Adversarial machine learning for network intrusion detection systems: A comprehensive survey
Network-based Intrusion Detection System (NIDS) forms the frontline defence against
network attacks that compromise the security of the data, systems, and networks. In recent …
network attacks that compromise the security of the data, systems, and networks. In recent …
[HTML][HTML] Unveiling the dynamics of AI applications: A review of reviews using scientometrics and BERTopic modeling
In a world that has rapidly transformed through the advent of artificial intelligence (AI), our
systematic review, guided by the PRISMA protocol, investigates a decade of AI research …
systematic review, guided by the PRISMA protocol, investigates a decade of AI research …
The role of machine learning in cybersecurity
Machine Learning (ML) represents a pivotal technology for current and future information
systems, and many domains already leverage the capabilities of ML. However, deployment …
systems, and many domains already leverage the capabilities of ML. However, deployment …
Adversarial examples: A survey of attacks and defenses in deep learning-enabled cybersecurity systems
Over the last few years, the adoption of machine learning in a wide range of domains has
been remarkable. Deep learning, in particular, has been extensively used to drive …
been remarkable. Deep learning, in particular, has been extensively used to drive …
A novel method for improving the robustness of deep learning-based malware detectors against adversarial attacks
Malware is constantly evolving with rising concern for cyberspace. Deep learning-based
malware detectors are being used as a potential solution. However, these detectors are …
malware detectors are being used as a potential solution. However, these detectors are …
Adversarial Deep Learning approach detection and defense against DDoS attacks in SDN environments
Over the last few years, Software Defined Networking (SDN) paradigm has become an
emerging architecture to design future networks and to meet new application demands. SDN …
emerging architecture to design future networks and to meet new application demands. SDN …
Detection of sql injection attack using machine learning techniques: a systematic literature review
An SQL injection attack, usually occur when the attacker (s) modify, delete, read, and copy
data from database servers and are among the most damaging of web application attacks. A …
data from database servers and are among the most damaging of web application attacks. A …
[HTML][HTML] IoT: Communication protocols and security threats
In this study, we review the fundamentals of IoT architecture and we thoroughly present the
communication protocols that have been invented especially for IoT technology. Moreover …
communication protocols that have been invented especially for IoT technology. Moreover …
Adversarial machine learning attacks against intrusion detection systems: A survey on strategies and defense
Concerns about cybersecurity and attack methods have risen in the information age. Many
techniques are used to detect or deter attacks, such as intrusion detection systems (IDSs) …
techniques are used to detect or deter attacks, such as intrusion detection systems (IDSs) …
Modeling realistic adversarial attacks against network intrusion detection systems
The incremental diffusion of machine learning algorithms in supporting cybersecurity is
creating novel defensive opportunities but also new types of risks. Multiple researches have …
creating novel defensive opportunities but also new types of risks. Multiple researches have …