相关论文: MAIDS: Malicious Agent Identification-based Data S…

Mobile collaborative learning (MCL) is highly acknowledged and focusing paradigm in eductional institutions and several organizations across the world. It exhibits intellectual synergy of various combined minds to handle the problem and…

Adversarial examples can represent a serious threat to machine learning (ML) algorithms. If used to manipulate the behaviour of ML-based Network Intrusion Detection Systems (NIDS), they can jeopardize network security. In this work, we aim…

A malicious cloud user may exploit outsourced data involved in online communication, co-residency, and hypervisor vulnerabilities to breach and hamper sensitive information, and inject malicious traffic-based congestion, rendering services…

There is a dynamic escalation and extension in the new infrastructure, educating personnel and licensing new computer programs in the field of IT, due to the emergence of Cloud Computing (CC) paradigm. It has become a quick growing segment…

With the rapid development of artificial intelligence technology, its application in the optimization of complex computer systems is becoming more and more extensive. Edge computing is an efficient distributed computing architecture, and…

The combination of cloud technology, machine learning, and data visualization techniques allows hybrid enterprise networks to hold massive volumes of data and provide employees and customers easy access to these cloud data. These massive…

Network Intrusion Detection Systems (NIDS) are a fundamental tool in cybersecurity. Their ability to generalize across diverse networks is a critical factor in their effectiveness and a prerequisite for real-world applications. In this…

Malware developers use combinations of techniques such as compression, encryption, and obfuscation to bypass anti-virus software. Malware with anti-analysis technologies can bypass AI-based anti-virus software and malware analysis tools.…

HIV/AIDS spread depends upon complex patterns of interaction among various sub-sets emerging at population level. This added complexity makes it difficult to study and model AIDS and its dynamics. AIDS is therefore a natural candidate to be…

Current Cloud solutions for Edge Computing are inefficient for data-centric applications, as they focus on the IaaS/PaaS level and they miss the data modeling and operations perspective. Consequently, Edge Computing opportunities are lost…

In this study, a new ensemble approach for classifiers is introduced. A verification method for better error elimination is developed through the integration of multiple classifiers. A multi-agent system comprised of multiple classifiers is…

LLM-based multi-agent systems (MAS) demonstrate increasing integration into next-generation applications, but their safety in backdoor attacks remains largely underexplored. However, existing research has focused exclusively on single-agent…

The offensive security landscape is highly fragmented: enterprise platforms avoid memory-corruption vulnerabilities due to Denial of Service (DoS) risks, Automatic Exploit Generation (AEG) systems suffer from semantic blindness, and Large…

Since it is impossible to predict and identify all the vulnerabilities of a network beforehand, and penetration into a system by malicious intruders cannot always be prevented, intrusion detection systems (IDSs) are essential entities to…

The rapid proliferation of unmanned aerial vehicles (UAVs) and their applications in diverse domains, such as surveillance, disaster management, agriculture, and defense, have revolutionized modern technology. While the potential benefits…

The power grid is a critical infrastructure essential for public safety and welfare. As its reliance on digital technologies grows, so do its vulnerabilities to sophisticated cyber threats, which could severely disrupt operations. Effective…

Recent studies have proven that deep neural networks are vulnerable to backdoor attacks. Specifically, by mixing a small number of poisoned samples into the training set, the behavior of the trained model can be maliciously controlled.…

This study examines how Artificial Intelligence can aid in identifying and mitigating cyber threats in the U.S. across four key areas: intrusion detection, malware classification, phishing detection, and insider threat analysis. Each of…

Healthcare 5.0 integrates Artificial Intelligence (AI), the Internet of Things (IoT), real-time monitoring, and human-centered design toward personalized medicine and predictive diagnostics. However, the increasing reliance on…

This paper summarizes the research conducted for a malware detection project using the Canadian Institute for Cybersecurity's MalMemAnalysis-2022 dataset. The purpose of the project was to explore the effectiveness and efficiency of machine…