Fusion-based anomaly detection system using modified isolation forest for internet of things
In recent years, advanced threat and zero day attacks are increasing significantly, but the
traditional network intrusion detection system based on feature filtering or based on a well …
traditional network intrusion detection system based on feature filtering or based on a well …
Evasion Attack and Defense On Machine Learning Models in Cyber-Physical Systems: A Survey
Cyber-physical systems (CPS) are increasingly relying on machine learning (ML)
techniques to reduce labor costs and improve efficiency. However, the adoption of ML also …
techniques to reduce labor costs and improve efficiency. However, the adoption of ML also …
A novel deep learning based model to defend network intrusion detection system against adversarial attacks
Network Intrusion Detection System (NIDS) is an essential tool in securing cyberspace from
a variety of security risks and unknown cyberattacks. A number of solutions have been …
a variety of security risks and unknown cyberattacks. A number of solutions have been …
Senet-i: An approach for detecting network intrusions through serialized network traffic images
The exponential growth of the internet and inter-connectivity has resulted in an extensive
increase in network size and the corresponding data, which has led to numerous novel …
increase in network size and the corresponding data, which has led to numerous novel …
Machine learning raw network traffic detection
Increasingly cyber-attacks are sophisticated and occur rapidly, necessitating the use of
machine learning techniques for detection at machine speed. However, the use of machine …
machine learning techniques for detection at machine speed. However, the use of machine …
Fence: Feasible evasion attacks on neural networks in constrained environments
A Chernikova, A Oprea - ACM Transactions on Privacy and Security, 2022 - dl.acm.org
As advances in Deep Neural Networks (DNNs) demonstrate unprecedented levels of
performance in many critical applications, their vulnerability to attacks is still an open …
performance in many critical applications, their vulnerability to attacks is still an open …
Enhancing the sustainability of deep-learning-based network intrusion detection classifiers against adversarial attacks
A Alotaibi, MA Rassam - Sustainability, 2023 - mdpi.com
An intrusion detection system (IDS) is an effective tool for securing networks and a
dependable technique for improving a user's internet security. It informs the administration …
dependable technique for improving a user's internet security. It informs the administration …
Efficient intrusion detection using multi-player generative adversarial networks (GANs): an ensemble-based deep learning architecture
R Soleymanzadeh, R Kashef - Neural Computing and Applications, 2023 - Springer
Intrusion detection systems (IDSs) investigate various attacks, identify malicious patterns,
and implement effective control strategies. With the recent advances in machine learning …
and implement effective control strategies. With the recent advances in machine learning …
Mitigation of black-box attacks on intrusion detection systems-based ml
Intrusion detection systems (IDS) are a very vital part of network security, as they can be
used to protect the network from illegal intrusions and communications. To detect malicious …
used to protect the network from illegal intrusions and communications. To detect malicious …
Constrained optimization based adversarial example generation for transfer attacks in network intrusion detection systems
M Chale, B Cox, J Weir, ND Bastian - Optimization Letters, 2023 - Springer
Deep learning has enabled network intrusion detection rates as high as 99.9% for malicious
network packets without requiring feature engineering. Adversarial machine learning …
network packets without requiring feature engineering. Adversarial machine learning …