Overview on intrusion detection systems design exploiting machine learning for networking cybersecurity
The Intrusion Detection System (IDS) is an effective tool utilized in cybersecurity systems to
detect and identify intrusion attacks. With the increasing volume of data generation, the …
detect and identify intrusion attacks. With the increasing volume of data generation, the …
IGRF-RFE: a hybrid feature selection method for MLP-based network intrusion detection on UNSW-NB15 dataset
The effectiveness of machine learning models can be significantly averse to redundant and
irrelevant features present in the large dataset which can cause drastic performance …
irrelevant features present in the large dataset which can cause drastic performance …
IMIDS: An intelligent intrusion detection system against cyber threats in IoT
The increasing popularity of the Internet of Things (IoT) has significantly impacted our daily
lives in the past few years. On one hand, it brings convenience, simplicity, and efficiency for …
lives in the past few years. On one hand, it brings convenience, simplicity, and efficiency for …
Chameleon: Optimized feature selection using particle swarm optimization and ensemble methods for network anomaly detection
In this paper, we propose an optimization approach by leveraging swarm intelligence and
ensemble methods to solve the non-deterministic feature selection problem. The proposed …
ensemble methods to solve the non-deterministic feature selection problem. The proposed …
Network Anomaly Detection Using Autoencoder on Various Datasets: A Comprehensive Review
R Singh, N Srivastava, A Kumar - Recent Patents on …, 2024 - ingentaconnect.com
The scientific community is currently very concerned about information and communication
technology security because any assault or network anomaly can have a remarkable …
technology security because any assault or network anomaly can have a remarkable …
[HTML][HTML] An integrated intrusion detection framework based on subspace clustering and ensemble learning
J Zhu, X Liu - Computers and Electrical Engineering, 2024 - Elsevier
In the rapidly evolving landscape of the Internet of Things (IoT), ensuring robust intrusion
detection is paramount for device and data security. This paper proposes a novel method for …
detection is paramount for device and data security. This paper proposes a novel method for …
[Retracted] Cyber Security against Intrusion Detection Using Ensemble‐Based Approaches
MN Alatawi, N Alsubaie, H Ullah Khan… - Security and …, 2023 - Wiley Online Library
The attacks of cyber are rapidly increasing due to advanced techniques applied by hackers.
Furthermore, cyber security is demanding day by day, as cybercriminals are performing …
Furthermore, cyber security is demanding day by day, as cybercriminals are performing …
Hybrid deep learning-based intrusion detection system for industrial internet of things
The internet of things (IoT) is expected to offer a significant impact on the industry domain
leading to the concept of industrial IoT (IIoT). The IIoT comprises machine-to-machine (M2M) …
leading to the concept of industrial IoT (IIoT). The IIoT comprises machine-to-machine (M2M) …
Efficient approach for anomaly detection in internet of things traffic using deep learning
The network intrusion detection system (NIDs) is a significant research milestone in
information security. NIDs can scan and analyze the network to detect an attack or anomaly …
information security. NIDs can scan and analyze the network to detect an attack or anomaly …
A weighted machine learning-based attacks classification to alleviating class imbalance
The Industrial Internet of Things (IIoT) has become very popular in recent years. However,
IIoT is still an attractive and vulnerable target for attackers to exploit and experiment with …
IIoT is still an attractive and vulnerable target for attackers to exploit and experiment with …