An Anomaly Intrusion Detection for High-Density Internet of Things Wireless Communication Network Based Deep Learning Algorithms

Salman, Emad Hmood; Taher, Montadar Abas; Hammadi, Yousif I.; Mahmood, Omar; Muthanna, Ammar; Koucheryavy, Andrey

doi:10.3390/s23010206

Cited by 15 publications

(7 citation statements)

References 46 publications

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“…Proposed WCGAN-GP [36] WAE-DNN [37] EHHO + GRU [39] Custom CNN + LSTM [43] IG-CS-PSO + RF [45] ABC-BWO-CONV-LSTM [40] RF-RFE + ensemble [44] FOA + ensemble method [42] Bagging BGM [ As the reach of IoT networks grows, the need for efficient IDS like the one presented in this study becomes increasingly critical. Future research efforts will focus on balancing attack category distributions in datasets.…”

Section: Discussionmentioning

confidence: 99%

A Novel Hybrid Feature Selection with Cascaded LSTM: Enhancing Security in IoT Networks

Sundaram,

Natarajan,

Perumalsamy

et al. 2024

Wireless Communications and Mobile Computing

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The rapid growth of the Internet of Things (IoT) has created a situation where a huge amount of sensitive data is constantly being created and sent through many devices, making data security a top priority. In the complex network of IoT, detecting intrusions becomes a key part of strengthening security. Since IoT environments can be easily affected by a wide range of cyber threats, intrusion detection systems (IDS) are crucial for quickly finding and dealing with potential intrusions as they happen. IDS datasets can have a wide range of features, from just a few to several hundreds or even thousands. Managing such large datasets is a big challenge, requiring a lot of computer power and leading to long processing times. To build an efficient IDS, this article introduces a combined feature selection strategy using recursive feature elimination and information gain. Then, a cascaded long–short-term memory is used to improve attack classifications. This method achieved an accuracy of 98.96% and 99.30% on the NSL-KDD and UNSW-NB15 datasets, respectively, for performing binary classification. This research provides a practical strategy for improving the effectiveness and accuracy of intrusion detection in IoT networks.

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Section: Discussionmentioning

confidence: 99%

A Novel Hybrid Feature Selection with Cascaded LSTM: Enhancing Security in IoT Networks

Sundaram,

Natarajan,

Perumalsamy

et al. 2024

Wireless Communications and Mobile Computing

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“…In 23 , two different approaches were introduced for intrusion detection in IoT systems. The first method combined a custom CNN with Long Short-Term Memory (LSTM) deep network layers.…”

Section: Literature Reviewmentioning

confidence: 99%

Firefly algorithm based WSN-IoT security enhancement with machine learning for intrusion detection

Karthikeyan,

Manimegalai,

RajaGopal

2024

Sci Rep

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A Wireless Sensor Network (WSN) aided by the Internet of Things (IoT) is a collaborative system of WSN systems and IoT networks are work to exchange, gather, and handle data. The primary objective of this collaboration is to enhance data analysis and automation to facilitate improved decision-making. Securing IoT with the assistance of WSN necessitates the implementation of protective measures to confirm the safety and reliability of the interconnected WSN and IoT components. This research significantly advances the current state of the art in IoT and WSN security by synergistically harnessing the potential of machine learning and the Firefly Algorithm. The contributions of this work are twofold: firstly, the proposed FA-ML technique exhibits an exceptional capability to enhance intrusion detection accuracy within the WSN-IoT landscape. Secondly, the amalgamation of the Firefly Algorithm and machine learning introduces a novel dimension to the domain of security-oriented optimization techniques. The implications of this research resonate across various sectors, ranging from critical infrastructure protection to industrial automation and beyond, where safeguarding the integrity of interconnected systems are of paramount importance. The amalgamation of cutting-edge machine learning and bio-inspired algorithms marks a pivotal step forward in crafting robust and intelligent security measures for the evolving landscape of IoT-driven technologies. For intrusion detection in the WSN-IoT, the FA-ML method employs a support vector machine (SVM) machine model for classification with parameter tuning accomplished using a Grey Wolf Optimizer (GWO) algorithm. The experimental evaluation is simulated using NSL-KDD Dataset, revealing the remarkable enhancement of the FA-ML technique, achieving a maximum accuracy of 99.34%. In comparison, the KNN-PSO and XGBoost models achieved lower accuracies of 96.42% and 95.36%, respectively. The findings validate the potential of the FA-ML technique as an active security solution for WSN-IoT systems, harnessing the power of machine learning and the Firefly Algorithm to bolster intrusion detection capabilities.

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“…Let S be the state of the variable and

be the state of n input features. To estimate the value of S based on X , the conditional probability of S needs to be calculated by X , and the expression is [ 41 ]

where

and

are constants that are obtained from data.

can be calculated as [ 41 ]

…”

Section: Ai-based Discriminant Algorithmsmentioning

confidence: 99%

Remote Interference Discrimination Testbed Employing AI Ensemble Algorithms for 6G TDD Networks

Zhang

Zhou

et al. 2023

Sensors

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The Internet-of-Things (IoT) massive access is a significant scenario for sixth-generation (6G) communications. However, low-power IoT devices easily suffer from remote interference caused by the atmospheric duct under the 6G time-division duplex (TDD) mode. It causes distant downlink wireless signals to propagate beyond the designed protection distance and interfere with local uplink signals, leading to a large outage probability. In this paper, a remote interference discrimination testbed is originally proposed to detect interference, which supports the comparison of different types of algorithms on the testbed. Specifically, 5,520,000 TDD network-side data collected by real sensors are used to validate the interference discrimination capabilities of nine promising AI algorithms. Moreover, a consistent comparison of the testbed shows that the ensemble algorithm achieves an average accuracy of 12% higher than the single model algorithm.

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An Anomaly Intrusion Detection for High-Density Internet of Things Wireless Communication Network Based Deep Learning Algorithms

Cited by 15 publications

References 46 publications

A Novel Hybrid Feature Selection with Cascaded LSTM: Enhancing Security in IoT Networks

A Novel Hybrid Feature Selection with Cascaded LSTM: Enhancing Security in IoT Networks

Firefly algorithm based WSN-IoT security enhancement with machine learning for intrusion detection

Remote Interference Discrimination Testbed Employing AI Ensemble Algorithms for 6G TDD Networks

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