An Efficient Flow-Based Anomaly Detection System for Enhanced Security in IoT Networks DOI Creative Commons
Ibrahim Mutambik

Sensors, Journal Year: 2024, Volume and Issue: 24(22), P. 7408 - 7408

Published: Nov. 20, 2024

The growing integration of Internet Things (IoT) devices into various sectors like healthcare, transportation, and agriculture has dramatically increased their presence in everyday life. However, this rapid expansion exposed new vulnerabilities within computer networks, creating security challenges. These IoT devices, often limited by hardware constraints, lack advanced features, making them easy targets for attackers compromising overall network integrity. To counteract these issues, Behavioral-based Intrusion Detection Systems (IDS) have been proposed as a potential solution safeguarding networks. While IDS demonstrated ability to detect threats effectively, they encounter practical challenges due reliance on pre-labeled data the heavy computational power require, limiting deployment. This research introduces IoT-FIDS (Flow-based System IoT), lightweight efficient anomaly detection framework tailored environments. Instead employing traditional machine learning techniques, focuses identifying unusual behaviors examining flow-based representations that capture standard device communication patterns, services used, packet header details. By analyzing only benign traffic, network-based offers streamlined approach securing Our experimental results reveal can accurately most abnormal traffic patterns with minimal false positives, it feasible real-world implementations.

Language: Английский

Enhancing IoT Security Using GA-HDLAD: A Hybrid Deep Learning Approach for Anomaly Detection DOI Creative Commons
Ibrahim Mutambik

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(21), P. 9848 - 9848

Published: Oct. 28, 2024

The adoption and use of the Internet Things (IoT) have increased rapidly over recent years, cyber threats in IoT devices also become more common. Thus, development a system that can effectively identify malicious attacks reduce security has topic great importance. One most serious comes from botnets, which commonly attack by interrupting networks required for to run. There are number methods be used improve identifying unknown patterns networks, including deep learning machine approaches. In this study, an algorithm named genetic with hybrid learning-based anomaly detection (GA-HDLAD) is developed, aim improving botnets within environment. GA-HDLAD technique addresses problem high dimensionality using during feature selection. Hybrid detect botnets; approach combination recurrent neural (RNNs), extraction techniques (FETs), attention concepts. Botnet involve complex (HDL) method detect. Moreover, FETs model ensures features extracted spatial data, while temporal dependencies captured RNNs. Simulated annealing (SA) utilized select hyperparameters necessary HDL approach. experimentally assessed benchmark botnet dataset, findings reveal provides superior results comparison existing methods.

Language: Английский

Citations

2
An Efficient Flow-Based Anomaly Detection System for Enhanced Security in IoT Networks DOI Creative Commons
Ibrahim Mutambik

Sensors, Journal Year: 2024, Volume and Issue: 24(22), P. 7408 - 7408

Published: Nov. 20, 2024

The growing integration of Internet Things (IoT) devices into various sectors like healthcare, transportation, and agriculture has dramatically increased their presence in everyday life. However, this rapid expansion exposed new vulnerabilities within computer networks, creating security challenges. These IoT devices, often limited by hardware constraints, lack advanced features, making them easy targets for attackers compromising overall network integrity. To counteract these issues, Behavioral-based Intrusion Detection Systems (IDS) have been proposed as a potential solution safeguarding networks. While IDS demonstrated ability to detect threats effectively, they encounter practical challenges due reliance on pre-labeled data the heavy computational power require, limiting deployment. This research introduces IoT-FIDS (Flow-based System IoT), lightweight efficient anomaly detection framework tailored environments. Instead employing traditional machine learning techniques, focuses identifying unusual behaviors examining flow-based representations that capture standard device communication patterns, services used, packet header details. By analyzing only benign traffic, network-based offers streamlined approach securing Our experimental results reveal can accurately most abnormal traffic patterns with minimal false positives, it feasible real-world implementations.

Language: Английский

Citations

1