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Strengthening security in IoT-based smart cities utilizing cycle-consistent generative adversarial networks for attack detection and secure data transmission

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2025年第2期18卷 1-16页

作者： Agitha, W. Brabin, D. R. Denslin Kumar, K. Kalai Sunitha, T. DMI Coll Engn Dept Comp Sci & Engn Chennai Tamil Nadu India Saveetha Engn Coll Dept Artificial Intelligence & Data Sci Chennai Tamil Nadu India

The main purpose of Smart Environments (SE) is to conveniently improve the human's daily life. Internet of Things (IoT) is a developing network for smart objects. Privacy-based security is a significant issue in any real-world smart environments centered on the IoT system. Security susceptibility in the IoT-centered systems provides a risk of security affecting smart environment applications. In this manuscript, Strengthening Security in IoT-Based Smart Cities utilizing Cycle-Consistent Generative Adversarial Networks for Attack Detection and Secure Data Transmission (IoT-SC-CCGAN-ADSDT) is proposed. Here, input information is gathered from NSL-KDD. The NSL-KDD input is pre-processed. Then, the important features of the pre-processed data are selected by using Wild horse optimizer (WHO). After feature selection, the chosen features are provided to cycle-consistent generative adversarial network classifier for classifying the attack and normal data. The selected features are sent to the use after the prediction of outcomes using Advanced Encryption Standard (AES). The AES is optimized using chameleon swarm algorithm for transmitting the data in a safer way. After transmitting the data securely, the normal data outcomes obviously shown in LCD monitor. To show these results, major problems in the smart cities are simply detected. The proposed model is activated using java. The efficiency is examined with performance metrics, like precision, sensitivity, specificity, accuracy, computational time, encryption time, decryption time, security level. The proposed IoT-SC-CCGAN-ADSDT approach provides 96.68%, 7.142%, 94.65%, and 97.58% greater accuracy compared to the existing DL-IOT-SCA, IoT-SC-PCA, IoT-SCA-DL methods respectively.

关键词： Advanced encryption standard Attack detection chameleon swarm algorithm Wild horse optimizer Internet of Things Smart cities

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Enhancing Cloud Task Scheduling With a Robust Security Approach and Optimized Hybrid POA

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IEEE ACCESS 2023年 11卷 122426-122445页

作者： Kumer, S. V. Aswin Prabakaran, N. Mohan, E. Natarajan, Balaji Sambasivam, G. Tyagi, Vaibhav Bhushan Koneru Lakshmaiah Educ Fdn Dept Elect & Commun Engn Vaddeswaram 522302 Andhra Pradesh India SIMATS Saveetha Sch Engn Dept ECE Chennai 602105 Tamil Nadu India Sri Venkateshwaraa Coll Engn & Technol Dept Comp Sci & Engn Pondicherry 605102 India Xiamen Univ Malaysia Sch Comp & Data Sci Sepang 43900 Selangor Darul Malaysia ISBAT Univ Fac Engn Kampala Uganda

Dynamic and flexible computing resources are offered by cloud computing (CC), which has gained popularity as a computing technology. Efficient task scheduling (TS) plays a critical role in CC by optimizing the distribution of tasks across available resources to achieve maximum performance. The allocation of computational tasks in a cloud environment is a complicated process that is affected by multiple factors, such as available network bandwidth, make span, and cost considerations. Therefore, it is crucial to optimize available bandwidth for efficient TS in CC. In the present research, a novel pelican-based approach is introduced to optimize TS in the CC environment. The newly developed method also utilizes a security approach called Polymorphic Advanced Encryption Standard (P-AES) to encode cloud information during scheduling. The study evaluates the proposed algorithm's performance in terms of the make span, resource utilization, cost, response time, throughput, latency, execution time, speed, and bandwidth utilization. The simulation is carried out using the Python tool, and it effectively handles a wide range of tasks from 1000 to 5000. The proposed algorithm offers a new perspective on utilizing pelican algorithms to optimize task scheduling in CC. The hybrid optimization enables the proposed algorithm to provide efficient task scheduling by exploiting the strengths of entire algorithms. The proposed approach offers an innovative solution to the challenges of scheduling tasks in cloud environments and provides a more effective and secure way of optimizing cloud services. Overall, this study provides valuable insights into task scheduling optimization in CC and offers an effective approach for enhancing the performance of CC services.

关键词： Task analysis Cloud computing Processor scheduling Dynamic scheduling Optimization Resource management Heuristic algorithms Encryption Particle swarm optimization Advanced encryption standard chameleon swarm algorithm cloud computing hybrid model security moth swarm algorithm task scheduling

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Mechanomyography Signal Pattern Recognition of Knee and Ankle Movements Using swarm Intelligence algorithm-Based Feature Selection Methods

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SENSORS 2023年第15期23卷 6939页

作者： Zhang, Yue Sun, Maoxun Xia, Chunming Zhou, Jie Cao, Gangsheng Wu, Qing Nantong Univ Sch Mech Engn Nantong 226019 Peoples R China Univ Shanghai Sci & Technol Sch Mech Engn Shanghai 200093 Peoples R China East China Univ Sci & Technol Sch Mech & Power Engn Shanghai 200237 Peoples R China

Pattern recognition of lower-limb movements based on mechanomyography (MMG) signals has a certain application value in the study of wearable rehabilitation-training devices. In this paper, MMG feature selection methods based on a chameleon swarm algorithm (CSA) and a grasshopper optimization algorithm (GOA) are proposed for the pattern recognition of knee and ankle movements in the sitting and standing positions. Wireless multichannel MMG acquisition systems were designed and used to collect MMG movements from four sites on the subjects thighs. The relationship between the threshold values and classification accuracy was analyzed, and comparatively high recognition rates were obtained after redundant information was eliminated. When the threshold value rose, the recognition rates from the CSA fluctuated within a small range: up to 88.17% (sitting position) and 90.07% (standing position). However, the recognition rates from the GOA drop dramatically when increasing the threshold value. The comparison results demonstrated that using a GOA consumes less time and selects fewer features, while a CSA gives higher recognition rates of knee and ankle movements.

关键词： feature selection chameleon swarm algorithm grasshopper optimization algorithm mechanomyography pattern recognition

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Optimization of K-means clustering method using hybrid capuchin search algorithm

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JOURNAL OF SUPERCOMPUTING 2024年第2期80卷 1728-1787页

作者： Qtaish, Amjad Braik, Malik Albashish, Dheeb Alshammari, Mohammad T. T. Alreshidi, Abdulrahman Alreshidi, Eissa Jaber Univ Hail Coll Comp Sci & Engn Dept Informat & Comp Sci Hail 81481 Saudi Arabia Al Balqa Appl Univ Prince Abdullah bin Ghazi Fac Informat & Commun Te Comp Sci Dept Salt Jordan

This work presents Hybrid Capuchin Search algorithm (HCSA) as a meta-heuristic method to deal with the vexing problems of local optima traps and initialization sensitivity of the K-means clustering technique. This study was inspired by the popularity and permanence of meta-heuristics in presenting convincing solutions, which sparked various efficient methods and computational tools to tackle difficult and practical real-world problems. The movement behavior of CSA is strengthened using the chameleon swarm algorithm to support the search agents of CSA to more effectively explore and exploit each potential region of the search space. This increases the capacity of both exploitation and exploration of the traditional CSA. Besides, the search agents of CSA utilize the rotation mechanism in CS to migrate to new spots outside the nearby regions to perform global search. This mechanism improves the search proficiency of CSA as well as the intensification and diversity abilities of the search agents. These expansion aptitudes of CSA expand its exploitation potential and broaden the range of search scopes, sizes, and directions in conducting clustering activities. A total of 16 different datasets from diverse sources, each with a different level of complexity, characteristics, and dimension, are used to assess the performance of the developed HCSA method on clustering tasks. According to the experimental results, the proposed HCSA performs statistically significantly better than the K-means clustering algorithm and eight meta-heuristics-based clustering in terms of both distance and performance metric measures.

关键词： Capuchin search algorithm chameleon swarm algorithm Clustering K-means Meta-heuristics

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Optimal threshold selection for segmentation of Chest X-Ray images using opposition-based swarm-inspired algorithm for diagnosis of pneumonia

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第9期83卷 27089-27119页

作者： Khosla, Tejna Verma, Om Prakash Delhi Technol Univ Comp Sci Engn Dept Bawana Delhi India Delhi Technol Univ Elect & Commun Engn Dept Bawana Delhi India

Image Segmentation using thresholding is one of the most significant areas of image processing. However, the challenge lies in accurately and effectively segmenting medical images, which is a crucial step in many applications of medical image analysis. It necessitates the development of an effective and robust segmentation approach that can handle the complexity and diversity of medical images. To address this problem, we propose a novel image segmentation technique based on minimizing the cross-entropy function using a hybrid approach that combines the features of Opposition-Based Learning (OBL), chameleon swarm algorithm (CSA), and Particle swarm Optimization algorithm (PSO). The opposition-based technique generates the initial population and improves convergence. Then, PSO and CSA are run in parallel on an unequal population set to improve the optimal results. The proposed approach, named the Opposition-based chameleon swarm algorithm improved by Particle swarm algorithm (CSAPSO), is evaluated on twelve Chest X-Ray (CXR) images of patients for the detection of Pneumonia. It is further tested on a large data set related to COVID-19. We conducted extensive comparisons with other state-of-the-art methods and the Deep Learning algorithms and used the performance indicators, namely Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR) and Structure Similarity Index (SSIM), Classification Accuracy, Area Under Curve for evaluating the performance. The proposed approach is statically analyzed using the Friedman rank-sum test. Through the analysis, CSAPSO demonstrates better global optimal results compared to state-of-the-art techniques.

关键词： chameleon swarm algorithm Cross entropy thresholding Medical imaging Particle swarm optimization algorithm Pneumonia Segmentation

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Efficient quantum inspired blockchain-based cyber security framework in IoT using deep learning and huristic algorithms

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2024年第2期18卷 1203-1232页

作者： Josphine, C. Vimala Kingslin, M. Theodore Vincy, R. Fatima Mohana, M. Babitha, S. RMK Engn Coll Artificial Intelligence & Datasci Chennai 601206 Tamil Nadu India RMK Coll Engn & Technol Dept Elect & Commun Engn Chennai Tamil Nadu India Easwari Engn Coll Dept Comp Sci & Engn Chennai Tamil Nadu India Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala E Artificial Intelligence & Data Sci Chennai Tamil Nadu India Hindustan Inst Technol & Sci Dept Informat Technol Padur India

"Internet-of-Things (IoT)" systems and components are highly noticed by cybercriminals including the affiliated or the nation-state sponsored threat actors as become a united part of the linked ecosystem and the society. But, the difficulties in protecting the systems and the devices are combined of scale and multiple deployments, the speed-paced cyber threats landscape, and more parameters. With the enhanced internet services, cyber security grows one of the highest research issues of the latest digital world. It is very important to develop a cyber security model to identify the various types of attacks. To overcome these problems, a quantum-inspired blockchain-assisted cyber security model is obtained in the IoT platform. Firstly, the required information is obtained from quality online information resources. Then, the information is stored in the quantum-inspired blockchain with optimal key, where the key optimization is performed with the help of the Fitness-based Jellyfish chameleon swarm algorithm (FJCSA). Then, the stored data are recovered and finally, fed to the intrusion detection stage to verify whether it is affected by any unauthorized entities. The intrusion detection is done with the support of "Adaptive Attention-based Long Short Term Memory (LSTM) with Adaboost (AALSTM-Ab)", where the parameters are optimized by using the FJCSA. Furthermore, the experimental results of the developed model are validated by comparing the performance of various recently implemented blockchain-based cyber security approaches with respect to several positive and negative performance measures. From the result analysis, the accuracy and precision rate of the recommended model are 95.50% and 91.40%.

关键词： Internet of things blockchain-based cyber security key optimization intrusion detection Fitness-based Jellyfish chameleon swarm algorithm Adaptive Attention-based Long Short Term Memory with Adaboost

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MCSA: Multi-strategy boosted chameleon-inspired optimization algorithm for engineering applications

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2023年第PartA期403卷 1页

作者： Hu, Gang Yang, Rui Qin, Xinqiang Wei, Guo Xian Univ Technol Dept Appl Math Xian 710054 Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Univ North Carolina Pembroke Pembroke NC 28372 USA Xian Univ Technol 5 South Jinhua Rd Xian 710048 Peoples R China

chameleon swarm algorithm (CSA) is a newly proposed swarm intelligence algorithm inspired by the chameleon's foraging strategies of tracking, searching and attacking targets, and has shown well competitive performance with other state-of-the-art algorithms. Interestingly, CSA mathematically models and implements the steps of chameleon's unique food-seeking behavior. Nevertheless, the original CSA suffers from the challenges of insufficient exploitation ability, ease of falling into local optima, and low convergence accuracy in complex large-scale applications. Aiming at these challenges, an efficient enhanced chameleon swarm algorithm termed MCSA, combined with fractional-order calculus, sinusoidal adjustment of parameters and crossoverbased comprehensive learning (CCL) strategy, is developed in this paper. Firstly, a good fractional-order calculus strategy is added to update the chameleon's attack velocity, which heightens the local search ability of CSA and accelerates the convergence speed of the algorithm;meanwhile, the sinusoidal adjustment of parameters is adopted to provide a better balance between exploration and exploitation of CSA. Secondly, the CCL strategy is used for the mutation to increase the diversity of the population and avoid becoming trapped in local optima. Three strategies enhance the overall performance and efficiency of the native CSA. Finally, the superiority of the presented MCSA is verified in detail by comparing it with native CSA and several state-of-the-art algorithms on the well-known 23 benchmark test functions, CEC2017 and CEC2019 test suites, respectively. Furthermore, the practicability of MCSA is also highlighted by six real-world engineering designs and two truss topology optimization problems. Simulation results demonstrate that MCSA has strong competitive capabilities and promising prospects. MCSA is potentially an excellent meta-heuristic algorithm for solving engineering optimization problems. (c) 2022 Elsevier B.V. All righ

关键词： chameleon swarm algorithm Fractional-order calculus Sinusoidal adjustment Crossover-based comprehensive learning Engineering design Truss topology optimization

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Metaheuristic Approach to Synthesis of Suspension System of Mobile Robot for Mining Infrastructure Inspection

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SENSORS 2022年第22期22卷 8839-8839页

作者： Malarczyk, Mateusz Kaminski, Marcin Szrek, Jaroslaw Wroclaw Univ Sci & Technol Fac Elect Engn Dept Elect Machines Drives & Measurements Smoluchowskiego 19 PL-50372 Wroclaw Poland Wroclaw Univ Sci & Technol Dept Fundamentals Machine Design & Mechatron Syst Fac Mech Engn Lukasiewicza 7-9 PL-50372 Wroclaw Poland

The article describes the problem of geometric synthesis of the inspection robot suspension system, designed for operation in difficult conditions with the presence of scattered obstacles. The exemplary application of a mine infrastructure inspection robot is developed and supported by the ideas. The brief introduction presents current trends, requirements and known design approaches of platforms enabled to cross the obstacles. The idea of a nature-inspired wheel-legged robot is given, and the general outline of its characteristics is provided. Then the general idea of kinematic system elements selection is discussed. The main subject of geometrical synthesis of the chosen four-bar mechanism is described in detail. The mathematical model of the suspension and connections between the parts of the structure is clarified. The well-known analytical approach of brute force search is analyzed and validated. Then the method inspired by the branch and bound algorithm is developed. Finally, a novel application of the nature-inspired algorithm (the chameleon swarm algorithm) to synthesis is proposed. The obtained results are analyzed, and a brief comparison of methods is given. The successful implementation of the algorithm is presented. The obtained results are effectively tested with simulations and experimental tests. The designed structure developed with the CSA is assembled and attached to the prototype of a 14-DOF wheel-legged robot. Furthermore, the principles of walking and the elements forming the control structure were also discussed. The paper is summarized with the description of the developed wheel-legged robot LegVan 1v2.

关键词： geometrical synthesis four-bar mechanism chameleon swarm algorithm wheel-legged robot optimization

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