Retracted: Neural Network based Intrusion Detection system for critical infrastructure

Gehlot, Anita; Joshi, Ankita

doi:10.1109/mysurucon55714.2022.9972524

Cited by 2 publications

(2 citation statements)

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“…Here, the attacker first performs reconnaissance of different IoT-based devices and their respective network interfaces to discover open vulnerabilities; then, it exploits the vulnerabilities and performs attacks, such as data manipulation, distributed denial-of-service (DoS), session hijacking, and malware, to manipulate the behavior of critical infrastructures. To overcome these issues, many researchers have proposed security-based solutions such as incident response, threat intelligence [5], artificial intelligence (AI) [6], blockchain [7], etc. They perform vulnerability assessments that identify the unpatched devices and introduce multi-factor authentication approaches to enhance network security and reduce the attack surface.…”

Section: Introductionmentioning

confidence: 99%

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AI and Blockchain-Based Secure Data Dissemination Architecture for IoT-Enabled Critical Infrastructure

Rathod,

Jadav,

Tanwar

et al. 2023

Sensors

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The Internet of Things (IoT) is the most abundant technology in the fields of manufacturing, automation, transportation, robotics, and agriculture, utilizing the IoT’s sensors-sensing capability. It plays a vital role in digital transformation and smart revolutions in critical infrastructure environments. However, handling heterogeneous data from different IoT devices is challenging from the perspective of security and privacy issues. The attacker targets the sensor communication between two IoT devices to jeopardize the regular operations of IoT-based critical infrastructure. In this paper, we propose an artificial intelligence (AI) and blockchain-driven secure data dissemination architecture to deal with critical infrastructure security and privacy issues. First, we reduced dimensionality using principal component analysis (PCA) and explainable AI (XAI) approaches. Furthermore, we applied different AI classifiers such as random forest (RF), decision tree (DT), support vector machine (SVM), perceptron, and Gaussian Naive Bayes (GaussianNB) that classify the data, i.e., malicious or non-malicious. Furthermore, we employ an interplanetary file system (IPFS)-driven blockchain network that offers security to the non-malicious data. In addition, to strengthen the security of AI classifiers, we analyze data poisoning attacks on the dataset that manipulate sensitive data and mislead the classifier, resulting in inaccurate results from the classifiers. To overcome this issue, we provide an anomaly detection approach that identifies malicious instances and removes the poisoned data from the dataset. The proposed architecture is evaluated using performance evaluation metrics such as accuracy, precision, recall, F1 score, and receiver operating characteristic curve (ROC curve). The findings show that the RF classifier transcends other AI classifiers in terms of accuracy, i.e., 98.46%.

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Section: Introductionmentioning

confidence: 99%

“…Most of the researchers in this domain use AI-enabled solutions for IoT-based critical infrastructure [5,10,13]. Their proposed AI solutions for security threat detection in IoT-based critical infrastructure are not resistant to data poisoning attacks, i.e., the dataset is itself corrupted or has been tampered with by the attackers.…”

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confidence: 99%