A novel hybrid optimization enabled robust CNN algorithm for an IoT network intrusion detection approach

Bahaa, Ahmed; Sayed, Abdalla; El-Fangary, Laila M.; Fahmy, Hanan

doi:10.1371/journal.pone.0278493

Cited by 10 publications

(6 citation statements)

References 72 publications

(85 reference statements)

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“…The security threats have addressed in [29] by using secure and intelligent framework. New hybrid metaheuristic adaptive particle swarm optimization (PSO) whale optimizer method has introduced in [30] with attaining security performance.…”

Section: Related Workmentioning

confidence: 99%

Enhancing data security in cloud computing: a blockchain-based Feistel cipher encryption and multiclass vector side-channel attack detection approach

2024

IJATEE

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Cloud computing (CC) permits more users for storing as well as distributing their applications and data. CC continuously evolving technology that can improve agility, availability, collaboration, and scalability of data. It offers on-demand solution for different applications namely data storage, servers, databases, networking, and software. One of the most challenging issues in CC is detecting attacks. Parallel sponge-based authenticated encryptionwith sidechannel protection and adversary-invisible nonces (PSASPIN) to sponge construction was designed [1]. Moreover, leveled implementation has also utilized in implementing key generation via pseudo random function (PRF). Next, data preprocessing has utilized to minimize the code size. Finally, security proof has ensured using game theory and therefore processing shorter messages significantly. The authenticated encryption (AE) schemes such as security, performance, and efficiency are enhanced.*Author for correspondence Despite improvement found in terms of communication complexity and throughput were not focused.Side channel assault also utilizes physical data which leaks as of cryptographic tool. For avoiding from these attacks, divide-and-conquer approach is employed. Here, malicious user splits key into subkeys, endeavors for recuperating sub-keys distinctly as well as integrates them to create key. Reinforcement-strategy-based side-channel attack has designed in [2] with divide-and-conquer. Initially, the power leakage models are modified, and accuracy has improved. Next, the recovered sub-keys are integrated during the attack phase through validation therefore mitigating side-channel attacks significantly and improving the successful recovery rate. Though successful recovery rate has ensured but the detection time and accuracy have not focused. The disclosure of the cloud storage in the recent years carries enormous east to person life. Moreover, business establishments as well as organizations transmitted huge data for storage also. However, the data privacy

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Section: Related Workmentioning

confidence: 99%

Enhancing data security in cloud computing: a blockchain-based Feistel cipher encryption and multiclass vector side-channel attack detection approach

2024

IJATEE

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“…[18] Because the logarithmic spiral function of the WOA covers a more ambiguous search space, it helps optimize the selection parameters. [33] In addition, a combination of CNN and WOA has been widely used in many fields. [34,35]…”

Section: Whale Optimization Algorithmmentioning

confidence: 99%

Crown Prediction of Hot‐Rolled Silicon Steel Using Transfer Learning Network Fused with Whale Optimization Algorithm

Meng

Ding

Dong

et al. 2023

steel research int.

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Massive amounts of data are generated during hot strip production in the hot‐rolling metal forming process; the resultant dataset is sufficient for model learning in strip steel crown prediction. However, the data of high‐grade nonoriented silicon steel are limited, and the rolling process parameters differ, resulting in poor crown prediction. Herein, a model based on the whale optimization algorithm and transfer learning to predict the crowns of hot‐rolled high‐grade nonoriented silicon strip steel is presented. The model is composed of convolutional and linear layers. The whale optimization algorithm is used to optimize the hyperparameters and obtain an optimal model during pretraining. The model is then fine tuned based on insufficient silicon steel data to achieve model migration. The application results show that the correlation coefficient reaches 0.993, which is the highest prediction accuracy among the comparison models. Furthermore, the root mean square error is 1.14 μm, and the hit rate within 4.0 μm of the crown deviation reaches 99.502%. In addition, the influences of four parameters on the crown of the silicon steel strip are studied based on the response surfaces. The results indicate that the proposed model can efficiently predict silicon steel strip crowns.

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“…As a result, IoT devices are said to be disturbed and denied their corresponding service due to these types of attacks. In Bahaa et al, 16 a novel Adaptive Particle Swarm Optimization Whale Optimizer Algorithm (APSOWOA) was proposed with the objective of optimizing the hyperparameters.…”

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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A novel hybrid optimization enabled robust CNN algorithm for an IoT network intrusion detection approach

Cited by 10 publications

References 72 publications

Enhancing data security in cloud computing: a blockchain-based Feistel cipher encryption and multiclass vector side-channel attack detection approach

Enhancing data security in cloud computing: a blockchain-based Feistel cipher encryption and multiclass vector side-channel attack detection approach

Crown Prediction of Hot‐Rolled Silicon Steel Using Transfer Learning Network Fused with Whale Optimization Algorithm

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

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