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A hybrid ant lion optimization algorithm based lightweight deep learning framework for cyber attack detection in IoT environment

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 122卷

作者： Gupta, Brij B. Gaurav, Akshat Attar, Razaz Waheeb Arya, Varsha Bansal, Shavi Alhomoud, Ahmed Chui, Kwok Tai Asia Univ Dept Comp Sci & Informat Engn Taichung Taiwan Kyung Hee Univ 26 Kyungheedae Ro Seoul 02447 South Korea Symbiosis Int Univ Symbiosis Ctr Informat Technol SCIT Pune India Univ Econ & Human Sci Warsaw Poland Ronin Inst Montclair NJ USA Asia Univ Int Ctr AI & Cyber Secur Res & Innovat CCRI Taichung 413 Taiwan Princess Nourah bint Abdulrahman Univ Management Dept Coll Business Adm POB 84428 Riyadh 11671 Saudi Arabia Lebanese Amer Univ Dept Elect & Comp Engn Beirut 1102 Lebanon Univ Petr & Energy Studies UPES Ctr Interdisciplinary Res Misraspatti 248007 Uttaranchal India Insights2Techinfo Chandigarh India Chandigarh Univ UCRD Chandigarh India Northern Border Univ Coll Sci Dept Comp Sci Ar Ar Saudi Arabia Hong Kong Metropolitan Univ HKMU Hong Kong Peoples R China

Internet of Things (IoTs) are integral part of Web3, in which they are used for information collecting and sharing. However, the limited storage capacity of IoT decides made them vulnerable to many types of cyber attacks. In this context, we proposed a hybrid deep learning model for the detection of cyber attacks in the IoT environment. The proposed approach used features selection technique for the selection of efficient features and ant lion optimization algorithm for tuning the hyper-parameters. This hybrid approach model train for five epochs and detects the attack traffic with an accuracy of 97%, which makes it efficient and lightweight for IoT applications. The proposed model is also outperformed the standard machine learning and deep learning models.

关键词： IoT DoS attack Probe attack CNN ant lion optimization algorithm

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Secured wireless sensor networks using hybrid Rivest Shamir Adleman with ant lion optimization algorithm

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WIRELESS NETWORKS 2024年第6期30卷 5977-5995页

作者： Almuzaini, Khalid K. Dubey, Rachana Gandhi, Charu Taram, Manish Soni, Anita Sharma, Seema Sanchez-Chero, Manuel Carrion-Barco, Gilberto King Abdulaziz City Sci & Technol KACST Cyber Secur Inst Riyadh 11442 Saudi Arabia Pandit S N Shukla Univ PTSNS Dept Comp Sci Shahdol Madhya Pradesh India Jaypee Inst Informat Technol Dept CSE & IT Noida Uttar Pradesh India IES Univ Dept Comp Sci Engn Bhopal Madhya Pradesh India Manav Rachna Int Inst Res & Studies Faridabad 121004 Haryana India Univ Nacl Frontera Fac Ingn Ind Alimentarias & Biotecnol Sullana Peru Univ Nacl Pedro Ruiz Gallo Dept Academ Comp & Elect Lambayeque Peru

The concept of security is quite broad and touches the lives of one million people on a daily basis. Those individuals who depend on networks for activities such as banking, shopping, and filing their tax returns may soon face a potentially significant challenge in the form of network security, which will need to be handled in the not-too-distant future. The solution that is being recommended is a combination of two algorithms: The Hybrid Rivest-Shamir-Adleman (RSA) algorithm and the ant lion optimization algorithm. There have been two noteworthy developments in the requirements for information security inside a wireless sensor network over the course of the last several decades. First, prior to the broad use of data processing equipment, the security of information that was regarded useful to a wireless sensor network was largely supplied by physical and administrative precautions. This was the case even after the widespread use of data processing equipment. Hash functions are used by these designs whenever Rebalanced RSA is used to encrypt a message. This helps to guarantee that the integrity of the message is not compromised in any way. It has been found out that this novel method is susceptible to attacks based on selected ciphertext as well as adaptive attacks based on chosen ciphertext. A second strategy, which entails converting the ciphertext into binary format, is also one of the options that are being investigated. The binary format is further compressed and coded, which makes it resistant to a variety of attacks, including adaptive chosen ciphertext assaults, selected ciphertext assaults, and other assaults. To get started, a thorough study project on mapping is carried out in order to establish the mapping of assaults and counterattacks that would be most effective. A brand-new index that goes by the name "Threat Severity Index" (TSI) has been developed with the intention of determining how secure each individual system is on the whole. In addition, the

关键词： ant lion optimization algorithm Hybrid Rivest-Shamir-Adleman algorithm Wireless sensor network Encryption

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Hybrid Grey Wolf Optimizer with ant lion optimization algorithm based Deep Learning for Face Expression Recognition 2

Hybrid Grey Wolf Optimizer with Ant Lion Optimization Algori...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Jyoti Vishwanath, P. Alzubaidi, Laith H. Devika Rajesh, N. Raichur Visvesvaraya Technological University H.K.E Society's S.M.V College of Engineering Department of Ece Belagavi India H.K.E. Society's S.M.V. College of Engineering Department of Ece Karnataka Raichur India The Islamic University Najaf Iraq Kumarguru College of Liberal Arts and Science Department of Visual Communication Coimbatore India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350317558

Facial expressions are an important part of recognizing human emotional messages, hence it has been a focus of pattern recognition research. However, developments in convolutional neural networks and network topologies have resulted in difficulties such as decreased overall performance and the potential loss of crucial feature information in these methods. To overcome this issue, the hybrid optimization algorithm of Grey Wolf optimization algorithm (GWO) based ant lion optimization algorithm (ALO) termed as GWALO is proposed with deep learning for recognizing facial expressions. Viola Jones algorithm and Gabor Filtering are used in preprocessing to improve image quality by minimizing redundant distortion, allowing for the extraction of relevant information. The Affine-Scale-Invariant Feature Transform is used in feature extraction to extract expressions from a preprocessed image. The hybrid GWALO is utilized in the feature selection for selecting the better features. Recurrent Neural Network (RNN) of Deep Learning (DL) is utilized in classification for classifying the various facial expressions (happiness, joy, sorrow, angry, sad and so on). The obtained result shows that the proposed GWALO algorithm attains accuracy of 99.7% precision of 98.5%, recall of 97.8%, specificity of 99.6% and f1score of 96.7% that is better when contrasted to existing optimization algorithms. © 2024 IEEE.

关键词： ant lion optimization algorithm Facial Expression Grey Wolf optimization algorithm

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Coverage Enhancement Strategy for WSNs Based on Virtual Force-Directed ant lion optimization algorithm

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IEEE SENSORS JOURNAL 2021年第17期21卷 19611-19622页

作者： Yao, Yindi Li, Ying Xie, Dangyuan Hu, Shanshan Wang, Chen Li, Yangli Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian 710121 Shaanxi Peoples R China Xian Univ Posts & Telecommun Key Lab Informat Commun Network & Secur Xian 710121 Shaanxi Peoples R China

When deploying wireless sensor networks in complex monitoring areas such as battlefields and disaster areas, sensor nodes usually form an initial deployment by airdropping. This random deployment method causes the nodes to deviate from the optimal deployment position and the phenomenon of coverage holes appears. This paper proposes a coverage enhancement strategy for WSNs based on the virtual force-directed ant lion optimization algorithm (VF-IALO). First, based on the original ant lion optimization algorithm, we re-assign antlions and dynamically reduce the number of antlions. The strategy of continuous ant random walk boundary shrinkage factor is combined. Secondly, we limit the range of ants' random walk to reduce the moving distance of the sensor node during the secondary deployment process. Finally, we introduce the virtual force composed of neighbor nodes force, grid point gravity, and boundary repulsion. The weight coefficients of the virtual force, antlion, and elite antlion dynamically changed to update the ant position. It can avoid the algorithm fall into the local optimal solution, accelerate the algorithm convergence speed and improve the global optimization ability. The simulation results show that when 30 sensors are deployed in a monitoring area of 60m x 60m, compared with the VFA, ALO, and VFPSO algorithms, the coverage rate of the VF-IALO algorithm is increased by 7.656%, 11.048%, and 4.088%, the average moving distance of the nodes is reduced by 0.4759m, 2.3387m, and 3.3762m respectively. More importantly, when the network scale (region size and number of nodes) changes, the VF-IALO algorithm still maintains a clear performance advantage.

关键词： ant lion optimization algorithm coverage rate moving distance virtual force algorithm wireless sensor networks (WSNs)

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Estimation algorithm for vehicle state estimation using ant lion optimization algorithm

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ADVANCES IN MECHANICAL ENGINEERING 2022年第3期14卷

作者： Liu, Yingjie Cui, Dawei Weifang Univ Sch Mech Elect & Vehicle Engn Dong Feng Dong St 5147 Weifang 261061 Shandong Peoples R China

In order to solve the problem of vehicle state estimation, an unscented Kalman filter state parameter estimator based on the ant lion algorithm is proposed. Aiming at the uncertainty of the noise covariance matrix in the unscented Kalman filter (UKF) process, the ant lion optimization algorithm (ALO) is used to optimize it. Based on the purpose, a 3-DOF nonlinear vehicle estimation model with Magic formula tire model was established firstly. Then the slalom road operating condition was simulated. The simulation results show that the estimated values of the key state variables are in better agreement with the virtual test values indicating the proposed algorithm having a good estimation performance. And also, compared with the estimation results of the UKF algorithm, the maximum error and the root mean square error of the estimation algorithm proposed in this paper are both smaller than the estimated value of the UKF algorithm. The results of a real-vehicle experiment demonstrate that the proposed method can be used effectively and accurately for solving the vehicle-state estimation problem. The study can provide precise status information for vehicle stability control under extreme conditions.

关键词： Automotive engineering unscented Kalman filter vehicle state estimation ant lion optimization algorithm

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Modified ant lion optimization algorithm for Improved Diagnosis of Thyroid Disease

Modified Ant Lion Optimization Algorithm for Improved Diagno...

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International Conference on Cognitive Informatics and Soft Computing (CISC)

作者： Gupta, Naman Jain, Rishabh Gupta, Deepak Khanna, Ashish Khamparia, Aditya Guru Gobind Singh Indraprastha Univ Maharaja Agrasen Inst Technol Delhi India Lovely Profess Univ Sch Comp Sci & Engn Phagwara Punjab India

ISBN: (纸本)9789811514517;9789811514500

Thyroid is one of the most common diseases affecting millions of individuals across the world. According to the findings from numerous studies and surveys on thyroid disease, it is estimated that about 42 million people in India and around 20 million people in America are suffering from some form of thyroid diseases, and women make up the majority of thyroid patients among them. It is caused due to the under (Hypothyroidism) or over (Hyperthyroidism) functionality of thyroid gland, which is responsible for maintaining the metabolism of the body, and it is imperative to diagnose its effects as early as possible so that a possible cure or treatment can be performed at the earliest. This paper aims to propose a modified ant lion optimization algorithm (MALO) for improving the diagnostic accuracy of thyroid disease. The proposed MALO is employed as a feature selection method to identify the most significant set of attributes from a large pool of available attributes to improve the classification accuracy and to reduce the computational time. Feature selection is one of the most significant aspects of machine learning which is used to remove the insignificant features from a given dataset to improve the accuracy of machine learning classifiers. Three different classifiers, namely Random Forest, k-Nearest Neighbor (kNN) and Decision Tree, are used for diagnosing the thyroid disease. The experimental results indicate that MALO eliminates 71.5% insignificant features out of the total number of features. The best accuracy achieved on the reduced set of features is 95.94% with Random Forest Classifier. Also, a notable accuracy of 95.66% and 92.51% has been achieved by Decision Tree classifier and k-Nearest Neighbor classifier, respectively. Additionally, MALO has been compared with other optimized variants of evolutionary algorithms to show the effectiveness and superiority of the proposed algorithm. Hence, the experimental results indicate that the MALO significantly outperf

关键词： Thyroid disease ant lion optimization algorithm Modified ant lion optimization algorithm Evolutionary algorithm Feature selection Machine learning

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Learning Parameters in Deep Belief Networks Through ant lion optimization algorithm 10

Learning Parameters in Deep Belief Networks Through Ant Lion...

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10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Ye, Zhiwei Tang, Yuanzhi Liu, Wei Hu, Mingwei Wang, Ziwei Zhang, Li Wei, Ming Hubei Univ Technol Sch Comp Sci Wuhan 430068 Peoples R China Wuhan Fiberhome Tech Serv Co Ltd Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9781728140698

Restricted Boltzmann Machine (RBM) has been widely used technologies in the field of deep learning. The RBM model provides good technical support for implementing various parallelisms. Deep Belief Networks are composed of a set of RBMs, and the performance of the DBN model depends in part on the performance of the RBM model. However, the performance of the RBM model affected by the parameters is its main drawback. How to correctly and effectively fine-tune the parameters of the RBM model has not been completely addressed. In this paper, The ant lion optimization (ALO) algorithm we mentioned is the latest intelligent optimization algorithm, which has good optimization performance and robustness. So, in order to verify our proposed method. First, ALO algorithm and PSO algorithm are used separately to fine-tune the parameters of RBM model, then DBN model stacked by adjusted RBM models are used for image classification. Experimental verifications are conducted on MNIST dataset. The results show that the ALO algorithm has better results.

关键词： Deep Belief Networks Restricted Boltzma nn Machine ant lion optimization algorithm

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ant lion optimization algorithm for optimal location and sizing of renewable distributed generations

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RENEWABLE ENERGY 2017年 101卷 1311-1324页

作者： Ali, E. S. Abd Elazim, S. M. Abdelaziz, A. Y. Zagazig Univ Fac Engn Elect Power & Machine Dept Zagazig 44519 Egypt Jazan Univ Fac Engn Dept Elect Jazan Saudi Arabia Ain Shams Univ Fac Engn Elect Power & Machine Dept Cairo Egypt

Renewable sources can supply a clean and smart solution to the increased demands. Thus, Photovoltaic (PV) and Wind Turbine (WT) are taken here as resources of Distributed Generation (DG). Location and sizing of DG have affected largely on the system losses. In this paper, ant lion optimization algorithm (ALOA) is proposed for optimal location and sizing of DG based renewable sources for various distribution systems. First the most candidate buses for installing DG are introduced using Loss Sensitivity Factors (LSFs). Then the proposed ALOA is used to deduce the locations and sizing of DG from the elected buses. The proposed algorithm is tested on two IEEE radial distribution systems. The obtained results via the proposed algorithm are compared with other algorithms to highlight its benefits in decreasing total power losses and consequently increasing the net saving. Moreover, the results are presented to confirm the effectiveness of ALOA in enhancing the voltage profiles for different distribution systems and loading conditions. Also, the Wilcoxon test is performed to verify the superiority of ALOA. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Distributed generation Renewable energy Loss reduction Voltage profiles ant lion optimization algorithm Loss sensitivity factors

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ant lion optimization algorithm for optimal sizing of renewable energy resources for loss reduction in distribution systems

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Journal of Electrical Systems and Information Technology 2018年第3期5卷 663-680页

作者： Dinakara Prasasd Reddy P. Veera Reddy V.C. Gowri Manohar T. Department of EEE S.V. University Tirupati India AITS Tirupati India

This paper mainly focused on the impact of distributed generation (DG) placement on distribution system. The integration of DG is transforming the traditional radial distribution system into a multi-source system. Distributed generation is a term that refers to the production of electricity near the consumption place. The effects of distributed generation are short circuit levels are increased, load losses change, reliability change and voltage profiles change along the network. The above advantages can be accomplished by ideal position and sizing of DG units. The ideal positions are obtained from index vector method. ant lion optimization (ALO), a novel meta heuristic algorithm is used to determine the optimal DG size. ALO is modeled based on the unique hunting behavior of ant lions. The ALO algorithm is evaluated on IEEE 15, 33, 69 and 85-bus test systems. ALO algorithm was compared with different types of DG units and other evolutionary algorithms. When compared with other algorithms the ALO algorithm gives better results. From the analysis best results have been achieved from type III DG operating at 0.9 pf.

关键词： ant lion optimization algorithm Index vector method Distributed generation placement Radial distribution system Loss reduction DG locations

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ant lion optimization algorithm for Renewable Distributed Generations

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ENERGY 2016年第Part1期116卷 445-458页

作者： Ali, E. S. Abd Elazim, S. M. Abdelaziz, A. Y. Zagazig Univ Fac Engn Elect Power & Machine Dept Zagazig Egypt Ain Shams Univ Fac Engn Elect Power & Machine Dept Cairo Egypt Jazan Univ Fac Engn Jazan Saudi Arabia

Renewable sources can provide a clean and smart solution to the increased demands. Thus, Photovoltaic (PV) system and Wind Turbine (WT) are considered here as sources of Distributed Generation (DG). Allocation and sizing of DG have greatly affected on the system losses. This paper aims to propose ant lion optimization algorithm (ALOA) for optimal allocation and sizing of renewable DG sources in various distribution networks. First the most candidate buses for installing DG are suggested using Loss Sensitivity Factors (LSFs). Then the proposed ALOA is employed to deduce the locations of DG and their sizing from the elected buses. The proposed algorithm is tested on 33 and 69 bus radial distribution systems. The obtained results via the proposed algorithm are compared with others to highlight its benefits in reducing total power losses and consequently maximizing the net saving. Moreover, the results are introduced to verify the superiority of the proposed algorithm to improve the voltage profiles for various loading conditions. Also, the Wilcoxon test is applied to confirm the effectiveness of the proposed algorithm. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Distributed Generation Renewable sources Voltage profiles ant lion optimization algorithm Loss sensitivity factors Wilcoxon test

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