A comprehensive systematic literature review on intrusion detection systems

M Ozkan-Okay, R Samet, Ö Aslan, D Gupta - IEEE Access, 2021 - ieeexplore.ieee.org
Effectively detecting intrusions in the computer networks still remains problematic. This is
because cyber attackers are changing packet contents to disguise the intrusion detection …

Overview on intrusion detection systems design exploiting machine learning for networking cybersecurity

P Dini, A Elhanashi, A Begni, S Saponara, Q Zheng… - Applied Sciences, 2023 - mdpi.com
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 …

A deep learning model for network intrusion detection with imbalanced data

Y Fu, Y Du, Z Cao, Q Li, W Xiang - Electronics, 2022 - mdpi.com
With an increase in the number and types of network attacks, traditional firewalls and data
encryption methods can no longer meet the needs of current network security. As a result …

A double-layered hybrid approach for network intrusion detection system using combined naive bayes and SVM

T Wisanwanichthan, M Thammawichai - Ieee Access, 2021 - ieeexplore.ieee.org
A pattern matching method (signature-based) is widely used in basic network intrusion
detection systems (IDS). A more robust method is to use a machine learning classifier to …

Network intrusion detection of drones using recurrent neural networks

Y Sucharitha, PCS Reddy… - … : Future Trends and …, 2023 - Wiley Online Library
Summary Flying Ad Hoc Network (FANET) has obtained a great deal of interest over recent
times because of their significant applications. Thus, various examinations have been led on …

Addressing the class imbalance problem in network intrusion detection systems using data resampling and deep learning

A Abdelkhalek, M Mashaly - The journal of Supercomputing, 2023 - Springer
Network intrusion detection systems (NIDS) are the most common tool used to detect
malicious attacks on a network. They help prevent the ever-increasing different attacks and …

A hybrid heuristics artificial intelligence feature selection for intrusion detection classifiers in cloud of things

AK Sangaiah, A Javadpour, F Ja'fari, P Pinto… - Cluster …, 2023 - Springer
Cloud computing environments provide users with Internet-based services and one of their
main challenges is security issues. Hence, using Intrusion Detection Systems (IDSs) as a …

A deep learning integrated blockchain framework for securing industrial IoT

A Aljuhani, P Kumar, R Alanazi… - IEEE Internet of …, 2023 - ieeexplore.ieee.org
The Industrial Internet of Things (IIoT) is a collection of interconnected smart sensors and
actuators with industrial software tools and applications. IIoT aims to enhance manufacturing …

Machine learning-based adaptive synthetic sampling technique for intrusion detection

M Zakariah, SA AlQahtani, MS Al-Rakhami - Applied Sciences, 2023 - mdpi.com
Traditional firewalls and data encryption techniques can no longer match the demands of
current IoT network security due to the rising amount and variety of network threats. In order …

Malware cyberattacks detection using a novel feature selection method based on a modified whale optimization algorithm

RRN Al Ogaili, ES Alomari, MBM Alkorani… - Wireless …, 2023 - Springer
Malware cyberattacks have increased rapidly with the rise of Internet users, IoT devices,
smart cities, etc. Attackers are constantly trying to evolve their methods and attack …