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All Journal International Journal of Electrical and Computer Engineering JOIV : International Journal on Informatics Visualization Indonesian Journal of Electrical Engineering and Computer Science
Mardenov, Yerik
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Computer Science & IT Electrical & Electronics Engineering

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Towards robust security in WSN: a comprehensive analytical review and future research directions Zhukabayeva, Tamara; Zholshiyeva, Lazzat; Ven-Tsen, Khu; Mardenov, Yerik; Adamova, Aigul; Karabayev, Nurdaulet; Abdildayeva, Assel; Baumuratova, Dilaram
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp318-337

Abstract

One of the most important aspects of the effective functioning of wireless sensor network (WSN) is their security. Despite significant progress in WSN security, there are still several unresolved issues. Many review studies have been published on the problems of possible attacks on WSN and their identification. However, due to the lack of their systematic analysis, it is not possible to fully substantiate practical recommendations for the effective application of the proposed solutions in the field of WSN security. In particular, the creation of methods that provide a high degree of security while minimizing computational effort and costs, and the development of effective methods for detecting and preventing attacks on WSN. The purpose of this document is to fill this gap. The article presents the results of the study in the form of a systematic analysis of the literature with a targeted selection of sources to identify the most effective methods for detecting and preventing attacks on WSN. By identifying the security of WSN, which has not yet been addressed in research works, the review aims to reduce its impact. As a result, our extended taxonomy is presented, including attack types, datasets, effective WSN attack detection methods, countermeasures, and intrusion detection systems (IDS).
Tackling the anomaly detection challenge in large-scale wireless sensor networks Zhukabayeva, Tamara; Adamova, Aigul; Zholshiyeva, Lazzat; Mardenov, Yerik; Karabayev, Nurdaulet; Baumuratova, Dilaram
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2479-2490

Abstract

One of the areas of ensuring the security of a wireless sensor network (WSN) is anomaly detection, which identifies deviations from normal behavior. In our paper, we investigate the optimal anomaly detection algorithms in a WSN. We highlight the problems in anomaly detection, and we also propose a new methodology using machine learning. The effectiveness of the k-nearest neighbor (kNN) and Z Score methods is evaluated on the data obtained from WSN devices in real time. According to the experimental study, the Z Score methodology showed a 98.9% level of accuracy, which was much superior to the kNN 43.7% method. In order to ensure accurate anomaly detection, it is crucial to have access to high-quality data when conducting a study. Our research enhances the field of WSN security by offering a novel approach for detecting anomalies. We compare the performance of two methods and provide evidence of the superior effectiveness of the Z Score method. Our future research will focus on exploring and comparing several approaches to identify the most effective anomaly detection method, with the ultimate goal of enhancing the security of WSN.
Network Attack Detection Using NeuroEvolution of Augmenting Topologies (NEAT) Algorithm Zhukabayeva, Tamara; Adamova, Aigul; Ven-Tsen, Khu; Nurlan, Zhanserik; Mardenov, Yerik; Karabayev, Nurdaulet
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2220

Abstract

The imperfection of existing intrusion detection methods and the changing nature of malicious actions on the attacker's part led to the Internet of Things (IoT) network interaction in an unsafe state. The actual problem of improving the technology of the IOT is counteracting malicious network impacts. In this regard, research and development aimed at creating effective tools for solving applied problems within the framework of this problem are becoming increasingly important.  This study seeks to develop tools for detecting anomalous network conditions resulting from malicious attacks. In particular, the accuracy of the identification of DoS and DDoS attacks is sufficient for operational use. This study analyzes various multi-level architectures, relevant communication protocols, and different types of network attacks. The presented research was conducted on open datasets TON_IOT DATASETS, which include multiple data sources collected from IoT sensors. The modified HyperNEAT algorithm was used as the basis for the development. The NEAT methodology used in the study allows you to combine various network nodes. Results of the study: a neuro-evolutionary algorithm for identifying DoS and DDoS attacks was implemented, integrated, and real-tested based on a multi-level analysis of network traffic combined with various adaptive modules. The accuracy of identifying DoS and DDoS attacks is 0.9242 in the Accuracy metric. The study implies that the proposed approach can be recommended for network intrusion detection, ensuring security when interacting with the IoT.
Co-Authors Abdildayeva, Assel Adamova, Aigul Baumuratova, Dilaram Karabayev, Nurdaulet Nurlan, Zhanserik Ven-Tsen, Khu Zholshiyeva, Lazzat Zhukabayeva, Tamara