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2023 International Conference on networking and network Applications, NaNA 2023

作者： Wang, YuBo Zhang, TaoYi Wei, ZhiCheng Key Lab of Network and Information Security China College of Computer and Cyber Security China Hebei Prov. Eng. Res. Center for Supply Chain Big Data Analytics Security Hebei Normal University Hebei Province Shijiazhuang China

ISBN: (纸本)9798350327380

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. © 2023 IEEE.

关键词： Reinforcement learning

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Taxi Station Location Model Based on Spatio-Temporal Demand Cube

Taxi Station Location Model Based on Spatio-Temporal Demand ...

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2023 International Conference on networking and network Applications, NaNA 2023

作者： Gao, LuLu Li, XiaoNan Wei, ZhiCheng Key Lab of Network and Information Security Hebei Province Shijiazhuang China College of Computer and Cyber Security Hebei Province Shijiazhuang China Hebei Normal University Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Security Hebei Province Shijiazhuang China

ISBN: (纸本)9798350327380

With the widespread use of mobile apps, empty taxis can easily receive new orders without having to roam around. Therefore, it is necessary to plan taxi stations where empty taxis can park. In the location selection problem, the spatial and temporal distribution of passenger demand needs to be considered to ensure that the selected location can meet the actual needs of passengers. In this paper, a Spatio-Temporal Cube Demand Coverage Model (STC-DCM) is proposed for taxi station location selection. Firstly, a spatiotemporal demand cube is constructed to obtain fine-grained user demands based on time and space, and genetic algorithm is used to maximize the demand coverage rate. Then, the final location selection results are obtained using the fusion module and adjustment optimization module, where the fusion module integrates multiple location selection solutions using ensemble learning method. Experimental results show that our proposed model can better cover dynamic passenger demand compared to traditional models. © 2023 IEEE.

关键词： Genetic algorithms

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A Novel Meta-Hierarchical Active Inference Reinforcement Learning Approach for QoS-Driven Resource Allocation in Dynamic Clouds

A Novel Meta-Hierarchical Active Inference Reinforcement Lea...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Xian, Peijie He, Ying Yu, F. Richard Du, Jianbo Shenzhen Univ. College of Computer Science and Software Engineering Shenzhen China Xi'an Univ. of Posts & Telecomm. Shaanxi Key Lab. of Information Comm. Network and Security Shaanxi Xi'an China Carleton Univ. School of Information Technology OttawaON Canada

ISBN: (纸本)9798350351255

The cloud computing environment is highly dynamic due to a variety of external factors such as seasonal changes, market trends, and social events. In this case, tenant behavior patterns exhibit significant variability. Therefore, the cloud computing resource allocation model must continuously adapt to these changes. To this end, we propose an adaptive algorithm for dynamic cloud resource allocation based on meta-hierarchical active inference reinforcement learning (MHAIRL). The algorithm combines active inference with meta-hierarchical reinforcement learning. It improves the overall performance of the algorithm, as well as quickly adapts to environmental changes, and improves the generalization performance. In addition, we design a novel polling scheduling framework combined with long short-term memory (LSTM) network. The framework ensures scheduling fairness and flexibility while greatly reducing the state and action space dimensions of the agent. Extensive simulation results show that our method outperforms baseline algorithms in quality of service (QoS) metrics and significantly improves system performance in highly dynamic cloud resource allocation. © 2024 IEEE.

关键词： Active learning

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An Efficient Unsupervised Domain Adaptation Deep Learning Model for Unknown Malware Detection 1

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4th EAI International Conference on security and Privacy in New Computing Environments, SPNCE 2021

作者： Wang, Fangwei Chai, Guofang Li, Qingru Wang, Changguang Key Lab of Network and Information Security of Hebei Province Hebei Normal University Shijiazhuang050024 China College of Computer and Cyberspace Security Hebei Normal University Shijiazhuang050024 China

ISBN: (数字)9783030967918

ISBN: (纸本)9783030967901

Emerging malware and zero-day vulnerabilities present new challenges to malware detection. Currently, numerous proposed malware detection approaches are based on supervised learning. However, these methods rely on a large amount of labeled data, which is usually difficult to obtain. Moreover, since the newly emerging malware has a different data distribution from the original training samples, the detection performance of the model will degrade when facing new malware. To solve the problems mentioned above, this paper proposes an unsupervised domain adaptation-based malware detection method to align the joint distribution of known malware and unknown malware. First, the distribution divergence between the source and target domain is minimized by adversarial learning to learn shared feature representations. Second, to further obtain semantic information of unlabeled target domain data, this paper reduces the class-level distribution divergence by aligning the class centers of labeled source data and pseudo-labeled target data. To improve the ability of the model for extracting feature information, this paper mainly uses a residual network with a self-attention mechanism as a pre-trained model. Extensive experiments are conducted on two datasets. The experimental results illustrate that the proposed method outperforms the state-of-art detection methods with an accuracy of 95.63% and a recall of 95.30% in detecting unknown malware. © 2022, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Semantics

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An IoMT based Ensemble Classification Framework to Predict Treatment Response in Hepatitis C Patients

An IoMT based Ensemble Classification Framework to Predict T...

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2022 International Conference on Business Analytics for Technology and security, ICBATS 2022

作者： Ghazal, Taher M. Abbas, Sagheer Ahmad, Munir Aftab, Shabib Faculty of Information Science and Technology Ukm Center for Cyber Security Network and Communication Technology Lab Malaysia NCBAandE School of Computer Science Lahore Pakistan

ISBN: (纸本)9781665409209

Hepatitis C is considered a deadly disease as mortality rate in the patients suffering from this disease is very high, if not properly treated. This research proposes an IOMT based ensemble classification framework for the prediction of treatment response of a drug: 'L-Ornithine-L-Aspartate' (LOLA) in hepatitis c patients. The treatment with this drug is called LOLA therapy which is significant for the patients to recover from the effects of hepatitis c disease. To implement the proposed framework, we used the medical data of hepatitis c patients who are being treated with LOLA therapy. The proposed framework integrates the predictive accuracy of two supervised machine learning techniques: Support Vector Machine (SVM) and Decision Tree (DT) by using Voting ensemble technique. The results reflect that the proposed ensemble framework performed well as compared to other published techniques on the prediction of treatment response for hepatitis c disease. © 2022 IEEE.

关键词： Diseases

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Uplink Control information Based Clustered Access and Discontinuous Reception Management for mMTC

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IEEE Communications Letters 2025年第6期29卷 1380-1384页

作者： Wang, Shao-Di Wang, Chang-Long Wang, Hui-Ming Zhou, Feng Leung, Victor C. M. Xidian University Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education Xi’an710071 China Xi’an Jiaotong University School of Information and Communication Engineering Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an710049 China Shenzhen MSU BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Uplink control information (UCI) and discontinuous reception (DRX) play important roles for massive machine type communication (mMTC). Despite their standalone significance, a conspicuous gap exists in comprehensively exploring the synergy between UCI and DRX concerning both energy conservation and latency reduction for mMTC. In this letter, we propose a UCI-based clustered access and DRX management (UCADM) scheme that strikes a balance between these two critical performance metrics, reducing energy usage while preserving low latency in uplink-downlink interaction for mMTC. Specifically, we present a strictly feasible solution for the proposed UCADM scheme by using the spectral radius and the constraint projection, which can effectively solve DRX switching operations as indicated by scheduling information from UCI. Simulation results confirm the effectiveness of our scheme. © 1997-2012 IEEE.

关键词： information management

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Early Detection of Autism in Children Using Transfer Learning

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Intelligent Automation & Soft Computing 2023年第4期36卷 11-22页

作者： Taher M.Ghazal Sundus Munir Sagheer Abbas Atifa Athar Hamza Alrababah Muhammad Adnan Khan School of Information Technology Skyline University CollegeSharjah1797UAE Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and Technology Universiti Kebangsaan Malaysia43600Malaysia School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Lahore Garrison University Lahore54000Pakistan Department of Computer Science COMSATS University Islamabad–Lahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnamGyeonggido13120Korea

Autism spectrum disorder(ASD)is a challenging and complex neurodevelopment syndrome that affects the child’s language,speech,social skills,communication skills,and logical thinking *** early detection of ASD is essential for delivering effective,timely *** facial features such as a lack of eye contact,showing uncommon hand or body movements,bab-bling or talking in an unusual tone,and not using common gestures could be used to detect and classify ASD at an early *** study aimed to develop a deep transfer learning model to facilitate the early detection of ASD based on facial fea-tures.A dataset of facial images of autistic and non-autistic children was collected from the Kaggle data repository and was used to develop the transfer learning AlexNet(ASDDTLA)*** model achieved a detection accuracy of 87.7%and performed better than other established ASD detection ***,this model could facilitate the early detection of ASD in clinical practice.

关键词： Autism spectrum disorder convolutional neural network loss rate transfer learning AlexNet deep learning

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Evading IoT Intrusion Detection Systems with GAN

Evading IoT Intrusion Detection Systems with GAN

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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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