396 citations · 1.6k across the 95 of their papers we have counts for
53 papers · 1 filter
DEFEAT: Stitching Fragmented File I/O Contexts for Early Ransomware Detection
Muhammad Ejaz Ahmed, Hyoungshick Kim, Mohsen Ali Alawami +4
Ransomware increasingly fragments its file operations across temporary and intermediate files, scattering the semantic context that links individual I/O events to an overarching en…
Original Sin of npm: A Study on Vulnerability Propagation in JavaScript Dependency Networks
Michael Robinson, Sajal Halder, Muhammad Ejaz Ahmed +3
Understanding vulnerability propagation is essential for assessing how vulnerabilities spread across components of a software package. This supports more accurate impact analysis a…
DyMA-Fuzz: Dynamic Direct Memory Access Abstraction for Re-hosted Monolithic Firmware Fuzzing
Guy Farrelly, Michael Chesser, Seyit Camtepe +1
The rise of smart devices in critical domains--including automotive, medical, industrial--demands robust firmware testing. Fuzzing firmware in re-hosted environments is a promising…
TempoNet: Learning Realistic Communication and Timing Patterns for Network Traffic Simulation
Kristen Moore, Diksha Goel, Cody James Christopher +5
Realistic network traffic simulation is critical for evaluating intrusion detection systems, stress-testing network protocols, and constructing high-fidelity environments for cyber…
AdaDoS: Adaptive DoS Attack via Deep Adversarial Reinforcement Learning in SDN
Wei Shao, Yuhao Wang, Rongguang He +2
Existing defence mechanisms have demonstrated significant effectiveness in mitigating rule-based Denial-of-Service (DoS) attacks, leveraging predefined signatures and static heuris…
TELSAFE: Security Gap Quantitative Risk Assessment Framework
Sarah Ali Siddiqui, Chandra Thapa, Derui Wang +5
Gaps between established security standards and their practical implementation have the potential to introduce vulnerabilities, possibly exposing them to security risks. To effecti…
FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk
Sajal Halder, Muhammad Ejaz Ahmed, Seyit Camtepe
Software supply chain vulnerabilities arise when attackers exploit weaknesses by injecting vulnerable code into widely used packages or libraries within software repositories. Whil…
Data Encryption Battlefield: A Deep Dive into the Dynamic Confrontations in Ransomware Attacks
Arash Mahboubi, Hamed Aboutorab, Seyit Camtepe +5
In the rapidly evolving landscape of cybersecurity threats, ransomware represents a significant challenge. Attackers increasingly employ sophisticated encryption methods, such as e…
CyberAlly: Leveraging LLMs and Knowledge Graphs to Empower Cyber Defenders
Minjune Kim, Jeff Wang, Kristen Moore +7
The increasing frequency and sophistication of cyberattacks demand innovative approaches to strengthen defense capabilities. Training on live infrastructure poses significant risks…
Setup Once, Secure Always: A Single-Setup Secure Federated Learning Aggregation Protocol with Forward and Backward Secrecy for Dynamic Users
Nazatul Haque Sultan, Yan Bo, Yansong Gao +10
Federated Learning (FL) enables multiple users to collaboratively train a machine learning model without sharing raw data, making it suitable for privacy-sensitive applications. Ho…
Provisioning Time-Based Subscription in NDN: A Secure and Efficient Access Control Scheme
Nazatul H. Sultan, Chandan Kumar, Saurab Dulal +3
This paper proposes a novel encryption-based access control mechanism for Named Data Networking (NDN). The scheme allows data producers to share their content in encrypted form bef…
Private Synthetic Data Generation in Bounded Memory
Rayne Holland, Seyit Camtepe, Chandra Thapa +1
We propose , a lightweight synthetic data generator with \textit{differential privacy} guarantees. uses a novel hierarchical decomposition that a…
Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification
Jinting Zhu, Julian Jang-Jaccard, Ian Welch +4
When malware employs an unseen zero-day exploit, traditional security measures such as vulnerability scanners and antivirus software can fail to detect them. This is because these…
Attacking Slicing Network via Side-channel Reinforcement Learning Attack
Wei Shao, Chandra Thapa, Rayne Holland +2
Network slicing in 5G and the future 6G networks will enable the creation of multiple virtualized networks on a shared physical infrastructure. This innovative approach enables the…
MTDSense: AI-Based Fingerprinting of Moving Target Defense Techniques in Software-Defined Networking
Tina Moghaddam, Guowei Yang, Chandra Thapa +2
Moving target defenses (MTD) are proactive security techniques that enhance network security by confusing the attacker and limiting their attack window. MTDs have been shown to hav…
On the Credibility of Backdoor Attacks Against Object Detectors in the Physical World
Bao Gia Doan, Dang Quang Nguyen, Callum Lindquist +7
Object detectors are vulnerable to backdoor attacks. In contrast to classifiers, detectors possess unique characteristics, architecturally and in task execution; often operating in…
EaTVul: ChatGPT-based Evasion Attack Against Software Vulnerability Detection
Shigang Liu, Di Cao, Junae Kim +5
Recently, deep learning has demonstrated promising results in enhancing the accuracy of vulnerability detection and identifying vulnerabilities in software. However, these techniqu…
Optimizing Cyber Defense in Dynamic Active Directories through Reinforcement Learning
Diksha Goel, Kristen Moore, Mingyu Guo +3
This paper addresses a significant gap in Autonomous Cyber Operations (ACO) literature: the absence of effective edge-blocking ACO strategies in dynamic, real-world networks. It sp…
Leakage-Resilient and Carbon-Neutral Aggregation Featuring the Federated AI-enabled Critical Infrastructure
Zehang Deng, Ruoxi Sun, Minhui Xue +4
AI-enabled critical infrastructures (ACIs) integrate artificial intelligence (AI) technologies into various essential systems and services that are vital to the functioning of soci…
Bayesian Learned Models Can Detect Adversarial Malware For Free
Bao Gia Doan, Dang Quang Nguyen, Paul Montague +6
The vulnerability of machine learning-based malware detectors to adversarial attacks has prompted the need for robust solutions. Adversarial training is an effective method but is…
SplITS: Split Input-to-State Mapping for Effective Firmware Fuzzing
Guy Farrelly, Paul Quirk, Salil S. Kanhere +2
Ability to test firmware on embedded devices is critical to discovering vulnerabilities prior to their adversarial exploitation. State-of-the-art automated testing methods rehost f…
Data-Driven Intelligence can Revolutionize Today's Cybersecurity World: A Position Paper
Iqbal H. Sarker, Helge Janicke, Leandros Maglaras +1
As cyber threats evolve and grow progressively more sophisticated, cyber security is becoming a more significant concern in today's digital era. Traditional security measures tend…
Classification and Explanation of Distributed Denial-of-Service (DDoS) Attack Detection using Machine Learning and Shapley Additive Explanation (SHAP) Methods
Yuanyuan Wei, Julian Jang-Jaccard, Amardeep Singh +2
DDoS attacks involve overwhelming a target system with a large number of requests or traffic from multiple sources, disrupting the normal traffic of a targeted server, service, or…
OptimShare: A Unified Framework for Privacy Preserving Data Sharing -- Towards the Practical Utility of Data with Privacy
M. A. P. Chamikara, Seung Ick Jang, Ian Oppermann +9
Tabular data sharing serves as a common method for data exchange. However, sharing sensitive information without adequate privacy protection can compromise individual privacy. Thus…