Comprehensive Assessment of Security Attack Detection Algorithms in Internet of Things

Karande, Jalindar Baban; Joshi, Sarang

doi:10.1109/iccubea.2018.8697406

Cited by 7 publications

(5 citation statements)

References 61 publications

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“…The drawback present in the system is the lack of process validation [24]. If the system is unaware of existing attack probes, the prolonged destruction of attacks corrupts the internal operations and provides a resilient change in the floor [25][26][27]. Bhatti et al discussed various watermarking techniques in image processing, in which a novel structure using Quaternion Fourier Transform (QFT) is being implemented.…”

Section: Related Workmentioning

confidence: 99%

A Cyber-Attack Detection System Using Late Fusion Aggregation Enabled Cyber-Net

Prabha¹,

Kumar²

2023

Intelligent Automation &Amp; Soft Computing

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Today, securing devices connected to the internet is challenging as security threats are generated through various sources. The protection of cyber-physical systems from external attacks is a primary task. The presented method is planned on the prime motive of detecting cybersecurity attacks and their impacted parameters. The proposed architecture employs the LYSIS dataset and formulates Multi Variant Exploratory Data Analysis (MEDA) through Principle Component Analysis (PCA) and Singular Value Decomposition (SVD) for the extraction of unique parameters. The feature mappings are analyzed with Recurrent 2 Convolutional Neural Network (R2CNN) and Gradient Boost Regression (GBR) to identify the maximum correlation. Novel Late Fusion Aggregation enabled with Cyber-Net (LFAEC) is the robust derived algorithm. The quantitative analysis uses predicted threat points with actual threat variables from which mean and difference vectors are evaluated. The performance of the presented system is assessed against the parameters such as Accuracy, Precision, Recall, and F1 Score. The proposed method outperformed by 98% to 100% in all quality measures compared to existing methods.

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Section: Related Workmentioning

confidence: 99%

A Cyber-Attack Detection System Using Late Fusion Aggregation Enabled Cyber-Net

Prabha¹,

Kumar²

2023

Intelligent Automation &Amp; Soft Computing

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“…J.B. Karande and S.A. Joshi [60] conducted their study to evaluate the effectiveness of numbers of attacks detection algorithms used in the IoT and found that these algorithms were effective in the case of independent attacks. Yet, when many attacks occur and cooperate on IoT networks, these algorithms would be less effective in protecting the IoT networks.…”

Section: Ids-based Security Solution In Iotmentioning

confidence: 99%

MADS Based on DL Techniques on the Internet of Things (IoT): Survey

Talal

Zagrouba

2021

Electronics

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Technologically speaking, humanity lives in an age of evolution, prosperity, and great development, as a new generation of the Internet has emerged; it is the Internet of Things (IoT) which controls all aspects of lives, from the different devices of the home to the large industries. Despite the tremendous benefits offered by IoT, still there are some challenges regarding privacy and information security. The traditional techniques used in Malware Anomaly Detection Systems (MADS) could not give us as robust protection as we need in IoT environments. Therefore, it needed to be replaced with Deep Learning (DL) techniques to improve the MADS and provide the intelligence solutions to protect against malware, attacks, and intrusions, in order to preserve the privacy of users and increase their confidence in and dependence on IoT systems. This research presents a comprehensive study on security solutions in IoT applications, Intrusion Detection Systems (IDS), Malware Detection Systems (MDS), and the role of artificial intelligent (AI) in improving security in IoT.

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“…Security attack detection mechanisms and their effectiveness are evaluated in various scenarios like a single attacker, multiple attackers and collaborative attackers in [4]. The attacks on the IoT devices are broadly classified according to target vulnerabilities in a) Device-based b) Network-based and c) Software-based.…”

Section: Literature Reviewmentioning

confidence: 99%

EADA: An Algorithm for Early Detection of Attacks on IoT Resources

2021

IJATCSE

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IoT applications are becoming widespread in monitoring and managing critical infrastructure. Many attacks have been demonstrated in the state-of-the-art on IoT resources. These attacks make use of vulnerabilities present in various connected systems and the Internet of Things (IoT). The state-of-the-art presents many approaches to detect and mitigate such attacks on IoT resources. The early attack detection mechanism is essential to prevent damage to the IoT system and human. This paper presents an algorithm for early detection of attacks on IoT resources through use of predictive descriptor tables. Effectiveness of the proposed algorithm is evaluated through experimental setup built using Google cloud platform. Experimental results show that the proposed algorithm is efficient in the detection of attacks in real-time.

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Comprehensive Assessment of Security Attack Detection Algorithms in Internet of Things

Cited by 7 publications

References 61 publications

A Cyber-Attack Detection System Using Late Fusion Aggregation Enabled Cyber-Net

A Cyber-Attack Detection System Using Late Fusion Aggregation Enabled Cyber-Net

MADS Based on DL Techniques on the Internet of Things (IoT): Survey

EADA: An Algorithm for Early Detection of Attacks on IoT Resources

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