A smart anomaly-based intrusion detection system for the Internet of Things (IoT) network using GWO–PSO–RF model

Keserwani, Pankaj Kumar; Govil, Mahesh Chandra; Pilli, Emmanuel S.; Govil, Prajjval

doi:10.1007/s40860-020-00126-x

Cited by 67 publications

(24 citation statements)

References 45 publications

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“…Here, n s and n g linearly decrease in relation to an increase in the number of iterations, which can be seen in ( 8) and (10), respectively. Meanwhile, n p linearly increases alongside the rising number of iterations, which can be seen in (9).…”

Section: The Proposed Modelmentioning

confidence: 94%

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Enhanced Anomaly Detection System for IoT Based on Improved Dynamic SBPSO

Sarwar

Alnajim

Marwat

et al. 2022

Sensors

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The Internet of Things (IoT) supports human endeavors by creating smart environments. Although the IoT has enabled many human comforts and enhanced business opportunities, it has also opened the door to intruders or attackers who can exploit the technology, either through attacks or by eluding it. Hence, security and privacy are the key concerns for IoT networks. To date, numerous intrusion detection systems (IDS) have been designed for IoT networks, using various optimization techniques. However, with the increase in data dimensionality, the search space has expanded dramatically, thereby posing significant challenges to optimization methods, including particle swarm optimization (PSO). In light of these challenges, this paper proposes a method called improved dynamic sticky binary particle swarm optimization (IDSBPSO) for feature selection, introducing a dynamic search space reduction strategy and a number of dynamic parameters to enhance the searchability of sticky binary particle swarm optimization (SBPSO). Through this approach, an IDS was designed to detect malicious data traffic in IoT networks. The proposed model was evaluated using two IoT network datasets: IoTID20 and UNSW-NB15. It was observed that in most cases, IDSBPSO obtained either higher or similar accuracy even with less number of features. Moreover, IDSBPSO substantially reduced computational cost and prediction time, compared with conventional PSO-based feature selection methods.

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Section: The Proposed Modelmentioning

confidence: 94%

“…Moreover, employing all features in the design of an IDS can lead to the introduction of redundant and irrelevant features into the model. Therefore, feature optimization must be used to achieve good IDS performance [9]. There are three main approaches to feature optimization.…”

Section: (D)mentioning

confidence: 99%

Enhanced Anomaly Detection System for IoT Based on Improved Dynamic SBPSO

Sarwar

Alnajim

Marwat

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…(2021) presented a detection system that is able to autonomously adjust the decision function of its underlying anomaly classification models to a smart home's changing condition. Another intrusion detection system was suggested by Keserwani et al (2021), which combined Grey Wolf Optimization and Particle Swam Optimization to identify various attacks for IoT networks. They used the KDD Cup 99, NSL-KDD and CICIDS-2017 to evaluate their model.…”

Section: Intrusion Detection Systems With a Focus On Iotmentioning

confidence: 99%

Cyber risk and cybersecurity: a systematic review of data availability

Cremer

Sheehan

Fortmann

et al. 2022

Geneva Pap Risk Insur Issues Pract

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Cybercrime is estimated to have cost the global economy just under USD 1 trillion in 2020, indicating an increase of more than 50% since 2018. With the average cyber insurance claim rising from USD 145,000 in 2019 to USD 359,000 in 2020, there is a growing necessity for better cyber information sources, standardised databases, mandatory reporting and public awareness. This research analyses the extant academic and industry literature on cybersecurity and cyber risk management with a particular focus on data availability. From a preliminary search resulting in 5219 cyber peer-reviewed studies, the application of the systematic methodology resulted in 79 unique datasets. We posit that the lack of available data on cyber risk poses a serious problem for stakeholders seeking to tackle this issue. In particular, we identify a lacuna in open databases that undermine collective endeavours to better manage this set of risks. The resulting data evaluation and categorisation will support cybersecurity researchers and the insurance industry in their efforts to comprehend, metricise and manage cyber risks.

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“…They used the IoTID20 dataset for the evaluation of these DL models. Keserwani et al [ 23 ] presented a method for extracting significant IoT network features for intrusion detection. The proposed method consists of a combination of grey wolf optimization (GWO) and particle swarm optimization (PSO).…”

Section: Related Workmentioning

confidence: 99%

A New Intrusion Detection System for the Internet of Things via Deep Convolutional Neural Network and Feature Engineering

Ullah

Ahmad

Khan

et al. 2022

Sensors

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The Internet of Things (IoT) is a widely used technology in automated network systems across the world. The impact of the IoT on different industries has occurred in recent years. Many IoT nodes collect, store, and process personal data, which is an ideal target for attackers. Several researchers have worked on this problem and have presented many intrusion detection systems (IDSs). The existing system has difficulties in improving performance and identifying subcategories of cyberattacks. This paper proposes a deep-convolutional-neural-network (DCNN)-based IDS. A DCNN consists of two convolutional layers and three fully connected dense layers. The proposed model aims to improve performance and reduce computational power. Experiments were conducted utilizing the IoTID20 dataset. The performance analysis of the proposed model was carried out with several metrics, such as accuracy, precision, recall, and F1-score. A number of optimization techniques were applied to the proposed model in which Adam, AdaMax, and Nadam performance was optimum. In addition, the proposed model was compared with various advanced deep learning (DL) and traditional machine learning (ML) techniques. All experimental analysis indicates that the accuracy of the proposed approach is high and more robust than existing DL-based algorithms.

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A smart anomaly-based intrusion detection system for the Internet of Things (IoT) network using GWO–PSO–RF model

Cited by 67 publications

References 45 publications

Enhanced Anomaly Detection System for IoT Based on Improved Dynamic SBPSO

Enhanced Anomaly Detection System for IoT Based on Improved Dynamic SBPSO

Cyber risk and cybersecurity: a systematic review of data availability

A New Intrusion Detection System for the Internet of Things via Deep Convolutional Neural Network and Feature Engineering

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