Learning Models for Phishing Webpage Detection
Aditya Kulkarni, Vivek Balachandran, Dinil Mon Divakaran +1
The widespread accessibility of the Internet has led to a surge in online fraudulent activities, underscoring the necessity of shielding users' sensitive information from cybercrim…
The Evolution of DNS Security and Privacy
Levente Csikor, Dinil Mon Divakaran
DNS, one of the fundamental protocols of the TCP/IP stack, has evolved over the years to protect against threats and attacks. This study examines the risks associated with DNS and…
ZEST: Attention-based Zero-Shot Learning for Unseen IoT Device Classification
Binghui Wu, Philipp Gysel, Dinil Mon Divakaran +1
Recent research works have proposed machine learning models for classifying IoT devices connected to a network. However, there is still a practical challenge of not having all devi…
Attacking logo-based phishing website detectors with adversarial perturbations
Jehyun Lee, Zhe Xin, Melanie Ng Pei See +3
Recent times have witnessed the rise of anti-phishing schemes powered by deep learning (DL). In particular, logo-based phishing detectors rely on DL models from Computer Vision to…
Phishing Detection Leveraging Machine Learning and Deep Learning: A Review
Dinil Mon Divakaran, Adam Oest
Phishing attacks trick victims into disclosing sensitive information. To counter rapidly evolving attacks, we must explore machine learning and deep learning models leveraging larg…
SIERRA: Ranking Anomalous Activities in Enterprise Networks
Jehyun Lee, Farren Tang, Phyo May Thet +3
An enterprise today deploys multiple security middleboxes such as firewalls, IDS, IPS, etc. in its network to collect different kinds of events related to threats and attacks. Thes…