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Asia Joint Conference on information security (Asia JCIS)

作者： Mariama Mbow Rodrigo Roman Takeshi Takahashi Kouichi Sakurai Department of Informatics Kyushu University Japan Network Information and Computer Security (NICS) Lab University of Malaga Spain National Institute of Information and Communications Technology Tokyo Japan

ISBN: (数字)9798350380149

ISBN: (纸本)9798350380156

network intrusion detection systems are essential tools used in cybersecurity to detect malicious traffic. They are used in the Internet of Things (IoT) to monitor the network traffic and identify infected IoT devices. Recently, machine learning and deep learning models have been increasingly adopted to enhance detection systems. However, these model-based detectors are vulnerable to adversarial attacks. This paper investigates the vulnerability of these machine learning-based NIDS models in the IoT environment. We propose a practical targeted black-box attack based on Generative Adversarial networks to generate adversarial DDoS and Mirai traffic that can evade the detector. Experimental results show that the proposed approach deceived the detector to misclassify the attack traffic as benign and achieved a 100% evasion success rate. Also, the proposed approach can be transferred to other models, achieving different levels of effectiveness.

关键词： Deep learning network intrusion detection Closed box Detectors Telecommunication traffic Generative adversarial networks Internet of Things Object recognition computer crime Monitoring

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Integration of MPC into Besu through an extended private transaction model

Integration of MPC into Besu through an extended private tra...

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Metaverse Computing, networking and Applications (MetaCom), IEEE International Conference on

作者： Daniel Morales Isaac Agudo Javier Lopez Department of Computer Science Network Information and Computer Security Lab ITIS Software Universidad de Málaga Málaga Spain

In the last few years we have seen many different approaches to incorporate privacy features to blockchains. In the area of cryptocurrencies that would normally mean protecting the identity of the owner of some funds, but there are other applications where privacy is even more important, especially in permissioned blockchains. Permissioned blockchain platforms, such as Hyperledger Besu or Hyperledger Fabric, already include the concept of private transactions, which essentially defines a sub-group of the blockchain where their participants share some private data. We want to go one step ahead and propose an extended model for private transactions where the different participants can have a separated view of the same transaction, allowing the integration of Multi-party Computation protocols in the blockchain. Our work extends Hyperledger Besu’s design for private transactions, offering better security properties and a finer grain customization. We cover two specific MPC examples, Private Set Intersection and Byzantine Fault-Tolerant Random Number Generation, and propose a mechanism to run them using smart contract interfaces.

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CYBERSHIELD: A Competitive Simulation Environment for Training AI in Cybersecurity

CYBERSHIELD: A Competitive Simulation Environment for Traini...

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International Conference on Internet of Things: Systems, Management and security

作者： José Álvaro Fernández Carrasco Iñigo Amonarriz Pagola Raúl Orduna Urrutia Rodrigo Román Vicomtech Basque Research and Technology Alliance (BRTA) Donostia San Sebastian Spain Network Information and Computer Security Lab University of Malaga Malaga Spain

ISBN: (数字)9798350366501

ISBN: (纸本)9798350366518

Simulation environments are crucial in cybersecurity for training AI algorithms, providing a secure and controlled space to explore the impact derived from diverse cyber threads. They offer a flexible platform for accelerated learning and development, especially useful to train Decision Support Systems in response contexts. This paper introduces CyberShield, an advanced simulation environment tailored for cybersecurity tasks, specifically focusing on supporting the implementation and evaluation of AI algorithms, with an emphasis on RL. CyberShield encompasses a comprehensive environment with multiple computers, each hosting various services with unique vulnerabilities. Within this environment, two opposing agents, defender and attacker, participate in a strategic battle, each equipped with distinct actions aimed at outsmarting the other. CyberShield is optimized for competitive multi-agent training using RL algorithms. The specifically adapted environment facilitates the exploration of novel strategies, thereby fostering the automation of defense and attack strategies. Taking inspiration from Capture The Flag (CTF) competitions, CyberShield offers a dynamic and realistic platform for refining advanced cybersecurity techniques, fostering innovation, and strengthening the resilience of AI algorithms against sophisticated cyber threats.

关键词： Training Technological innovation Heuristic algorithms Refining Focusing Space exploration Internet computer security Artificial intelligence Resilience

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Improved Design of des Algorithm Based on Symmetric Encryption Algorithm

Improved Design of des Algorithm Based on Symmetric Encrypti...

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2021 IEEE International Conference on Power Electronics, computer Applications, ICPECA 2021

作者： Yihan, Wang Yongzhen, Li Yanbian University Network Communication and Information Security Lab Dept. of Computer Science and Technology Yanji China

ISBN: (纸本)9781728190037

To address issues in security the threat posed to the DES algorithm by brute force cracking, as well as the low efficiency of the 3DES algorithm, I propose extending the length of the DES algorithm's key. First, change the block length of the DES algorithm from 64 bits to 128 bits and expand the key length of the DES algorithm to double length;secondly, iterative encryption is performed according to the new plaintext grouping and the expanded key length. The two groups are iteratively encrypted, and after every single iteration the two sets of R part data obtained from this round of encryption are exchanged, and then the next round of iteration;finally, the improved DES algorithm is compared with the DES algorithm and the 3DES algorithm by experiment. The results showing that the operating efficiency of the improved DES algorithm is better than 3DES algorithm. Therefore, the improved algorithm is proposed here greatly improves the security of the algorithm under the premise of ensuring operating efficiency and has good application value. © 2021 IEEE.

关键词： Cryptography

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Smart Energy Management System Using Machine Learning

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computers, Materials & Continua 2024年第1期78卷 959-973页

作者： Ali Sheraz Akram Sagheer Abbas Muhammad Adnan Khan Atifa Athar Taher M.Ghazal Hussam Al Hamadi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science COMSATS University IslamabadLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeSharjah1797United Arab Emirates Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia College of Engineering and IT University of DubaiDubaiUnited Arab Emirates

Energy management is an inspiring domain in developing of renewable energy ***,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and *** major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental *** energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user *** address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve *** inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy *** Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)*** IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource *** this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management *** proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

关键词： Intelligent energy management system smart cities machine learning

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Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignment 23

Attribute-Consistent Knowledge Graph Representation Learning...

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32nd ACM World Wide Web Conference, WWW 2023

作者： Li, Qian Guo, Shu Luo, Yangyifei Ji, Cheng Wang, Lihong Sheng, Jiawei Li, Jianxin School of Computer Science and Engineering Bdbc Beihang University Beijing China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security Ucas Beijing China Scse Beihang University Zhongguancun Lab Beijing China

ISBN: (纸本)9781450394161

The multi-modal entity alignment (MMEA) aims to find all equivalent entity pairs between multi-modal knowledge graphs (MMKGs). Rich attributes and neighboring entities are valuable for the alignment task, but existing works ignore contextual gap problems that the aligned entities have different numbers of attributes on specific modality when learning entity representations. In this paper, we propose a novel attribute-consistent knowledge graph representation learning framework for MMEA (ACK-MMEA) to compensate the contextual gaps through incorporating consistent alignment knowledge. Attribute-consistent KGs (ACKGs) are first constructed via multi-modal attribute uniformization with merge and generate operators so that each entity has one and only one uniform feature in each modality. The ACKGs are then fed into a relation-aware graph neural network with random dropouts, to obtain aggregated relation representations and robust entity representations. In order to evaluate the ACK-MMEA facilitated for entity alignment, we specially design a joint alignment loss for both entity and attribute evaluation. Extensive experiments conducted on two benchmark datasets show that our approach achieves excellent performance compared to its competitors. © 2023 ACM.

关键词： Knowledge graph

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An Efficient Multi-Dimensional Adaptive Federated Learning Algorithm

An Efficient Multi-Dimensional Adaptive Federated Learning A...

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International Conference on computer Supported Cooperative Work in Design

作者： Shujiang Xu Dehua Li Lianhai Wang Shuhui Zhang Wei Shao Qizheng Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan PR China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan PR China Quancheng Prov Lab Jinan Peoples R China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

With the rapid development of the Internet of Vehicles (IoV),Nevertheless, Federated Learning (FL) plays an increasingly crucial role in the secure data sharing within this field. Nevertheless, data in IoV settings frequently display Non-IID traits, which exerts a considerable influence on the efficiency and performance of FL. To enhance the model convergence speed and global performance under conditions of data imbalance and high heterogeneity, this paper presents a FL algorithm, named FedAE, that dynamically adjusts the local training epochs based on the changes in the AUC of model. FedAE assigns distinct local training epochs based on the quantity of training data in the initial stage and adaptively modifies them in subsequent stages in accordance with the dynamic changes in the AUC of both the global model and each client's model. Based on the performance disparity of the local training equipment, the algorithm establishes diverse training iterations to enhance the utilization of the local equipment and the training efficiency of the algorithm. Experimental results show that, compared to FedProx, the proposed approach significantly enhances model convergence efficiency and generalization ability. Specifically, FedAE reduces the loss by 26 %, demonstrating greater robustness, especially in scenarios with imbalanced data distributions.

关键词： Training Adaptation models Accuracy Federated learning Heuristic algorithms Data models Robustness Vehicle dynamics Convergence Internet of Vehicles

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Double-Blockchain Assisted Secure and Anonymous Data Aggregation for Fog-Enabled Smart Grid

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Engineering 2022年第1期8卷 159-169页

作者： Siguang Chen Li Yang Chuanxin Zhao Vijayakumar Varadarajan Kun Wang Jiangsu Key Lab of Broadband Wireless Communication and Internet of Things Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu Engineering Research Center of Communication and Network Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China Anhui Provincial Key Laboratory of Network and Information Security Anhui Normal UniversityWuhu 241000China School of Computer Science and Engineering Vellore Institute of TechnologyChennai 600127India Department of Electrical and Computer Engineering University of CaliforniaLos AngelesCA 90095USA

As a future energy system,the smart grid is designed to improve the efficiency of traditional power systems while providing more stable and reliable ***,this efficient and reliable service relies on collecting and analyzing users’electricity consumption data frequently,which induces various security and privacy *** address these challenges,we propose a double-blockchain assisted secure and anonymous data aggregation scheme for fog-enabled smart grid named ***,we design a three-tier architecture-based data aggregation framework by integrating fog computing and the blockchain,which provides strong support for achieving efficient and secure data collection in smart ***,we develop a secure and anonymous data aggregation mechanism with low computational overhead by jointly leveraging the Paillier encryption,batch aggregation signature and anonymous *** particular,the system achieves fine-grained data aggregation and provides effective support for power dispatching and price adjustment by the designed double-blockchain and two-level data ***,the superiority of the proposed scheme is illustrated by a series of security and computation cost analyses.

关键词： Blockchain Fog computing Homomorphic encryption Smart grid Anonymity

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

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

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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...

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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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