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A dynamic multi-objective evolutionary algorithm based on Niche prediction strategy

Applied Soft computing 2023年 142卷

作者： Zheng, Jinhua Zhang, Bo Zou, Juan Yang, Shengxiang Hu, Yaru Hunan Engineering Research Center of Intelligent System Optimization and Security Xiangtan University Hunan Province Xiangtan411105 China Key Laboratory of Intelligent Computing and Information Processing Ministry of Education of China Xiangtan University Hunan Province Xiangtan411105 China Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Hunan Province Xiangtan411105 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

In reality, many multi-objective optimization problems are dynamic. The Pareto optimal front (PF) or Pareto optimal solution (PS) of these dynamic multi-objective problems (DMOPs) changes as the environment change. Therefore, solving such problems requires an optimization algorithm that can quickly track the PF or PS after an environment change. Prediction-based response mechanism is a common method used to deal with environmental changes, which is commonly known as center point-based prediction. However, if the predicted direction of the center point is inaccurate, the predicted population will be biased towards one side. In this paper, we propose a niche prediction strategy based on center and boundary points (PCPB) to solve the dynamic multi-objective optimization problems, which consists of three steps. After environmental changes are detected, the first step is to divide the niche, dividing different individuals in the PS into different niche populations. The second step is to independently predict different niches, and select individuals with good convergence and distribution in the niche to predict the individuals that will produce the next generation. Finally, some different individuals are randomly generated in the next possible PS area to ensure the diversity of the population. To verify whether our proposed strategy is effective and competitive, PCPB was compared with five state-of-the-art strategies. The experimental results show that PCPB performed competitively in solving dynamic multi-objective optimization problems, which proves that our algorithm has good competitiveness. © 2023 Elsevier B.V.

关键词： Forecasting

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Ciphertext-only fault analysis on the Midori lightweight cryptosystem

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Science China(Information Sciences) 2020年第3期63卷 288-292页

作者： Wei LI Linfeng LIAO Dawu GU Shan CAO Yixin WU Jiayao LI Zhihong ZHOU Zheng GUO Ya LIU Zhiqiang LIU School of Computer Science and Technology Donghua University Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Key Laboratory of Integrate Administration Technologies for Information Security School of Microelectronics Shanghai Jiao Tong University Department of Computer Science and Engineering University of Shanghai for Science and Technology

Dear editor,The Midori lightweight cipher can be applied in the RFID tags and other low-resource devices to protect information on the internet. This cipher was proposed by Banik et al. and presented at the ASIACRYPT conference [1]. It has a Substitution Permutation Network structure with a 128-bit keysize, and two block-size variants, 64 bits

关键词： CFA Ciphertext-only fault analysis on the Midori lightweight cryptosystem

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Stackelberg Game-Based Pricing and Offloading for the DVFS-Enabled MEC Systems

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2502-2515页

作者： Mei, Jing Zeng, Cuibin Tong, Zhao Yang, Zhibang Li, Keqin Hunan Normal University College of Information Science and Engineering Changsha410081 China Changsha University in Changsha Hunan Province Key Laboratory of Industrial Internet Technology and Security Hunan 410022 China Hunan University College of Information Science and Engineering Changsha410012 China National Supercomputing Center Hunan Changsha410082 China State University of New York Department of Computer Science New YorkNY12561 United States

Due to the limited computing resources of both mobile devices (MDs) and the mobile edge computing (MEC) server, devising reasonable strategies for MD task offloading, MEC server resource pricing, and resource allocation is crucial. In this paper, a scenario is considered, comprising multiple MDs and a single MEC server. Each MD has a divisible task in each time slot, allowing for partial offloading and the option to discard parts of the task. The MEC server contains multiple computing units with the same computing power, and its computing resources can be dynamically adjusted through dynamic voltage and frequency scaling (DVFS) according to the size of tasks offloaded by MDs. At any given time slice, a Stackelberg game is formulated based on the strategies of the MDs and the strategy of the MEC server. An iterative evolution algorithm is employed to explore the optimal strategies for MDs and the MEC server. Simulation results demonstrate that both parties can reach an equilibrium state through the game, and these experiments confirm that the algorithm effectively enhances system efficiency. © 2004-2012 IEEE.

关键词： Mobile edge computing

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Power internet of Things security Evaluation Method Based on Fuzzy Set Theory 15th

Power Internet of Things Security Evaluation Method Based on...

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15th International Conference on Genetic and Evolutionary computing, ICGEC 2023

作者： Wang, Yuman Wu, Hongbin Wang, Yilei Wang, Zixiang Zhu, Xinyue Qian, Kexiang SGRI Power Grid Digitizing Technology Department State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co. Ltd. Beijing102200 China Internet Technology Center State Grid Zhejiang Electric Power Co. Ltd. Research Institute Hangzhou310000 China School of Computer Science Northeast Electric Power University Jilin Jilin City132012 China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100000 China

ISBN: (纸本)9789819994113

Aiming at the problems of high fuzziness and low integrity of the current Power internet of Things network security evaluation index system, the fuzzy set theory is used to evaluate the security issues of Power IoT terminal, protocol, strategy, business operation, business management, employee operation and user operation of the Power internet of Things from two aspects of the Power internet of Things itself and manpower, the fuzzy judgment matrix is constructed, and the weight of index items is determined by AHP. To establish a comprehensive and reasonable Power internet of Things safety evaluation index system, overcome the shortcomings of single and quantitative analysis of Power internet of Things network risk, and realize the determination of fuzzy index weight combining quantitative and qualitative, so as to improve the accuracy of Power internet of Things safety evaluation. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： internet of things

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Utilizing Lexicon-enhanced Approach to Sensitive Information Identification 27

Utilizing Lexicon-enhanced Approach to Sensitive Information...

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27th International Conference on Automation and computing, ICAC 2022

作者： Cai, Lihua Zhou, Yujue Ding, Yulong Jiang, Jie Yang, Shuang-Hua Institute of Risk Analysis Prediction & Management Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Shenzhen Key Laboratory of Future Industrial Internet Safety and Security Southern University of Science and Technology Shenzhen China Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology Shenzhen China Department of Computer Science University of Warwick CoventryCV4 7AL United Kingdom

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

Large-scale sensitive information leakage incidents have occurred frequently, causing huge impacts and losses to individuals, enterprises, and society. Most sensitive information exists in unstructured data, making it challenging for people to identify when it is leaked, an important cause of information leakage. Therefore, sensitive information identification from unstructured data has received extensive attention. In addition, the smallest unit of Chinese is a character, so its lexical boundary is flexible, which makes it very difficult to identify sensitive information in Chinese. It is worth mentioning that there are no publicly available datasets in this field of sensitive information identification due to the sensitivity. To address the above challenges, we first create the SPIDC (Sensitive Personal Information Dataset in Chinese) and release it as a public resource for related research. Second, we apply the existing sensitive information identification methods on the English datasets to the Chinese datasets. In addition, to solve the problem of uncertainty and ambiguity of Chinese vocabulary boundary, we apply three lexicon-enhanced technologies from NER (Named Entity Recognition) to the Chinese sensitive information identification for the first time. Experimental results on the SPIDC show that the lexicon-enhanced approach has better performance than other methods. © 2022 IEEE.

关键词： Sensitive data

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Robin: A Novel Method to Produce Robust Interpreters for Deep Learning-Based Code Classifiers

Robin: A Novel Method to Produce Robust Interpreters for Dee...

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IEEE International Conference on Automated Software engineering (ASE)

作者： Zhen Li Ruqian Zhang Deqing Zou Ning Wang Yating Li Shouhuai Xu Chen Chen Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Hubei Engineering Research Center on Big Data Security Department of Computer Science University of Colorado Colorado Springs USA Center for Research in Computer Vision University of Central Florida USA School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Deep learning has been widely used in source code classification tasks, such as code classification according to their functionalities, code authorship attribution, and vulnerability detection. Unfortunately, the black-box nature of deep learning makes it hard to interpret and understand why a classifier (i.e., classification model) makes a particular prediction on a given example. This lack of interpretability (or explainability) might have hindered their adoption by practitioners because it is not clear when they should or should not trust a classifier's prediction. The lack of interpretability has motivated a number of studies in recent years. However, existing methods are neither robust nor able to cope with out-of-distribution examples. In this paper, we propose a novel method to produce Robust interpreters for a given deep learning-based code classifier; the method is dubbed Robin. The key idea behind Robin is a novel hybrid structure combining an interpreter and two approximators, while leveraging the ideas of adversarial training and data augmentation. Experimental results show that on average the interpreter produced by Robin achieves a 6.11% higher fidelity (evaluated on the classifier), 67.22% higher fidelity (evaluated on the approximator), and 15.87x higher robustness than that of the three existing interpreters we evaluated. Moreover, the interpreter is 47.31% less affected by out-of-distribution examples than that of LEMNA.

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Meet-in-the-Middle Statistical Fault Analysis of the PRESENT lightweight Cryptosystem

Meet-in-the-Middle Statistical Fault Analysis of the PRESENT...

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作者： Li, Wei Zhu, Xiao-Ming Gu, Da-Wu Li, Jia-Yao Cai, Tian-Pei School of Computer Science and Technology Donghua University Shanghai201620 China Department of Computer and Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Key Laboratory of Scalable Computing and System Shanghai200240 China Shanghai Key Laboratory of Integrate Administration Technologies for Information Security Shanghai200240 China

The lightweight block cipher PRESENT was presented at the International Conference on Cryptographic Hardware and Embedded Systems (CHES) in 2007, and it has become a standard of ISO/IEC-29192-2 for lightweight cryptosystems since 2012. Within the environment of the internet of Things, this cipher is applicable to Radio Frequency Identification, network sensors, and smart cards. Its secret key can be either 80 or 128 bits long, while the block is 128 bits long. Since the publication of the PRESENT, numerous forms of cryptanalysis have been developed in order to evaluate its level of security. Some examples of these attacks include the differential attack, the linear attack, the integral attack, the algebraic attack, the fault attack, the side-cube attack, and the Biclique attack. All of these cryptanalyses are on the basis of the assumption that an attack using a chosen plaintext or an attack using a known plaintext would occur. In order to carry out a chosen-plaintext attack, an attacker must have the ciphertexts along with the plaintexts that go along with them, whereas a known-plaintext attack requires a significant quantity of plaintexts and ciphertexts. The ciphertext-only attack is distinct from the chosen-plaintext attack and the known-plaintext attack, which require the ciphertext only in order to be successful. In this sense, the ciphertext-only attack is relevant in a range of scenarios. There is no research that has been released yet regarding PRESENT'S resilience to the ciphertext-only attack. This study presents a novel meet-in-the-middle statistical fault analysis using the approach of statistical analysis and meet-in-the-middle attack of PRESENT. This analysis is able to decrypt the 80-bit and 128-bit secret keys of PRESENT with a series of new distinguishers that include the Pearson correlation coefficient-Hamming weight, the Kullback Leibler divergence- Hamming weight, and the Jaccard similarity coefficient-Hamming weight-maximum likelihood estimatio

关键词： Side channel attack

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NEXT:a neural network framework for next POI recommendation

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Frontiers of computer Science 2020年第2期14卷 314-333页

作者： Zhiqian ZHANG Chenliang LI Zhiyong WU Aixin SUN Dengpan YE Xiangyang LUO Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of EducationSchool of Cyber Science and EngineeringWuhan UniversityWuhan 430072China Department of Computer Science The University of Hong KongPokfulam RoadHong Kong 999077China School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore State Key Lab of Mathematical Engineering and Advanced Computing Zhengzhou 450001China

The task of next POI recommendations has been studied extensively in recent ***,developing a unified recommendation framework to incorporate multiple factors associated with both POIs and users remains challenging,because of the heterogeneity nature of these ***,effective mechanisms to smoothly handle cold-start cases are also a difficult *** by the recent success of neural networks in many areas,in this paper,we propose a simple yet effective neural network framework,named NEXT,for next POI *** is a unified framework to learn the hidden intent regarding user's next move,by incorporating different factors in a unified ***,in NEXT,we incorporate meta-data information,e.g.,user friendship and textual descriptions of POIs,and two kinds of temporal contexts(i.e.,time interval and visit time).To leverage sequential relations and geographical influence,we propose to adopt DeepWalk,a network representation learning technique,to encode such *** evaluate the effectiveness of NEXT against other state-of-the-art alternatives and neural networks based *** results on three publicly available datasets demonstrate that NEXT significantly outperforms baselines in real-time next POI *** experiments show inherent ability of NEXT in handling cold-start.

关键词： POI neural networks POI recommendation

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Ensemble Learning-based Traffic Classification with Small-Scale Datasets for Wireless Networks

Ensemble Learning-based Traffic Classification with Small-Sc...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Xiaorong Wang Wenting Wei Xiaoshan Yu Danyang Zheng Neeraj Kuma Lei Liu State Key Laboratory of Integrated Service Networks Xidian University Xi'an China School of Computing and Artificial Intelligence Southwest Jiaotong University China Department of Computer Science and Engineering Thapar Institute of Engineering and Technology India Guangzhou Institute of Technology Xidian University Xi'an China Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts & Telecommunications Xi'an China

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

Traffic classification is an essential part of intelligent network management in Fifth generation (5G) networks, from quality of service guarantees to security monitoring. With the evolution of diverse services and applications over 5G networks, traffic patterns and their implications also becoming more complicated, especially in wireless networks. How to dynamically manage the growing traffic demand and satisfy the quality of service (QoS) needs of new applications is particularly critical. The biggest challenge is how to identify traffic types accurately without application information and manual features. However, the existing deep learning (DL)-based classifiers heavily rely on extensive and high-quality samples, which proves impractical to obtain in wireless networks. To overcome this limitation, we propose an ensemble Learning based traffic classification model to provide accurate identification with small-scale datasets, where Attention-based Long Short-Term Memory Networks are bagged to train this classifier. The ensemble learning approach employs a combinatorial design involving five base models to construct more accurate and robust learning models. This enables effective feature absorption, making it feasible to train classifiers on limited sample sets. Comprehensive experiments are conducted using a real-world dataset covering 16 applications. Results demonstrate that, even with a mere 10% of training samples, our proposed model achieves a classification accuracy of 95.57% and a precision of 95.83%, surpassing multiple cutting-edge methods.

关键词： Training Accuracy 5G mobile communication Wireless networks Computational modeling Quality of service Telecommunication traffic

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Compression Meets security: Low-Complexity Linear Collaborative Federated Learning with Enhanced Accuracy

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IEEE Transactions on Mobile computing 2025年

作者： Wang, Tianshun Li, Peichun Feng, Panpan Wei, Xin Qian, Liping Wu, Yuan Nanjing University of Posts and Telecommunications School of Communication and Information Engineering Nanjing China University of Macau State Key Laboratory of Internet of Things for Smart City China University of Macau Department of Computer and Information Science China Zhejiang University of Technology Institute of Cyberspace Security Hangzhou310023 China

Federated learning (FL) has been regarded as a promising paradigm for enabling distributed model training over resource-limited edge devices. Although FL maintains data locality and enhances model generalization, it faces challenges such as model leakage and pressure from frequent model updates. Some existing schemes, such as differential privacy and model encryption, can partially alleviate these issues while sacrificing the accuracy of the modeling training or increasing the computational overheads in training. To address this issue, we design a low-complexity linear collaborative FL (LCFL) framework to enhance the privacy and accuracy of FL. Specifically, we propose the collaborative secrecy transmission (CST) algorithm by integrating a variant of Shamir's secret-sharing with the model segmentation, which can compresses and encrypts the local models for FL. The decoding complexity of the CST algorithm is only O(N3) under the compression ratio of N, which reduces the communication overhead and computational complexity. We conduct a quantitative analysis of the model error induced by the CST algorithm and derive its closed-form upper bound. Within LCFL, we formulate an optimization problem to maximize the global model accuracy in wireless FL by optimizing compression ratios, bandwidth allocation, and transmit-powers. Subsequently, we propose a low-complexity algorithm to solve this problem effectively. Numerical simulations demonstrate the efficacy of LCFL in improving FL's accuracy and security, and the results validate the efficiency of the proposed optimization scheme for wireless FL. © 2002-2012 IEEE.

关键词： Federated learning

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