54 citations · 181 across the 35 of their papers we have counts for
16 papers · 1 filter
Verifying Intent and Harm: A Unified Defense Against LLM-Generated Threats
Poojitha Thota, Yun Lei, Santhosh Thangaraj +2
Large language models (LLMs) are increasingly deployed in interactive applications, yet they remain vulnerable to adversarial interactions that induce harmful, deceptive, or policy…
Binge, Bot, Repeat: Unpacking the Ecosystem of Video Piracy on Telegram
Sadikshya Gyawali, Jaishnoor Kaur, Taylor Graham +4
Telegram has emerged as a major platform for large-scale video piracy, where copyrighted content is rapidly distributed among users. Despite its prominence, the structural and oper…
Context-Aware Spear Phishing: Generative AI-Enabled Attacks Against Individuals via Public Social Media Data
Elham Pourabbas Vafa, Sayak Saha Roy, Shirin Nilizadeh
We demonstrate how publicly available social-media data and generative AI (GenAI) can be misused to automate and scale highly personalized, context-aware spear-phishing campaigns.…
Taming Data Challenges in ML-based Security Tasks Using Generative AI
Shravya Kanchi, Neal Mangaokar, Aravind Cheruvu +4
Machine learning-based supervised classifiers are widely used for security tasks, and their improvement has been largely focused on algorithmic advancements. We argue that data cha…
"Explain, Don't Just Warn!" -- A Real-Time Framework for Generating Phishing Warnings with Contextual Cues
Sayak Saha Roy, Cesar Torres, Shirin Nilizadeh
Anti-phishing tools typically display generic warnings that offer users limited explanation on why a website is considered malicious, which can prevent end-users from developing th…
DarkGram: A Large-Scale Analysis of Cybercriminal Activity Channels on Telegram
Sayak Saha Roy, Elham Pourabbas Vafa, Kobra Khanmohammadi +1
We present the first large-scale analysis of 339 cybercriminal activity channels (CACs). Followed by over 23.8 million users, these channels share a wide array of malicious and une…
PhishLang: A Real-Time, Fully Client-Side Phishing Detection Framework Using MobileBERT
Sayak Saha Roy, Shirin Nilizadeh
In this paper, we introduce PhishLang, the first fully client-side anti-phishing framework built on a lightweight ensemble framework that utilizes advanced language models to analy…
From Chatbots to PhishBots? -- Preventing Phishing scams created using ChatGPT, Google Bard and Claude
Sayak Saha Roy, Poojitha Thota, Krishna Vamsi Naragam +1
The advanced capabilities of Large Language Models (LLMs) have made them invaluable across various applications, from conversational agents and content creation to data analysis, r…
Generating Phishing Attacks using ChatGPT
Sayak Saha Roy, Krishna Vamsi Naragam, Shirin Nilizadeh
The ability of ChatGPT to generate human-like responses and understand context has made it a popular tool for conversational agents, content creation, data analysis, and research a…
Unveiling the Risks of NFT Promotion Scams
Sayak Saha Roy, Dipanjan Das, Priyanka Bose +3
The rapid growth in popularity and hype surrounding digital assets such as art, video, and music in the form of non-fungible tokens (NFTs) has made them a lucrative investment oppo…
A Large-Scale Analysis of Phishing Websites Hosted on Free Web Hosting Domains
Sayak Saha Roy, Unique Karanjit, Shirin Nilizadeh
Free Website Building services (FWBs) provide individuals with a cost-effective and convenient way to create a website without requiring advanced technical knowledge or coding skil…
Evaluating the effectiveness of Phishing Reports on Twitter
Sayak Saha Roy, Unique Karanjit, Shirin Nilizadeh
Phishing attacks are an increasingly potent web-based threat, with nearly 1.5 million websites created on a monthly basis. In this work, we present the first study towards identify…
DifFuzz: Differential Fuzzing for Side-Channel Analysis
Shirin Nilizadeh, Yannic Noller, Corina S. Pasareanu
Side-channel attacks allow an adversary to uncover secret program data by observing the behavior of a program with respect to a resource, such as execution time, consumed memory or…
Detecting Deceptive Reviews using Generative Adversarial Networks
Hojjat Aghakhani, Aravind Machiry, Shirin Nilizadeh +2
In the past few years, consumer review sites have become the main target of deceptive opinion spam, where fictitious opinions or reviews are deliberately written to sound authentic…
POISED: Spotting Twitter Spam Off the Beaten Paths
Shirin Nilizadeh, Francois Labreche, Alireza Sedighian +5
Cybercriminals have found in online social networks a propitious medium to spread spam and malicious content. Existing techniques for detecting spam include predicting the trustwor…
DECENT: A Decentralized Architecture for Enforcing Privacy in Online Social Networks
Sonia Jahid, Shirin Nilizadeh, Prateek Mittal +2
A multitude of privacy breaches, both accidental and malicious, have prompted users to distrust centralized providers of online social networks (OSNs) and investigate decentralized…