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 …
A survey of cyber attacks on cyber physical systems: Recent advances and challenges
W Duo, MC Zhou, A Abusorrah - IEEE/CAA Journal of …, 2022 - ieeexplore.ieee.org
A cyber physical system (CPS) is a complex system that integrates sensing, computation,
control and networking into physical processes and objects over Internet. It plays a key role …
control and networking into physical processes and objects over Internet. It plays a key role …
Future smart cities: Requirements, emerging technologies, applications, challenges, and future aspects
Future smart cities are the key to fulfilling the ever-growing demands of citizens. Information
and communication advancements will empower better administration of accessible …
and communication advancements will empower better administration of accessible …
Recent advances in trustworthy explainable artificial intelligence: Status, challenges, and perspectives
Artificial intelligence (AI) and machine learning (ML) have come a long way from the earlier
days of conceptual theories, to being an integral part of today's technological society. Rapid …
days of conceptual theories, to being an integral part of today's technological society. Rapid …
Reinforcement learning for iot security: A comprehensive survey
The number of connected smart devices has been increasing exponentially for different
Internet-of-Things (IoT) applications. Security has been a long run challenge in the IoT …
Internet-of-Things (IoT) applications. Security has been a long run challenge in the IoT …
A survey on deep learning for cybersecurity: Progress, challenges, and opportunities
As the number of Internet-connected systems rises, cyber analysts find it increasingly difficult
to effectively monitor the produced volume of data, its velocity and diversity. Signature-based …
to effectively monitor the produced volume of data, its velocity and diversity. Signature-based …
Adversarial machine learning in wireless communications using RF data: A review
D Adesina, CC Hsieh, YE Sagduyu… - … Surveys & Tutorials, 2022 - ieeexplore.ieee.org
Machine learning (ML) provides effective means to learn from spectrum data and solve
complex tasks involved in wireless communications. Supported by recent advances in …
complex tasks involved in wireless communications. Supported by recent advances in …
A survey on threat hunting in enterprise networks
With the rapidly evolving technological landscape, the huge development of the Internet of
Things, and the embracing of digital transformation, the world is witnessing an explosion in …
Things, and the embracing of digital transformation, the world is witnessing an explosion in …
Adversarial machine learning: A multilayer review of the state-of-the-art and challenges for wireless and mobile systems
J Liu, M Nogueira, J Fernandes… - … Surveys & Tutorials, 2021 - ieeexplore.ieee.org
Machine Learning (ML) models are susceptible to adversarial samples that appear as
normal samples but have some imperceptible noise added to them with the intention of …
normal samples but have some imperceptible noise added to them with the intention of …
On the performance of machine learning models for anomaly-based intelligent intrusion detection systems for the internet of things
G Abdelmoumin, DB Rawat… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
Anomaly-based machine learning-enabled intrusion detection systems (AML-IDSs) show
low performance and prediction accuracy while detecting intrusions in the Internet of Things …
low performance and prediction accuracy while detecting intrusions in the Internet of Things …