Internet of things applications, security challenges, attacks, intrusion detection, and future visions: A systematic review
N Mishra, S Pandya - IEEE Access, 2021 - ieeexplore.ieee.org
Internet of Things (IoT) technology is prospering and entering every part of our lives, be it
education, home, vehicles, or healthcare. With the increase in the number of connected …
education, home, vehicles, or healthcare. With the increase in the number of connected …
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 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 …
Variational restricted Boltzmann machines to automated anomaly detection
Data-driven methods are implemented using particularly complex scenarios that reflect in-
depth perennial knowledge and research. Hence, the available intelligent algorithms are …
depth perennial knowledge and research. Hence, the available intelligent algorithms are …
Artificial intelligence and machine learning in cybersecurity: Applications, challenges, and opportunities for mis academics
The availability of massive amounts of data, fast computers, and superior machine learning
(ML) algorithms has spurred interest in artificial intelligence (AI). It is no surprise, then, that …
(ML) algorithms has spurred interest in artificial intelligence (AI). It is no surprise, then, that …
On the evaluation of sequential machine learning for network intrusion detection
Recent advances in deep learning renewed the research interests in machine learning for
Network Intrusion Detection Systems (NIDS). Specifically, attention has been given to …
Network Intrusion Detection Systems (NIDS). Specifically, attention has been given to …
Deep encrypted traffic detection: An anomaly detection framework for encryption traffic based on parallel automatic feature extraction
G Long, Z Zhang - Computational Intelligence and …, 2023 - Wiley Online Library
With an increasing number of network attacks using encrypted communication, the anomaly
detection of encryption traffic is of great importance to ensure reliable network operation …
detection of encryption traffic is of great importance to ensure reliable network operation …
IoT botnet detection with feature reconstruction and interval optimization
The existing botnet detection methods have the problems of uneven sampling, poor feature
selection, and weak generalization ability, resulting in low detection and classification …
selection, and weak generalization ability, resulting in low detection and classification …
An HTTP anomaly detection architecture based on the internet of intelligence
The prompt expansion of the Internet of Things (IoT) and its wide application in smart homes
and transportation has brought tremendous convenience to people's lives. However, the …
and transportation has brought tremendous convenience to people's lives. However, the …
Robustness Evaluation of Network Intrusion Detection Systems based on Sequential Machine Learning
The rise of sequential Machine Learning (ML) methods has paved the way for a new
generation of Network Intrusion Detection Systems (NIDS) which base their classification on …
generation of Network Intrusion Detection Systems (NIDS) which base their classification on …