8,299 papers across 0 tags, ranked by community signal and explained.
The proactive identification of threats, response to incidents, and prediction of patterns of future cyber threats by organizations using machine learning and deep learning techniq…
The fast growth of 6G-enabled Internet of Things (IoT) networks has transformed communication and made it possible for smart cities, driverless cars, healthcare, and industrial aut…
Lightweight machine learning models are increasingly proposed for intrusion detection in Industrial Internet of Things (IIoT) networks due to their suitability for resourc
Understanding how aligned LLMs internally represent safety is critical for diagnosing alignment vulnerabilities, as it explains why jailbreaks succeed and inform
This study discusses two widely-recognized deep learning approaches for network intrusion detection: a Deep Neural Network (DNN) and a Recurrent Neural Network (RNN). Both models a…
This study presents a comprehensive comparative analysis of four traditional machine learning algorithms Decision Tree, Random Forest, K-Nearest Neighbors, and Support Vector Machi…
Large language models (LLMs) are highly susceptible to backdoor attacks (BAs), wherein training samples are poisoned using trigger-based harmful content. Furthermore, existing defe…
Mobile on-device AI (MoAI) systems that integrate locally deployed AI models with conventional mobile software components are emerging as a key paradigm for delivering int
Embedding models are essential components of modern Information Retrieval (IR) systems, yet they are typically hidden behind APIs. Recent works have shown that dense IR sy
Large language models (LLMs) can be induced to produce harmful content through multi turn strategies in which no single user message appears clearly unsafe. Existing runti
Intrusion Detection Systems (IDSs) are essential for monitoring network traffic and identifying malicious activities in modern cyber-physical, Internet of Things (IoT), en
Cyber-physical systems (CPSs) are increasingly deployed in every aspect of our lives and can be compromised through memory corruption vulnerabilities, allowing attackers t
Malware poses a critical and ever-evolving threat, and robust and effective systems for detecting and classifying malware are of essential importance. $n$-grams features a
Large Vision-Language Models (LVLMs) have been increasingly integrated into robotic systems. However, these models may exhibit overthinking behaviors, where they generate
Adversarial attacks on cybersecurity classifiers pose a dual threat: degrading predictions and destabilising the SHAP-based explanations that security analysts rely on to
Recently, speech classification methods have gained widespread adoption in intelligent gadgets. Current study indicates that backdoor attacks provide a substantial securit
We present HaloGuard 1.0, an open-weights implementation of the constitutional-classifier paradigm for input safety. It achieves state-of-the-art performance on English
As Large Language Models (LLMs) and agentic systems become integrated into real-world applications, ensuring their safety and security is critical. Guardrail systems that
A computer worm is malware that spreads on a network by replicating itself from one machine to another. Traditional worms, like WannaCry, exploited predetermined vulnerabilities, a…
Cybersecurity threats cost organizations $6 trillion annually [2], with traditional signature-based antivirus systems achieving only 78.5% detection accuracy and failing completely…
In contemporary malware detection, machine learning has proved an essential component; many models are not transparent, making them less trustworthy and rarely suitable for securit…
This paper proposes an intelligent and adaptive security framework for internet of things (IoT) environments by integrating edge Honeypots, AI‐driven intrusion detection systems (I…
Highlights What are the main findings? We propose a Structured Tensor Streaming with Adversarial Training (STS-AT) framework that achieves over 99% accuracy in normal traffic class…
The rapid expansion of cloud, edge, and Internet of Things environments has increased both the scale and complexity of modern cyber-attacks. Conventional detection systems that ope…