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An enhanced genetic algorithm for computation task offloading in MEC scenario

International Journal of Wireless and Mobile Computing 2023年第2期25卷 118-127页

作者： Zhao, Jiacheng Li, Wenzao Liu, Hantao Yu, Peizhen Li, Hanyun Wen, Zhan School of Communication Engineering Chengdu University of Information Technology Sichuan Chengdu China Chengdu University of Information Technology Sichuan Chengdu China Network and Data Security Key Lab of Sichuan Pro. University of Electronic Science and Technology of China Sichuan Chengdu China Educational Informationisation and Big Data Centre Education Department of Sichuan Province Sichuan Chengdu China

The explosive growth of Internet of Things (IoT) and 5G communication technologies has driven the increasing computing demands for wireless devices. Mobile edge computing in the 5G scenario is a promising solution for energy-efficient and low latency applications. However, due to limited bandwidth, the selection of appropriate computing tasks greatly affects the user experience and system performance. Under the wireless bandwidth constraint, the reasonable choice of offloading objects is an NP-hard problem. The genetic algorithm has a great ability to solve this problem, but the performance of the algorithm varies with different scenarios. This paper proposes a task offloading strategy based on an enhanced genetic algorithm for small-scale computing tasks with an ultra-dense terminal distribution. Numerical experiments show that the convergence speed and optimisation effect of the enhanced genetic algorithm are significantly improved compared to the conventional genetic algorithm. © 2023 Inderscience Enterprises Ltd.

关键词： Genetic algorithms

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Fair electronic voting via bitcoin deposits 5th

Fair electronic voting via bitcoin deposits

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5th International Conference on Geo-Spatial Knowledge and Intelligence, GSKI 2017

作者： Wu, Xijuan Wei, Baodian Tian, Haibo Du, Yusong Ma, Xiao School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Information Security Technology Guangzhou China Chongqing Key Lab of Computer Network and Communication Technology Chongqing China

ISBN: (纸本)9789811308956

Bitcoin is the most popular decentralized digital currency now in use. Block chain is the basic technology of Bitcoin, providing a trustable ledger that can be publicly verified. Research on distributed applications based on block chain has become a new trend. We propose an electronic voting scheme based on block chain and prime numbers, which can support voting situations for multiple candidates. We design protocols for the Bitcoin voting situation, in which there are n voters and k candidates. Each voter will vote for one candidate. The proposed protocols could guarantee that the candidate who gets the majority voting wins the game and no individual voting information is disclosed. Due to the nature of the block chain, the voting results could not be tampered. It is transparent since the block chain is open to the public for verification. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Deposits

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Statistical disclosure: Improved, extended, and resisted

arXiv

引用

arXiv 2017年

作者： Emamdoost, Navid Dousti, Mohammad Sadeq Jalili, Rasool Data and Network Security Lab Department of Computer Engineering Sharif University of Technology Tehran Iran

Traffic analysis is a type of attack on secure communications systems, in which the adversary extracts useful patterns and information from the observed traffic. This paper improves and extends an efficient traffic analysis attack, called "statistical disclosure attack." Moreover, we propose a solution to defend against the improved (and, a fortiori, the original) statistical disclosure attack. Our solution delays the attacker considerably, meaning that he should gather significantly more observations to be able to deduce meaningful information from the traffic. Copyright © 2017, The Authors. All rights reserved.

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Tamperproof IoT with Blockchain

arXiv

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arXiv 2022年

作者： Yu, Guangsheng Liu, Ren Ping Zhang, J. Andrew Guo, Y. Jay Network Security Lab Global Big Data Technologies Centre University of Technology Sydney Australia

We investigate the tamper-resistant property of Blockchain and its effectiveness for IoT systems. In particular, we implemented an IoT testbed, and built a Blockchain into the testbed. A number of tamper-resistance experiments were conducted and analyzed to corroborate the process of block validation in Blockchain. Our analysis and experimental results demonstrate the tamper-resistant capability of Blockchain in securing trust in IoT systems. The demonstration video is provided at [1]. © 2022, CC BY.

关键词： Blockchain

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Novel way to research nonlinear feedback shift register

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Science China(Information Sciences) 2014年第9期57卷 161-174页

作者： ZHAO DaWei PENG HaiPeng LI LiXiang HUI SiLi YANG YiXian Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and Telecommunications Zhejiang Provincial Key Lab of Data Storage and Transmission Technology Hangzhou Dianzi University National Engineering Laboratory for Disaster Backup and Recovery Beijing University of Posts and Telecommunications Shandong Provincial Key Laboratory of Computer Network Shandong Computer Science Center

In this paper,we regard the nonlinear feedback shift register(NLFSR)as a special Boolean network,and use semi-tensor product of matrices and matrix expression of logic to convert the dynamic equations of NLFSR into an equivalent algebraic *** on them,we propose some novel and generalized techniques to study ***,a general method is presented to solve an open problem of how to obtain the properties(the number of fixed points and the cycles with different lengths)of the state sequences produced by a given NLFSR,i.e.,the analysis of a given *** then show how to construct all 22n-(l-n)/22n-lshortest n-stage feedback shift registers(nFSR)and at least 22n-(l-n)-1/22n-l-1shortest n-stage nonlinear feedback shift registers(nNLFSR)which can output a given nonperiodic/periodic sequence with length ***,we propose two novel cycles joining algorithms for the construction of full-length ***,two algorithms are presented to construct 22n-2-1different full-length nNLFSRs,respectively.

关键词： nonlinear feedback shift register semi-tensor product Boolean network full-length nNLFSR binary sequence

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MAL2GCN: A ROBUST MALWARE DETECTION APPROACH USING DEEP GRAPH CONVOLUTIONAL networkS WITH NON-NEGATIVE WEIGHTS

arXiv

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arXiv 2021年

作者： Kargarnovin, Omid Sadeghzadeh, Amir Mahdi Jalili, Rasool Data and Network Security Lab Department of Computer Enginnering Sharif University of Technology Tehran Iran

With the growing pace of using Deep Learning (DL) to solve various problems, securing these models against adversaries has become one of the main concerns of researchers. Recent studies have shown that DL-based malware detectors are vulnerable to adversarial examples. An adversary can create carefully crafted adversarial example to evade DL-based malware detectors. In this paper, we propose Mal2GCN, a robust malware detection model that uses Function Call Graph (FCG) representation of executable files combined with Graph Convolution network (GCN) to detect Windows malware. Since FCG representation of executable files is more robust than raw byte sequence representation, numerous proposed adversarial example generating methods are ineffective in evading Mal2GCN. Moreover, we use the non-negative training method to transform Mal2GCN to a monotonically non-decreasing function;thereby, it becomes theoretically robust against appending attacks. We then present a black-box source code-based adversarial malware generation approach that can be used to evaluate the robustness of malware detection models against real-world adversaries. The proposed approach injects adversarial codes into the various locations of malware source codes to evade malware detection models. The experiments demonstrate that Mal2GCN with non-negative weights has high accuracy in detecting Windows malware, and it is also robust against adversarial attacks that add benign features to the Malware source code. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

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Efficient Zero-Trust data Transmission for Consumer Electronic Using Holographic Counterparts in Internet of Things: Ciphertext-Policy Attribute-Based Signcryption With Equality Test

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IEEE Transactions on Consumer Electronics 2025年

作者： Kuang, Ping Zhang, Xiyue Yang, Mengheng Xiong, Hu Feng, Chaosheng Wu, Deyan Wang, Weizheng Wahaballa, Abubaker University of Electronic Science and Technology of China Intelligent Information Technologies and Applications Lab Chengdu610054 China University of Electronic Science and Technology of China Network and Data Security Lab Chengdu610054 China Sichuan Normal University School of Computer Science Chengdu610101 China Sichuan Institute of Computer Sciences Chengdu610000 China City University of Hong Kong Department of Computer Science Hong Kong The University of Aizu Aizuwakamatsu Japan Arab East Colleges Department of Computer Science Riyadh53354 Saudi Arabia

Secure communication in consumer electronic devices, such as health monitoring systems, is essential as they process sensitive medical data. This paper proposes a Ciphertext-Policy Attribute-Based Signcryption with Equality Test (CP-ABSC-ET) scheme to address these security challenges. The scheme controls who can access data and ensures both encryption and signature verification are done together, keeping the data secure. The integration of equality test enables secure ciphertext verification without decryption, ensuring both data integrity and confidentiality. Compared to existing schemes, the proposed approach reduces the computational overhead. Comprehensive security analysis and performance evaluations demonstrate the scheme’s suitability for dynamic and resource-constrained environments. The CP-ABSC-ET scheme ensures strong data protection, better access control, and efficient communication, making it ideal for consumer electronics like health monitoring devices. © 1975-2011 IEEE.

关键词： Authentication

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PSI Protocol with Structured Encryption

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Jisuanji Xuebao/Chinese Journal of Computers 2022年第12期45卷 2652-2666页

作者： Yang, Jia-Hui Chen, Lan-Xiang Mu, Yi Zeng, Ling-Fang Xue, Yu-Jie Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fuzhou350117 China Zhejiang Lab Hangzhou311121 China Faculty of Data Science City University of Macau Macau999078 China

With the advancement of information technology, local storage and computing capacity cannot well support massive data. The emergence of cloud computing makes data outsourcing a new trend. However, data outsourcing compromises data security and privacy. Therefore, searchable symmetric encryption (SSE) was introduced as a potential solution. The traditional SSE scheme is mainly for text data types. In order to achieve searchable encryption of a wider range of data types, Kamara et al. proposed the concept of structured encryption (STE) in 2010. Structured encryption can realize searchable encryption of complex data structures. It can be used to encrypt social network graphs to achieve retrieval of encrypted data, but cannot be applied to the computation and statistical analysis of social network graphs. Therefore, we design the private set intersection protocol max-PSI, and apply it to the structured encrypted social network graph data, and propose a structured encrypted PSI scheme, STE_max-PSI, to realize any number of nodes in the encrypted graph and the calculation of the maximum intersection size of the set of neighbor nodes. In the encrypted social network graph, it can query the node with the highest affinity of any node (we think two nodes with the most common neighbors have the highest affinity). The proposed scheme utilized a garbled Bloom filter (GBF) to achieve richer query functions for more complex data structures, while protecting the privacy of data. The GBF has negligible false positives when querying elements. Compared with the existing PSI protocols, our scheme can greatly improve the query accuracy. Experiments on the real dataset show that the query accuracy of our scheme is better than the existing schemes. © 2022, Science Press. All right reserved.

关键词： Cryptography

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STGAT: A Spatio-Temporal Graph Attention network for Travel Demand Prediction

STGAT: A Spatio-Temporal Graph Attention Network for Travel ...

引用

International Conference on networking and network Applications (NaNA)

作者： Tao Yi Zhang YuBo Wang ZhiCheng Wei Key Lab of Network and Information Security College of Computer and Cyberspace Security Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Security Hebei Normal University Shijiazhuang Hebei Province China

Forecasting travel demand is a challenging task due to the complex spatial dependencies and dynamic temporal correlation of the traffic data. Furthermore, a limited representation of the given spatial graph structure may restrict the model's ability to effectively learn spatial-temporal dependencies. To extract latent semantic features from traffic data, this paper proposes a method to construct a traffic graph using the Markov cluster algorithm. The traffic semantic correlation matrix is constructed based on a traffic graph to obtain the deep semantic information. Specifically, this study proposes a Spatio-Temporal Graph Attention network(STGAT) for travel demand prediction. STGAT uses a graph attention layer based on the Node2Vec graph embedding algorithm, two convolutional layers based on the Markov cluster algorithm, and a long short-term memory network to capture spatial-temporal dependencies. Experimental results on the NYC Taxi dataset and the Chengdu online car dataset demonstrate that STGAT achieved state-of-the-art performance compared to other baseline models.

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A Deep Reinforcement Learning Approach for Online Taxi Dispatching

A Deep Reinforcement Learning Approach for Online Taxi Dispa...

引用

International Conference on networking and network Applications (NaNA)

作者： YuBo Wang TaoYi Zhang ZhiCheng Wei Key Lab of Network and Information Security College of Computer and Cyber Security Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Security Hebei Normal University Shijiazhuang Hebei Province China

With the development of smart city transportation systems, developing reasonable dispatching strategies for idle ride-hailing vehicles has become an urgent research problem. In this paper, we address the short-sightedness issue of existing dispatching strategies and design a ride-hailing dispatching model based on deep reinforcement learning in the Markov decision process(MDP) framework to obtain long-term dispatching strategies with foresight. Firstly, we define the basic framework of agent and optimize the dispatching strategy of ride-hailing services by defining a metric that combines instant revenue with the demand situation of the next state in the action space as a reward function. Then, we construct an order-matching system simulator based on historical order data sets to facilitate interaction between the intelligent agent and the environment. Finally, based on the Bellman optimality equation, we approximate the value function using the Eval Net and Target Net, and train the model using Temporal Difference Learning. Simulation experiments demonstrate that this approach can increase driver income, provide more services to passengers, and effectively improve the operating efficiency of the transportation network.

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