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Reduce Low-Frequency Distributed Denial of Service Threats by Combining Deep and Active Learning 1

9th International Conference on Cyber Security, Privacy in Communication Networks, ICCS 2023

作者： Shukla, Aditya Kumar Sharma, Ashish Sengar, Sandeep Singh Department of Computer Engineering and Applications GLA University NH#2 Delhi Mathura Highway Post Ajhai Uttar Pradesh Mathura India Department of Computer Science Cardiff School of Technologies Cardiff Metropolitan University Cardiff United Kingdom

ISBN: (数字)9789819739738

ISBN: (纸本)9789819739721

This study introduces substantial contributions to the field of Low-Rate Detection of Distribution Denial of Service (DDoS) Attacks, leveraging convolutional neural networks (CNNs) with an attention mechanism and incorporating active learning with semi-labelled data. These contributions collectively enhance the accuracy, efficiency, and adaptability of DDoS detection systems. Additionally, the study introduces active learning strategies into the learning process. By actively selecting instances for manual labelling, the study reduces the burden of extensive manual labelling efforts and enhances the model’s scalability. In the dynamic realm of cybersecurity, where threats evolve rapidly, active learning ensures the model’s adaptability with minimal human intervention. Furthermore, this research addresses the challenge of scarce labelled data in real-world cybersecurity contexts. By harnessing semi-labelled data efficiently and pairing it with active learning, the study streamlines the detection and defence against DDoS assaults with low attack rates. This is particularly relevant in situations where procuring an abundance of labelled data is impractical or cost-prohibitive. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Active learning Attention mechanism CNNs Cybersecurity Low-rate DDoS detection Semi-labelled data Threat mitigation

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The Role of Machine Learning in Equipping Students with Emerging Opportunities

The Role of Machine Learning in Equipping Students with Emer...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Sengar, Alok Singh Bahadure, Nilesh Bhaskarrao Mishra, Amit Kumar Chaudhari, Vaibhav Pandey, Neeraj Kumar Dumka, Ankur Noida International University School of Sciences Department of Computer Science Greater Noida India Symbiosis Institute of Technology Nagpur Campus Pune India Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India Nutanix Technologies India Pvt Ltd Technical Staff-2 Karnataka Bengaluru India Graphic Era Deemed to be University Dept. of Computer Science & Engineering Dehradun India Women Institute of Technology Dept. of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350394412

The growth of the machine learning (ML) process is crucial to evaluate the basic influence on the growth of students' life. Main advantage of using ML in education is that it can be used to generate individualized learning programs that meet the desires of each learner. Large-scale information on student performance, learning preferences, and styles may be examined by ML algorithms to build personalized learning paths. This level of personalization accounts for each learner's unique learning style and pace to ensure that they receive just the appropriate amount of challenge and support. For instance, adaptive learning platforms with machine learning abilities can maximize student engagement and learning efficiency by dynamically modifying the material trouble in response to real-time student performance. Another way that machine learning is being incorporated into education is through the presence of relevant, up-to-date knowledge to the syllabus that tracks business trends. The need for expertise in fields like data analytics, cybersecurity, and artificial intelligence is rising as the labor market changes. Educational institutions may identify growing skill requirements and modify their courses accordingly by regularly analyzing labor market data with the use of ML. By ensuring that students possess the abilities that employers value, they increase their competitiveness and adaptability in the labor market. There are some different prospectuses that allows determining the basic impact of the motivated student's aspects to the improvement of their studies. In addition, maintaining appropriate ideas in the machine learning process allows for the organization of some better approaches where the student's activity is analyzed appropriately. All of the opportunities defined in the machine learning process are determined basic approach which is described the student's improvement in their growth process. There is the basic approach where the impact of the machine learni

关键词： Federated learning

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Collaborative Computing in Non-Terrestrial Networks: A Multi-Time-Scale Deep Reinforcement Learning Approach

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IEEE Transactions on Wireless Communications 2024年第5期23卷 4932-4949页

作者： Cao, Yang Lien, Shao-Yu Liang, Ying-Chang Niyato, Dusit Shen, Xuemin Southwest Jiaotong University School of Information Science and Technology Chengdu611756 China Institute of Intelligent Systems National Yang Ming Chiao Tung University Tainan City711 Taiwan Chengdu611731 China Nanyang Technological University School of Computer Science and Engineering Jurong West639798 Singapore University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Constructing earth-fixed cells with low-earth orbit (LEO) satellites in non-terrestrial networks (NTNs) has been the most promising paradigm to enable global coverage. The limited computing capabilities on LEO satellites however render tackling resource optimization within a short duration a critical challenge. Although the sufficient computing capabilities of the ground infrastructures can be utilized to assist the LEO satellite, different time-scale control cycles and coupling decisions between the space- and ground-segments still obstruct the joint optimization design for computing agents at different segments. To address the above challenges, in this paper, a multi-time-scale deep reinforcement learning (DRL) scheme is developed for achieving the radio resource optimization in NTNs, in which the LEO satellite and user equipment (UE) collaborate with each other to perform individual decision-making tasks with different control cycles. Specifically, the UE updates its policy toward improving value functions of both the satellite and UE, while the LEO satellite only performs finite-step rollout for decision-makings based on the reference decision trajectory provided by the UE. Most importantly, rigorous analysis to guarantee the performance convergence of the proposed scheme is provided. Comprehensive simulations are conducted to justify the effectiveness of the proposed scheme in balancing the transmission performance and computational complexity. © 2002-2012 IEEE.

关键词： Resource allocation

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Multi-Modal Transformer and Reinforcement Learning-Based Beam Management

IEEE Networking Letters

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IEEE Networking Letters 2024年第4期6卷 222-226页

作者： Ghassemi, Mohammad Zhang, Han Afana, Ali Sediq, Akram Bin Erol-Kantarci, Melike University of Ottawa School of Electrical Engineering and Computer Science OttawaONK1N 6N5 Canada Ericsson Inc Research and Development Department OttawaONK2K 2V6 Canada

Beam management is an important technique to improve signal strength and reduce interference in wireless communication systems. Recently, there has been increasing interest in using diverse sensing modalities for beam management. However, it remains a big challenge to process multi-modal data efficiently and extract useful information. On the other hand, the recently emerging multi-modal transformer (MMT) is a promising technique that can process multi-modal data by capturing long-range dependencies. While MMT is highly effective in handling multi-modal data and providing robust beam management, integrating reinforcement learning (RL) further enhances their adaptability in dynamic environments. In this letter, we propose a two-step beam management method by combining MMT with RL for dynamic beam index prediction. In the first step, we divide available beam indices into several groups and leverage MMT to process diverse data modalities to predict the optimal beam group. In the second step, we employ RL for fast beam decision-making within each group, which in return maximizes throughput. Our proposed framework is tested on a 6G dataset. In this testing scenario, it achieves higher beam prediction accuracy and system throughput compared to both the MMT-only based method and the RL-only based method. © 2019 IEEE.

关键词： Decision making

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RedTops: real-time energy-aware dynamic task offloading via federated mountain gazelle optimisation in SDN-enhanced edge computing

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Neural Computing and Applications 2025年 1-39页

作者： Al Aghbari, Zaher Khedr, Ahmed M. Ahmed, Naveed Girija, Shini Baker, Thar Department of Computer Science University of Sharjah Sharjah United Arab Emirates School of Architecture Technology and Engineering University of Brighton BrightonBN2 4GJ United Kingdom

Offloading computational tasks is vital for real-time applications on mobile devices with limited resources. Mobile edge computing (MEC) is deemed a solution that puts computational resources closer to users. Nevertheless, there are many associated concerns during the offloading procedure (i.e., privacy, delay, and high energy consumption). Federated learning (FL) has been considered a solution to address MEC’s data privacy issues;however, it comes with its own resource consumption issues. To address these issues, this paper proposes a distributed learning paradigm inspired by FL. We propose an optimisation technique for offloading computational tasks that aims to reduce both total delay and energy consumption by using the mountain gazelle optimisation algorithm, which shows it can reduce both delay and energy consumption in dynamic situations. Additionally, an improved variant known as the improved mountain gazelle optimiser is integrated into a distributed SDN controller architecture to create an offloading policy model for optimal edge node selection. We also present a new SDN-enabled edge computing architecture that achieves the best task distribution through task offloading using federated mountain gazelle optimisation (RedTops). Energy usage, delay, and bandwidth are considered by RedTops, which successfully addresses high training costs, dependability issues, and privacy concerns in MEC. Based on the outcomes of five extensive simulations, RedTops is more energy-efficient and faster at completing tasks than four state-of-the-art offloading methods (DDLO, DROO, DRL without TL and SDN, and DTRL). © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Federated learning

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Enhancing WSN Performance with DRL-CaCC: An Adaptive Cache-Aware Congestion Control Mechanism 15

Enhancing WSN Performance with DRL-CaCC: An Adaptive Cache-A...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Alipio, Melchizedek Bures, Miroslav De La Salle University Department of Electronics and Computer Engineering 2401 Taft Avenue Manila Malate1004 Philippines Faculty of Electrical Engineering Czech Technical University in Prague Department of Computer Science Karlovo namesti 13 Prague 212135 Czech Republic

ISBN: (纸本)9798350364637

Traditionally, rule-based is the predominant approach in Wireless Sensor Networks (WSN) communication protocols for managing caching and congestion control that relies on explicitly defined, unchanging models. A growing trend has recently been toward incorporating adaptive methods that leverage machine learning (ML) and deep learning (DL) during network congestion conditions. However, the design of an adaptive cache-aware congestion control mechanism in WSN using Deep Reinforcement Learning (DRL) is yet to be explored. Hence, this work developed a DRL-based adaptive cache-aware congestion control mechanism called DRL-CaCC to alleviate WSN during congestion scenarios. DRL-CaCC employs interme-diate caching parameters as its state space and adaptively moves the congestion window as its action space. It aims to find the optimal congestion window movement to avoid further network congestion while ensuring maximum cache utilization. Results show that DRL-CaCC achieved an average improvement gain from 10% to 40% compared to two other cache-based congestion control mechanisms in terms of cache utilization, throughput, and end-to-end delay metrics. © 2024 IEEE.

关键词： Deep reinforcement learning

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Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation

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IEEE Transactions on Medical Imaging 2023年第11期42卷 3323-3335页

作者： Li, Zeju Kamnitsas, Konstantinos Dou, Qi Qin, Chen Glocker, Ben Imperial College London BioMedIA Group Department of Computing LondonSW7 2AZ United Kingdom University of Oxford Department of Engineering Science OxfordOX3 7DQ United Kingdom University of Birmingham School of Computer Science BirminghamB15 2TT United Kingdom The Chinese University of Hong Kong Department of Computer Science and Engineering Shatin999077 Hong Kong Imperial College London I-X and the Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom

This paper presents an effective and general data augmentation framework for medical image segmentation. We adopt a computationally efficient and data-efficient gradient-based meta-learning scheme to explicitly align the distribution of training and validation data which is used as a proxy for unseen test data. We improve the current data augmentation strategies with two core designs. First, we learn class-specific training-time data augmentation (TRA) effectively increasing the heterogeneity within the training subsets and tackling the class imbalance common in segmentation. Second, we jointly optimize TRA and test-time data augmentation (TEA), which are closely connected as both aim to align the training and test data distribution but were so far considered separately in previous works. We demonstrate the effectiveness of our method on four medical image segmentation tasks across different scenarios with two state-of-the-art segmentation models, DeepMedic and nnU-Net. Extensive experimentation shows that the proposed data augmentation framework can significantly and consistently improve the segmentation performance when compared to existing solutions. Code is publicly available at https://***/ZerojumpLine/JCSAugment. © 1982-2012 IEEE.

关键词： Image segmentation

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Building Consensus in Group Decision-Making with Intuitionistic Reciprocal Preference Relations: An Analysis of Various Protocols of Information Granularity Distribution

Building Consensus in Group Decision-Making with Intuitionis...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Gonzalez-Quesada, Juan Carlos Cabrerizo, Francisco Javier Herrera-Viedma, Enrique Pedrycz, Witold Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Department of Computer Science and Artificial Intelligence Granada18071 Spain University of Alberta Department of Electrical & Computer Engineering EdmontonABT6R 2V4 Canada

ISBN: (纸本)9781665410205

On the one hand, to model experts' preferences in group decision-making, intuitionistic reciprocal preference relations have widely been used because they allow for accommodating hesitation degrees, which are inherent to all decision-making processes. On the other hand, an optimization of information granularity distribution has recently been applied to establish consensus during group decision-making processes. Concretely, a symmetric and uniform distribution of information granularity has been considered for intuitionistic reciprocal preference relations. However, there exist other protocols of information granularity distribution that could be used. Therefore, we aim to analyze all the information granularity distribution protocols and determine their effectiveness in building consensus through intuitionistic reciprocal preference relations. The performance of the different protocols is discussed by conducting some numerical experiments that help provide insights into the effectiveness of the protocols to build consensus. © 2024 IEEE.

关键词： Information granules

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Optimal Link Configuration for Covert Heterogeneous Wireless Networks with Power Constraints 59

Optimal Link Configuration for Covert Heterogeneous Wireless...

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59th Annual Conference on Information sciences and Systems, CISS 2025

作者： Gillani, Amna Lorenzo, Beatriz Ghaderi, Majid Dagefu, Fikadu Goeckel, Dennis University of Massachusetts Amherst Department of Electrical and Computer Engineering AmherstMA01003 United States University of Calgary Department of Computer Science CalgaryABT2N 1N4 Canada AdelphiMD20783 United States

ISBN: (纸本)9798331513269

Heterogeneous Networks (HetNets) leverage multiple communication interfaces (e.g., Wi-Fi, cellular, Bluetooth) seamlessly integrated to enhance connectivity and capacity across diverse environments. These capabilities can be advantageous for covert communication, where the objective is to ensure undetectable transmission in the presence of an adversary. This work investigates optimal power allocation strategies for covert communication within HetNets under maximum power constraints. We propose a polynomial-time (m2) algorithm for optimal link configuration by allocating power across multiple modes. Next, we address network-level covertness by allocating the end-to-end covertness constraint across links in a two-hop system that maximizes throughput. Leveraging the link configuration approach, we derive the optimal power allocation for each mode and link, ensuring the overall transmission remains undetectable. Numerical results demonstrate that the proposed method outperforms existing single-link and two-hop approaches, achieving higher efficiency and security in covert communication across HetNets. © 2025 IEEE.

关键词： Wireless local area networks (WLAN)

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Boost DC-DC Converter with Non-dissipative Snubber

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IEEJ Transactions on Electrical and Electronic engineering 2024年第11期19卷 1858-1878页

作者： Yau, Yeu-Torng Department of Ph.D. Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung No.57 Sec. 2 Zhongshan Rd. Taiping Dist Taichung41170 Taiwan

To provide a hold-up time function in DC-DC supplies for cell site stations or data centers, using a boost converter with a bulk output capacitor as a front-end converter stage is a simple and highly cost-effective solution. However, conventional boost converters with a hard-switching operation result in low conversion efficiency and increased electromagnetic interference emissions. In this paper, a boost converter with a new non-dissipative snubber is proposed that only requires two additional diodes, an inductor, and a multilayer ceramic capacitor. The main feature of the proposed snubber is that the components are not located on the main power processing path. This means they only require low ratings, improving cost-effectiveness. A prototype converter is analyzed, realized, and applied to a 300 W redundant DC-DC power supply module for a 5G cell base station. © 2024 The Author(s). IEEJ Transactions on Electrical and Electronic engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. © 2024 The Author(s). IEEJ Transactions on Electrical and Electronic engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

关键词： Boost converter

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