Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey

Jayalaxmi, P.L.S.; Saha, Rahul; Kumar, Gulshan; Conti, Mauro; Kim, Tai-hoon

doi:10.1109/access.2022.3220622

Cited by 33 publications

(10 citation statements)

References 107 publications

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“…An ensemble of the algorithm was designed in Sharma and Bathla 37 using a voting classifier to detect DDOS attacks. For determining intrusions, ML and DL mechanisms were introduced in Jayalaxmi et al 38 DNN was analyzed in Vinayakumar et al 39 for efficient intrusion detection. ML algorithms were explained in Esmaeili et al 40 to diagnose DDoS attacks in IoT.…”

Section: Related Workmentioning

confidence: 99%

Artificial intelligence enabled Luong Attention and Hosmer Lemeshow Regression Window‐based attack detection in 6G

Bhuvaneshwari¹,

Balusamy

Dhanaraj

et al. 2023

Int J Communication

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SummaryRegardless of the developments of networking and communication technologies, security is without exception a predominant feature to ensure network reliability. The future sixth‐generation (6G) network is anticipated to be carried out with artificial intelligence (AI) powered communication via machine learning (ML), post‐quantum cryptography, and so on. AI‐powered communication has been in recent years utilized in enhancing network traffic performance with respect to resource management, optimal frequency spectrum design, security, and latency. The studies of modern wireless communications and anticipated features of 6G networks revealed a prerequisite for designing a trustworthy attack detection mechanism. In this work, a method called, Luong Attention and Hosmer Lemeshow Regression Window‐based (LA‐HLRW) attack detection in 6G is proposed. Initially, with the raw Botnet Attack dataset obtained as input, preprocessing is performed to normalize network traffic features. Next, the dimensionality of network traffic feature of large‐scale network traffic data is reduced using the Luong Attention integrated with Long Short Term Memory (LSTM)‐based Feature extraction model. Finally, with the objective of classifying network traffic samples for attack detection in 6G, we analyze the low dimensional network traffic feature set produced by Luong Attention integrated with LSTM using the Hosmer Lemeshow Logistic Regression Window‐based Attack Detection model. Extensive experiments are performed with the Botnet Attack dataset to validate the efficiency of the proposed LA‐HLRW method by using different parameters such as attack detection accuracy, attack detection time, precision, and recall. The overall analysis of proposed LA‐HLRW results significantly reduced the attack detection time by 24%, and additionally improved attack detection accuracy, precision, and recall by 5%, 5%, and 6% as compared to existing attack detection methods respectively.

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Section: Related Workmentioning

confidence: 99%

Artificial intelligence enabled Luong Attention and Hosmer Lemeshow Regression Window‐based attack detection in 6G

Bhuvaneshwari¹,

Balusamy

Dhanaraj

et al. 2023

Int J Communication

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“…There are 41 characteristics in total, 38 of which are numeric, and 3 of which are not (protocol type, service type, and fag). There are also fundamental traffic features (23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41), content features (11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22), features (1-10), and a class label for each item.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…For small amounts of data, machine learning algorithms work well enough. However, the network generates a large quantity of data in real time, and current machine-learning algorithms are unable to learn such a vast amount of data [22]. Additionally, data is subjected to minimal training and utilized as a benchmark.…”

Section: Introductionmentioning

confidence: 99%

A Double-Layer Indemnity Enhancement Using LSTM and HASH Function Technique for Intrusion Detection System

Ali,

Alqurashi,

Alsolami

et al. 2023

Mathematics

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The Intrusion Detection System (IDS) is the most widely used network security mechanism for distinguishing between normal and malicious traffic network activities. It aids network security in that it may identify unforeseen hazards in network traffic. Several techniques have been put forth by different researchers for network intrusion detection. However, because network attacks have increased dramatically, making it difficult to execute precise detection rates quickly, the demand for effectively recognizing network incursion is growing. This research proposed an improved solution that uses Long Short-Term Memory (LSTM) and hash functions to construct a revolutionary double-layer security solution for IoT Network Intrusion Detection. The presented framework utilizes standard and well-known real-time IDS datasets such as KDDCUP99 and UNSWNB-15. In the presented framework, the dataset was pre-processed, and it employed the Shuffle Shepherd Optimization (SSO) algorithm for tracking the most informative attributes from the filtered database. Further, the designed model used the LSTM algorithm for classifying the normal and malicious network traffic precisely. Finally, a secure hash function SHA3-256 was utilized for countering the attacks. The intensive experimental assessment of the presented approach with the conventional algorithms emphasized the efficiency of the proposed framework in terms of accuracy, precision, recall, etc. The analysis showed that the presented model attained attack prediction accuracy of 99.92% and 99.91% for KDDCUP99 and UNSWNB-15, respectively.

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“…In urban management, IoT can be used for traffic control, environmental monitoring, and public safety applications. In addition, IoT has various applications in healthcare, agriculture, and retail [4].…”

Section: Introductionmentioning

confidence: 99%

A Survey of Deep Learning Technologies for Intrusion Detection in Internet of Things

Liao,

Murah,

Hasan

et al. 2024

IEEE Access

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Internet of Things (IoT) is transforming how we live and work and its applications are widespread, spanning smart homes, industrial monitoring, smart cities, healthcare, agriculture, and retail Considering its wide range applications, it is vital to address the security challenges arising from the massive collection and transmission of user data by IoT devices. Intrusion detection systems (IDS) based on deep learning techniques offer new means and research directions for resolving IoT security issues. Deep learning models can process large volumes of data and extract complex patterns, making them generally more effective than traditional rule based IDSs. While deep learning techniques are gradually gaining popularity in IDS applications, current research lacks a comprehensive summary of deep learning-based IDS in the context of IoT. This paper provides an introduction to intrusion detection technologies, followed by a detailed comparison, analysis, and discussion of deep learning models, datasets, feature extraction and classifiers, data preprocessing techniques, and experimental design of the models. It also highlights the current challenges and issues associated with deep learning models and relevant techniques for IDS. Finally, it concludes by providing recommendations to assist researchers in this domain.

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Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey

Cited by 33 publications

References 107 publications

Artificial intelligence enabled Luong Attention and Hosmer Lemeshow Regression Window‐based attack detection in 6G

Artificial intelligence enabled Luong Attention and Hosmer Lemeshow Regression Window‐based attack detection in 6G

A Double-Layer Indemnity Enhancement Using LSTM and HASH Function Technique for Intrusion Detection System

A Survey of Deep Learning Technologies for Intrusion Detection in Internet of Things

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