140 matches found
VeriPHY: Physical Layer Signal Authentication for Wireless Communication in 5G Environments
Physical layer authentication PLA uses inherent characteristics of the communication medium to provide secure and efficient authentication in wireless networks, bypassing the need for traditional cryptographic methods. With advancements in deep learning, PLA has become a widely adopted technique...
Phishing Detection in the Gen-AI Era: Quantized LLMs Vs Classical Models
Phishing attacks are becoming increasingly sophisticated, underscoring the need for detection systems that strike a balance between high accuracy and computational efficiency. This paper presents a comparative evaluation of traditional Machine Learning ML, Deep Learning DL, and quantized...
Exploiting Efficiency Vulnerabilities in Dynamic Deep Learning Systems
The growing deployment of deep learning models in real-world environments has intensified the need for efficient inference under strict latency and resource constraints. To meet these demands, dynamic deep learning systems DDLSs have emerged, offering input-adaptive computation to optimize runtim...
Efficient Retail Video Annotation: a Robust Key Frame Generation Approach for Product and Customer Interaction Analysis
Accurate video annotation plays a vital role in modern retail applications, including customer behavior analysis, product interaction detection, and in-store activity recognition. However, conventional annotation methods heavily rely on time-consuming manual labeling by human annotators,...
A Comprehensive Survey on Underwater Acoustic Target Positioning and Tracking: Progress, Challenges, and Perspectives
Underwater target tracking technology plays a pivotal role in marine resource exploration, environmental monitoring, and national defense security. Given that acoustic waves represent an effective medium for long-distance transmission in aquatic environments, underwater acoustic target tracking h...
Evaluating Explainable AI for Deep Learning-Based Network Intrusion Detection System Alert Classification
A Network Intrusion Detection System NIDS monitors networks for cyber attacks and other unwanted activities. However, NIDS solutions often generate an overwhelming number of alerts daily, making it challenging for analysts to prioritize high-priority threats. While deep learning models promise to...
Ai-Driven Vulnerability Analysis in Smart Contracts: Trends, Challenges and Future Directions
Smart contracts, integral to blockchain ecosystems, enable decentralized applications to execute predefined operations without intermediaries. Their ability to enforce trustless interactions has made them a core component of platforms such as Ethereum. Vulnerabilities such as numerical overflows,...
Synthetic Tabular Data: Methods, Attacks and Defenses
Synthetic data is often positioned as a solution to replace sensitive fixed-size datasets with a source of unlimited matching data, freed from privacy concerns. There has been much progress in synthetic data generation over the last decade, leveraging corresponding advances in machine learning an...
Fingerprinting Deep Learning Models Via Network Traffic Patterns in Federated Learning
Federated Learning FL is increasingly adopted as a decentralized machine learning paradigm due to its capability to preserve data privacy by training models without centralizing user data. However, FL is susceptible to indirect privacy breaches via network traffic analysis-an area not explored in...
Sec5GLoc: Securing 5G Indoor Localization Via Adversary-Resilient Deep Learning Architecture
Emerging 5G millimeter-wave and sub-6 GHz networks enable high-accuracy indoor localization, but security and privacy vulnerabilities pose serious challenges. In this paper, we identify and address threats including location spoofing and adversarial signal manipulation against 5G-based indoor...
A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network
Connected and Autonomous Vehicles CAVs enhance mobility but face cybersecurity threats, particularly through the insecure Controller Area Network CAN bus. Cyberattacks can have devastating consequences in connected vehicles, including the loss of control over critical systems, necessitating robus...
The Ephemeral Threat: Assessing the Security of Algorithmic Trading Systems Powered by Deep Learning
We study the security of stock price forecasting using Deep Learning DL in computational finance. Despite abundant prior research on the vulnerability of DL to adversarial perturbations, such work has hitherto hardly addressed practical adversarial threat models in the context of DL-powered...
MUBox: a Critical Evaluation Framework of Deep Machine Unlearning
Recent legal frameworks have mandated the right to be forgotten, obligating the removal of specific data upon user requests. Machine Unlearning has emerged as a promising solution by selectively removing learned information from machine learning models. This paper presents MUBox, a comprehensive...
Intrusion Detection System Using Deep Learning for Network Security
As the number of cyberattacks and their particualr nature escalate, the need for effective intrusion detection systems IDS has become indispensable for ensuring the security of contemporary networks. Adaptive and more sophisticated threats are often beyond the reach of traditional approaches to...
Efficient Full-Stack Private Federated Deep Learning with Post-Quantum Security
Federated learning FL enables collaborative model training while preserving user data privacy by keeping data local. Despite these advantages, FL remains vulnerable to privacy attacks on user updates and model parameters during training and deployment. Secure aggregation protocols have been...
A Comprehensive Analysis of Adversarial Attacks against Spam Filters
Deep learning has revolutionized email filtering, which is critical to protect users from cyber threats such as spam, malware, and phishing. However, the increasing sophistication of adversarial attacks poses a significant challenge to the effectiveness of these filters. This study investigates t...
Explainable Machine Learning for Cyberattack Identification from Traffic Flows
The increasing automation of traffic management systems has made them prime targets for cyberattacks, disrupting urban mobility and public safety. Traditional network-layer defenses are often inaccessible to transportation agencies, necessitating a machine learning-based approach that relies sole...
Bipartite Randomized Response Mechanism for Local Differential Privacy
With the increasing importance of data privacy, Local Differential Privacy LDP has recently become a strong measure of privacy for protecting each user's privacy from data analysts without relying on a trusted third party. In many cases, both data providers and data analysts hope to maximize the...
Optimized Approaches to Malware Detection: a Study of Machine Learning and Deep Learning Techniques
Digital systems find it challenging to keep up with cybersecurity threats. The daily emergence of more than 560,000 new malware strains poses significant hazards to the digital ecosystem. The traditional malware detection methods fail to operate properly and yield high false positive rates with l...