A multi-layer classification approach for intrusion detection in iot networks based on deep learning

R Qaddoura, A M. Al-Zoubi, H Faris, I Almomani - Sensors, 2021 - mdpi.com
The security of IoT networks is an important concern to researchers and business owners,
which is taken into careful consideration due to its direct impact on the availability of the …

Hybrid deep-learning model to detect botnet attacks over internet of things environments

MY Alzahrani, AM Bamhdi - Soft Computing, 2022 - Springer
In recent years, the use of the internet of things (IoT) has increased dramatically, and
cybersecurity concerns have grown in tandem. Cybersecurity has become a major …

A simulation work for generating a novel dataset to detect distributed denial of service attacks on Vehicular Ad hoc NETwork systems

FA Alhaidari, AM Alrehan - International Journal of …, 2021 - journals.sagepub.com
Vehicular Ad hoc NETwork is a promising technology providing important facilities for
modern transportation systems. It has garnered much interest from researchers studying the …

A perspective review of security challenges in body area networks for healthcare applications

JV Ananthi, PSH Jose - International Journal of Wireless Information …, 2021 - Springer
Body area network (BAN) connects sensors and actuators to the human body in order to
collect patient's information and transmitting it to doctors in a confined space with limited …

A review on machine learning approaches for network malicious behavior detection in emerging technologies

M Rabbani, Y Wang, R Khoshkangini, H Jelodar… - Entropy, 2021 - mdpi.com
Network anomaly detection systems (NADSs) play a significant role in every network
defense system as they detect and prevent malicious activities. Therefore, this paper offers …

An improved long short term memory network for intrusion detection

AA Awad, AF Ali, T Gaber - Plos one, 2023 - journals.plos.org
Over the years, intrusion detection system has played a crucial role in network security by
discovering attacks from network traffics and generating an alarm signal to be sent to the …

BoAu: Malicious traffic detection with noise labels based on boundary augmentation

Q Yuan, C Liu, W Yu, Y Zhu, G Xiong, Y Wang… - Computers & Security, 2023 - Elsevier
The effectiveness of deep-learning-based malicious traffic detection systems relies on high-
quality labeled traffic datasets. However, malicious traffic labeling approaches can easily …

The proposition and evaluation of the roedunet-simargl2021 network intrusion detection dataset

ME Mihailescu, D Mihai, M Carabas, M Komisarek… - Sensors, 2021 - mdpi.com
Cybersecurity is an arms race, with both the security and the adversaries attempting to
outsmart one another, coming up with new attacks, new ways to defend against those …

Real-time intrusion detection in wireless network: A deep learning-based intelligent mechanism

L Yang, J Li, L Yin, Z Sun, Y Zhao, Z Li - Ieee Access, 2020 - ieeexplore.ieee.org
With the development of the wireless network techniques, the number of cyber-attack
increases significantly, which has seriously threat the security of Wireless Local Area …

A Novel Intelligent‐Based Intrusion Detection System Approach Using Deep Multilayer Classification

A Ugendhar, B Illuri, SR Vulapula… - Mathematical …, 2022 - Wiley Online Library
Cybersecurity in information technology (IT) infrastructures is one of the most significant and
complex issues of the digital era. Increases in network size and associated data have …