Intrusion detection in internet of things using improved binary golden jackal optimization algorithm and LSTM

Hanafi, Ainain Nur; Ghaffari, Ali; Rezaei, Hesam; Valipour, Alireza; Arasteh, Bahman

doi:10.1007/s10586-023-04102-x

Cited by 10 publications

(1 citation statement)

References 61 publications

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“…The IoT has become an attractive sector for cyberattacks due to its business growth and financial potential. This is the primary reason for the rapid increase in cyberattacks against IoT devices [7]. Proactive network security defenses are required to protect essential assets and data because IoT attack vectors have the potential to result in successful security breaches [8].…”

Section: Introductionmentioning

confidence: 99%

Logistic Regression Ensemble Classifier for Intrusion Detection System in Internet of Things

Chalichalamala,

Govindan,

Kasarapu

2023

Sensors

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The Internet of Things (IoT) is a powerful technology that connect its users worldwide with everyday objects without any human interference. On the contrary, the utilization of IoT infrastructure in different fields such as smart homes, healthcare and transportation also raises potential risks of attacks and anomalies caused through node security breaches. Therefore, an Intrusion Detection System (IDS) must be developed to largely scale up the security of IoT technologies. This paper proposes a Logistic Regression based Ensemble Classifier (LREC) for effective IDS implementation. The LREC combines AdaBoost and Random Forest (RF) to develop an effective classifier using the iterative ensemble approach. The issue of data imbalance is avoided by using the adaptive synthetic sampling (ADASYN) approach. Further, inappropriate features are eliminated using recursive feature elimination (RFE). There are two different datasets, namely BoT-IoT and TON-IoT, for analyzing the proposed RFE-LREC method. The RFE-LREC is analyzed on the basis of accuracy, recall, precision, F1-score, false alarm rate (FAR), receiver operating characteristic (ROC) curve, true negative rate (TNR) and Matthews correlation coefficient (MCC). The existing researches, namely NetFlow-based feature set, TL-IDS and LSTM, are used to compare with the RFE-LREC. The classification accuracy of RFE-LREC for the BoT-IoT dataset is 99.99%, which is higher when compared to those of TL-IDS and LSTM.

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Section: Introductionmentioning

confidence: 99%

Logistic Regression Ensemble Classifier for Intrusion Detection System in Internet of Things

Chalichalamala,

Govindan,

Kasarapu

2023

Sensors

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Optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in Internet of things

Otokwala,

Petrovski,

Kalutarage

2024

Int. J. Inf. Secur.

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Embedded systems, including the Internet of things (IoT), play a crucial role in the functioning of critical infrastructure. However, these devices face significant challenges such as memory footprint, technical challenges, privacy concerns, performance trade-offs and vulnerability to cyber-attacks. One approach to address these concerns is minimising computational overhead and adopting lightweight intrusion detection techniques. In this study, we propose a highly efficient model called optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in IoT environments. The proposed OCFSDA model incorporates feature selection, data compression, pruning, and deparameterization. We deployed the model on a Raspberry Pi4 using the TFLite interpreter by leveraging optimisation and inferencing with semi-supervised learning. Using the MQTT-IoT-IDS2020 and CIC-IDS2017 datasets, our experimental results demonstrate a remarkable reduction in the computation cost in terms of time and memory use. Notably, the model achieved an overall average accuracies of 99% and 97%, along with comparable performance on other important metrics such as precision, recall, and F1-score. Moreover, the model accomplished the classification tasks within 0.30 and 0.12 s using only 2KB of memory.

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Uit-DGAdetector: detect domains generated by algorithms using machine learning

Cam,

Man

2024

Cluster Comput

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Intrusion detection in internet of things using improved binary golden jackal optimization algorithm and LSTM

Cited by 10 publications

References 61 publications

Logistic Regression Ensemble Classifier for Intrusion Detection System in Internet of Things

Logistic Regression Ensemble Classifier for Intrusion Detection System in Internet of Things

Optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in Internet of things

Uit-DGAdetector: detect domains generated by algorithms using machine learning

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