Intrusion Detection System for IoT Based on Deep Learning and Modified Reptile Search Algorithm

Dahou, Abdelghani; Elaziz, Mohamed Abd; Chelloug, Samia Allaoua; Awadallah, Mohammed A.; Al-Betar, Mohammed Azmi; Al-qaness, Mohammed A. A.; Forestiero, Agostino

doi:10.1155/2022/6473507

Cited by 82 publications

(50 citation statements)

References 58 publications

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“…Machine and deep learning approaches are widely used in different areas of life. The author proposed an intrusion detection system for IoT based on deep learning and a modified reptile search algorithm in [ 56 ] and a modified Aquila optimizer for forecasting oil production in [ 57 ]. In [ 58 ], author forecasted the wind power using the marine predator algorithm and mutation operators for wind power forecasting to evaluate the performance of meta heuristic approach.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Deep Learning Model for Software Cost Estimation Using Hybrid Meta-Heuristic Algorithmic Approach

Hassan

Khan

Irfan

et al. 2022

Computational Intelligence and Neuroscience

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Effective software cost estimation significantly contributes to decision-making. The rising trend of using nature-inspired meta-heuristic algorithms has been seen in software cost estimation problems. The constructive cost model (COCOMO) method is a well-known regression-based algorithmic technique for estimating software costs. The limitation of the COCOMO models is that the values of these coefficients are constant for similar kinds of projects whereas, in reality, these parameters vary from one organization to another organization. Therefore, for accurate estimation, it is necessary to fine-tune the coefficients. The research community is now examining deep learning (DL) as a forward-looking solution to improve cost estimation. Although deep learning architectures provide some improvements over existing flat technologies, they also have some shortcomings, such as large training delays, over-fitting, and under-fitting. Deep learning models usually require fine-tuning to a large number of parameters. The meta-heuristic algorithm supports finding a good optimal solution at a reasonable computational cost. Additionally, heuristic approaches allow for the location of an optimum solution. So, it can be used with deep neural networks to minimize training delays. The hybrid of ant colony optimization with BAT (HACO-BA) algorithm is a hybrid optimization technique that combines the most common global optimum search technique for ant colonies (ACO) in association with one of the newest search techniques called the BAT algorithm (BA). This technology supports the solution of multivariable problems and has been applied to the optimization of a large number of engineering problems. This work will perform a two-fold assessment of algorithms: (i) comparing the efficacy of ACO, BA, and HACO-BA in optimizing COCOMO II coefficients; and (ii) using HACO-BA algorithms to optimize and improve the deep learning training process. The experimental results show that the hybrid HACO-BA performs better as compared to ACO and BA for tuning COCOMO II. HACO-BA also performs better in the optimization of DNN in terms of execution time and accuracy. The process is executed upto 100 epochs, and the accuracy achieved by the proposed DNN approach is almost 98% while NN achieved accuracy of up to 85% on the same datasets.

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Section: Related Workmentioning

confidence: 99%

Optimizing Deep Learning Model for Software Cost Estimation Using Hybrid Meta-Heuristic Algorithmic Approach

Hassan

Khan

Irfan

et al. 2022

Computational Intelligence and Neuroscience

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“…Classification of IoT intrusive traffic is done using random forest (RF). Dahou et al ( 2022 ) employed a reptile search algorithm for selecting optimal features in IoT framework. The proposed work is evaluated against multiple datasets such as NSL-KDD, BoT-IoT, KDDCup-99, and CICIDS-2017.…”

Section: Introductionmentioning

confidence: 99%

Hybridized bio-inspired intrusion detection system for Internet of Things

Singh

Ujjwal

2023

Front. Big Data

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The Internet of Things (IoT) consists of several smart devices equipped with computing, sensing, and network capabilities, which enable them to collect and exchange heterogeneous data wirelessly. The increasing usage of IoT devices in daily activities increases the security needs of IoT systems. These IoT devices are an easy target for intruders to perform malicious activities and make the underlying network corrupt. Hence, this paper proposes a hybridized bio-inspired-based intrusion detection system (IDS) for the IoT framework. The hybridized sine-cosine algorithm (SCA) and salp swarm algorithm (SSA) determines the essential features of the network traffic. Selected features are passed to a machine learning (ML) classifier for the detection and classification of intrusive traffic. The IoT network intrusion dataset determines the performance of the proposed system in a python environment. The proposed hybridized system achieves maximum accuracy of 84.75% with minimum selected features i.e., 8 and takes minimum time of 96.42 s in detecting intrusion for the IoT network. The proposed system's effectiveness is shown by comparing it with other similar approaches for performing multiclass classification.

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“…The authors in [20] combined RSA with Remora Optimization Algorithm (ROA) for data clustering. In another work [21], RSA is combined with deep learning for ID. In [22], chaotic-map, and simulated annealing are used to improve RSA for FS in Medical field.…”

Section: Introductionmentioning

confidence: 99%

An Improved Reptile Search Algorithm Based on Cauchy Mutation for Intrusion Detection

Duraibi¹

2023

Computer Systems Science and Engineering

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With the growth of the discipline of digital communication, the topic has acquired more attention in the cybersecurity medium. The Intrusion Detection (ID) system monitors network traffic to detect malicious activities. The paper introduces a novel Feature Selection (FS) approach for ID. Reptile Search Algorithm (RSA)-is a new optimization algorithm; in this method, each agent searches a new region according to the position of the host, which makes the algorithm suffers from getting stuck in local optima and a slow convergence rate. To overcome these problems, this study introduces an improved RSA approach by integrating Cauchy Mutation (CM) into the RSA's structure. Thus, the CM can effectively expand search space and enhance the performance of the RSA. The developed RSA-CM is assessed on five publicly available ID datasets: KDD-CUP99, NSL-KDD, UNSW-NB15, CIC-IDS2017, and CIC-IDS2018 and two engineering problems. The RSA-CM is compared with the original RSA, and three other state-of-theart FS methods, namely particle swarm optimization, grey wolf optimization, and multi-verse optimizer, and quantitatively is evaluated using fitness value, the number of selected optimum features, accuracy, precision, recall, and F1score evaluation measures. The results reveal that the developed RSA-CM got better results than the other competitive methods applied for FS on the ID datasets and the examined engineering problems. Moreover, the Friedman test results confirm that RSA-CM has a significant superiority compared to other methods as an FS method for ID.

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Intrusion Detection System for IoT Based on Deep Learning and Modified Reptile Search Algorithm

Cited by 82 publications

References 58 publications

Optimizing Deep Learning Model for Software Cost Estimation Using Hybrid Meta-Heuristic Algorithmic Approach

Optimizing Deep Learning Model for Software Cost Estimation Using Hybrid Meta-Heuristic Algorithmic Approach

Hybridized bio-inspired intrusion detection system for Internet of Things

An Improved Reptile Search Algorithm Based on Cauchy Mutation for Intrusion Detection

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