Intrusion detection and prevention system for an IoT environment

Kumar, Ajay; Abhishek, Kumar; Ghalib, Muhammad Rukunuddin; Shankar, Achyut; Cheng, Xiaochun

doi:10.1016/j.dcan.2022.05.027

Cited by 47 publications

(8 citation statements)

References 28 publications

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“…Their approach achieves high accuracy, surpassing 85% across multiple classifiers. Kumar et al [ 18 ] presented a distributed IDS, harnessing fog computing [ 19 ] to identify and mitigate Distributed Denial of Service (DDoS) [ 20 ] attacks in blockchain-enabled [ 21 ] IoT networks. Their system comprises three critical engines, facilitating effective attack detection and classification.…”

Section: Related Workmentioning

confidence: 99%

Security at the Edge for Resource-Limited IoT Devices

Canavese,

Mannella,

Regano

et al. 2024

Sensors

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The Internet of Things (IoT) is rapidly growing, with an estimated 14.4 billion active endpoints in 2022 and a forecast of approximately 30 billion connected devices by 2027. This proliferation of IoT devices has come with significant security challenges, including intrinsic security vulnerabilities, limited computing power, and the absence of timely security updates. Attacks leveraging such shortcomings could lead to severe consequences, including data breaches and potential disruptions to critical infrastructures. In response to these challenges, this research paper presents the IoT Proxy, a modular component designed to create a more resilient and secure IoT environment, especially in resource-limited scenarios. The core idea behind the IoT Proxy is to externalize security-related aspects of IoT devices by channeling their traffic through a secure network gateway equipped with different Virtual Network Security Functions (VNSFs). Our solution includes a Virtual Private Network (VPN) terminator and an Intrusion Prevention System (IPS) that uses a machine learning-based technique called oblivious authentication to identify connected devices. The IoT Proxy’s modular, scalable, and externalized security approach creates a more resilient and secure IoT environment, especially for resource-limited IoT devices. The promising experimental results from laboratory testing demonstrate the suitability of IoT Proxy to secure real-world IoT ecosystems.

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Section: Related Workmentioning

confidence: 99%

Security at the Edge for Resource-Limited IoT Devices

Canavese,

Mannella,

Regano

et al. 2024

Sensors

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“…Intrusion detection systems (IDS) are just one component of a comprehensive cybersecurity strategy. Beyond simple detection, the focus has shifted towards intrusion prevention systems (IPS) that not only detect but also take proactive measures to prevent unauthorized access [35,36].…”

Section: Intrusion Detection Systems (Ids)mentioning

confidence: 99%

Ensemble-Based Deep Learning Models for Enhancing IoT Intrusion Detection

Odeh,

Abu Taleb

2023

Applied Sciences

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Cybersecurity finds widespread applications across diverse domains, encompassing intelligent industrial systems, residential environments, personal gadgets, and automobiles. This has spurred groundbreaking advancements while concurrently posing persistent challenges in addressing security concerns tied to IoT devices. IoT intrusion detection involves using sophisticated techniques, including deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and anomaly detection algorithms, to identify unauthorized or malicious activities within IoT ecosystems. These systems continuously monitor and analyze network traffic and device behavior, seeking patterns that deviate from established norms. When anomalies are detected, security measures are triggered to thwart potential threats. IoT intrusion detection is vital for safeguarding data integrity, ensuring users’ privacy, and maintaining critical systems’ reliability and safety. As the IoT landscape evolves, effective intrusion detection mechanisms become increasingly essential to mitigate the ever-growing spectrum of cyber threats. Practical security approaches, notably deep learning-based intrusion detection, have been introduced to tackle these issues. This study utilizes deep learning models, including convolutional neural networks (CNNs), long short-term memory (LSTM), and gated recurrent units (GRUs), while introducing an ensemble deep learning architectural framework that integrates a voting policy within the model’s structure, thereby facilitating the computation and learning of hierarchical patterns. In our analysis, we compared the performance of ensemble deep learning classifiers with traditional deep learning techniques. The standout models were CNN-LSTM and CNN-GRU, achieving impressive accuracies of 99.7% and 99.6%, along with exceptional F1-scores of 0.998 and 0.997, respectively.

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“…The process of determining water quality in rivers is complex, and the identification of polluting wastes in water bodies in rivers is much more difficult when water quality is not collected at all sections of the river and when material resources are limited, so that only non-comprehensive judgements can be made on water quality indicators. On the other hand, considering the interference of wastes in wastewater to water quality monitoring, river water quality will also be affected by the natural environment, making the identification of wastewater more complex than other engineering systems [9]. Therefore, to establish a water quality model with good results, it is necessary to pre-determine the model's identification parameters for water quality indicators through water quality parameter valuation to achieve efficient water quality detection.…”

Section: Research On the Application Of Enoa In The Valuation Of Wate...mentioning

confidence: 99%

Efficient Nonlinear Optimization Algorithm Based on Water Pollution Prevention System

2022

WPPCP

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Due to the emergence of water pollution (WP) problems in many places, these problems have not been completely solved and WP incidents still occur from time to time. Therefore, carrying out WP prevention and control work and solving WP problems have become important topics of discussion and research for relevant scholars. In this paper, an efficient non-linear optimisation algorithm is used to establish a water quality model, which can be used to assess the regional water quality conditions. This paper uses an efficient nonlinear optimization algorithm (ENOA) to establish a water quality model, which can be used to assess the regional water quality conditions. Through the analysis of the industrial and domestic production of sewage discharge in T city, the problems and suggestions of WP control in T city are put forward, hoping to improve the water environment and people's living standard in T city through scientific and reasonable development of WP prevention and control scheme in T city.

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Intrusion detection and prevention system for an IoT environment

Cited by 47 publications

References 28 publications

Security at the Edge for Resource-Limited IoT Devices

Security at the Edge for Resource-Limited IoT Devices

Ensemble-Based Deep Learning Models for Enhancing IoT Intrusion Detection

Efficient Nonlinear Optimization Algorithm Based on Water Pollution Prevention System

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