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Security Enhancement in Consumer Enterprises using Neural Nets within the SIEM Framework

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Security Enhancement in Consumer Enterprises using Neural Ne...

2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Arora, Saksham Kumar, Sudhakar Singh, Sunil K. Garg, Sahil Gupta, Brij B. Bansal, Shavi Chui, Kwok Tai Chandigarh College of Engineering and Technology Chandigarh India Department of Computer Science and Information Engineering Asia University Taiwan University of Economics and Human Sciences Warsaw Poland Insights2Techinfo India Hong Kong Metropolitan University Hong Kong

ISBN: (纸本)9798331521165

With internet popularity increasing every day and more physical activities being brought to the online spectrum, Security Information and Event Management (SIEM) tools have emerged as the main way to tackle any threat intrusions in the network infrastructure of organizations. Threats are evolving and becoming more advanced with the advancements in technology, and detecting and preventing the attacks need more human intervention than ever. To prevent these attacks, artificial intelligence and neural networks are a great way to tackle the increasing needs of human intervention. This article, thus, proposes a novel framework that uses a neural network named FeedForward to develop a model for anomaly and threat protection. It then prompts the user and takes input when a threat is detected. On confirmation of a threat, the feedback is sent back to the neural network to train itself better for future threats and keep itself updated. Therefore, this article proposes a supervised learning and retraining framework on the neural network. The dataset chosen has 763,144 instances that are divided into 80% training and 20% testing dataset. On running the neural network on the dataset, we achieved an astonishing 99.2% accuracy. © 2025 IEEE.

关键词： Feedforward neural networks

Uplink Rate-Splitting Multiple Access for Mobile Edge Computing with Short-Packet Communications

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arXiv 2025年

作者： Xu, Jiawei Zhang, Yumeng Xu, Yunnuo Clerckx, Bruno Imperial College London United Kingdom Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Electronics and Computer Engineering The Hong Kong University of Science and Technology 999077 Hong Kong School of Information Science and Engineering Shandong University Qingdao266237 China

In this paper, a Rate-Splitting Multiple Access (RSMA) scheme is proposed to assist a Mobile Edge Computing (MEC) system where local computation tasks from two users are offloaded to the MEC server, facilitated by uplink RSMA for processing. The efficiency of the MEC service is hence primarily influenced by the RSMA-aided task offloading phase and the subsequent task computation phase, where reliable and low-latency communication is required. For this practical consideration, short-packet communication in the Finite Blocklength (FBL) regime is introduced. In this context, we propose a novel uplink RSMA-aided MEC framework and derive the overall Successful Computation Probability (SCP) with FBL consideration. To maximize the SCP of our proposed RSMA-aided MEC, we strategically optimize: (1) the task offloading factor which determines the number of tasks to be offloaded and processed by the MEC server;(2) the transmit power allocation between different RSMA streams;and (3) the task-splitting factor which decides how many tasks are allocated to splitting streams, while adhering to FBL constraints. To address the strong coupling between these variables in the SCP expression, we apply the Alternative Optimization method, which formulates tractable subproblems to optimize each variable iteratively. The resultant non-convex subproblems are then tackled by Successive Convex Approximation. Numerical results demonstrate that applying uplink RSMA in the MEC system with FBL constraints can not only improve the SCP performance but also provide lower latency in comparison to conventional transmission scheme such as Non-orthogonal Multiple Access (NOMA). © 2025, CC BY.

关键词： Iterative methods

Generative Artificial Intelligence-Supported Pentesting: A Comparison between Claude Opus, GPT-4, and Copilot

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arXiv 2025年

作者： Martínez, Antonio López Cano, Alejandro Ruiz-Martínez, Antonio Department of Information and Communication Engineering University of Murcia Faculty of Computer Science Murcia Espinardo30100 Spain Legitec Avda. de los Rectores nº2-4 bajo Murcia Espinardo30100 Spain

The advent of Generative Artificial Intelligence (GenAI) has brought a significant change to our society. GenAI can be applied across numerous fields, with particular relevance in cybersecurity. Among the various areas of application, its use in penetration testing (pentesting) or ethical hacking processes is of special interest. In this paper, we have analyzed the potential of leading generic-purpose GenAI tools—Claude Opus, GPT-4 from ChatGPT, and Copilot—in augmenting the penetration testing process as defined by the Penetration Testing Execution Standard (PTES). Our analysis involved evaluating each tool across all PTES phases within a controlled virtualized environment. The findings reveal that, while these tools cannot fully automate the pentesting process, they provide substantial support by enhancing efficiency and effectiveness in specific tasks. Notably, all tools demonstrated utility;however, Claude Opus consistently outperformed the others in our experimental scenarios. © 2025, CC BY.

关键词： Economic and social effects

The graphene squeeze-film microphone

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arXiv 2024年

作者： Abrahams, M.P. Martinez, J. Steeneken, P.G. Verbiest, G.J. Department of Precision and Microsystems Engineering Delft University of Technology Mekelweg 2 Delft2628 CD Netherlands Multimedia Computing Group Intelligent Systems Department Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 XE Netherlands

Most microphones operate by detecting the sound-pressure induced motion of a membrane. In contrast, here we introduce a microphone that operates by monitoring the sound-pressure-induced modulation of the compressibility of air. By driving a graphene membrane at its resonance frequency, the gas, that is trapped in a squeeze-film beneath it, is compressed at high frequency. Since the stiffness of the gas film depend on the air pressure, the resonance frequency of the graphene is modulated by variations in sound pressure. We demonstrate that this squeeze-film microphone principle can be used to detect sound and music by tracking the membrane's resonance frequency using a phase-locked loop (PLL). Since the sound detection principle is different from conventional devices, the squeeze-film microphone potentially offers advantages like increased dynamic range, and a lower susceptibility to pressure-induced failure and vibration-induced noise. Moreover, it might be made much smaller, as demonstrated by the microphone in this work that operates using a circular graphene membrane with an area that is more than a factor 1000 smaller than that of MEMS microphones. Copyright © 2024, The Authors. All rights reserved.

关键词： Graphene

StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems

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IEEE Transactions on Network and Service Management 2025年

作者： Bouzinis, Pavlos S. Radoglou-Grammatikis, Panagiotis Makris, Ioannis Lagkas, Thomas Argyriou, Vasileios Papadopoulos, Georgios Th Sarigiannidis, Panagiotis Karagiannidis, George K. MetaMind Innovation P.C. Kila Kozani50100 Greece University of Western Macedonia Department of Electrical and Computer Engineering Campus ZEP Kozani50100 Greece thae K3Y Ltd William Gladstone 31 Sofia1000 Bulgaria Democritus University of Thrace Kavala Campus Department of Computer Science Kavala65404 Greece Department of Networks and Digital Media Kingston University London KT1 2EE United Kingdom Harokopio University of Athens Department of Informatics and Telematics Athens17778 Greece Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki54124 Greece

Federated learning (FL) enables devices to collaboratively build a shared machine learning (ML) or deep learning (DL) model without exposing raw data. Its privacy-preserving nature has made it popular for intrusion detection systems (IDS) in the field of cybersecurity. However, data heterogeneity across participants poses challenges for FL-based IDS. This paper proposes statistical averaging (StatAvg) method to alleviate non-independently and identically (non-iid) distributed features across local clients’ data in FL. In particular, StatAvg allows the FL clients to share their individual local data statistics with the server. These statistics include the mean and variance of each client’s feature vector. The server then aggregates this information to produce global statistics, which are shared with the clients and used for universal data normalization, i.e., common scaling of the input features by all clients. It is worth mentioning that StatAvg can seamlessly integrate with any FL aggregation strategy, as it occurs before the actual FL training process. The proposed method is evaluated against well-known baseline approaches that rely on batch and layer normalization, such as FedBN, and address the non-iid features issue in FL. Experiments were conducted using the TON-IoT and CIC-IoT-2023 datasets, which are relevant to the design of host and network IDS, respectively. The experimental results demonstrate the efficiency of StatAvg in mitigating non-iid feature distributions across the FL clients compared to the baseline methods, offering a gain in IDS accuracy ranging from 4% to 17%. © 2004-2012 IEEE.

关键词： Federated learning

Energy Landscape Shaping for Robust Control of Atoms in Optical Lattices

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arXiv 2025年

作者： Weidner, C.A. O'Neil, S.P. Jonckheere, E.A. Langbein, F.C. Schirmer, S.G. Quantum Engineering Technology Laboratories H. H. Wills Physics Laboratory Department of Electrical and Electronic Engineering University of Bristol BristolBS8 1FD United Kingdom Department of Electrical Engineering and Computer Science United States Military Academy West PointNY10996 United States Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90007 United States School of Computer Science and Informatics Cardiff University CardiffCF24 4AG United Kingdom Faculty of Science & Engineering Physics Swansea University SwanseaSA2 8PP United Kingdom

Robust quantum control is crucial for realizing practical quantum technologies. Energy landscape shaping offers an alternative to conventional dynamic control, providing theoretically enhanced robustness and simplifying implementation for certain applications. This work demonstrates the feasibility of robust energy landscape control in a practical implementation with ultracold atoms. We leverage a digital mirror device (DMD) to shape optical potentials, creating complex energy landscapes. To achieve a desired objective, such as efficient quantum state transfer, we formulate a novel hybrid optimization approach that effectively handles both continuous (laser power) and discrete (DMD pixel activation) control parameters. This approach combines constrained quasi-Newton methods with surrogate models for efficient exploration of the vast parameter space. Furthermore, we introduce a framework for analyzing the robustness of the resulting control schemes against experimental uncertainties. By modeling uncertainties as structured perturbations, we systematically assess controller performance and identify robust solutions. We apply these techniques to maximize spin transfer in a chain of trapped atoms, achieving high-fidelity control while maintaining robustness. Our findings provide insights into the experimental viability of controlled spin transfer in cold atom systems. More broadly, the presented optimization and robustness analysis methods apply to a wide range of quantum control problems, offering a toolkit for designing and evaluating robust controllers in complex experimental settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Robust control

Application-dependent support of its-g5 car-to-car communication by selected usage of lte-v2x resources 12

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Application-dependent support of its-g5 car-to-car communica...

12th GMM Symposium on Automotive meets Electronics, AmE 2021

作者： Stellwagen, Jochen Deegener, Matthias Kuhn, Michael Frankfurt University of Applied Sciences Facility 2: Computer Science and Engineering Frankfurt a.M. Germany Darmstadt University of Applied Sciences Department of Electrical Engineering Darmstadt Germany

ISBN: (纸本)9783800754885

Car-to-car communication is besides autonomous driving and e-mobility one of today’s most important topics in automotive research. Currently, two transmission technologies are available for safety-critical applications in vehicle-to-vehicle communication: WIFI-based ITS-G5 and cellular-based LTE-V2X [1]. In this article ITS-G5 is used as the primary transmission technology to support safety-critical applications within a wide range of traffic situations by exchanging information between individual vehicles. The resources of the LTE-V2X communication will be used to support the WIFI-based communication in case a reliable transmission via ITS-G5 is not or only partially possible. In a simulation environment several test series were developed to investigate different traffic scenarios, use cases and equipment rates for both transmission technologies. This will show how often and in which situations the resources of LTE-V2X are used. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Vehicle to vehicle communications

Deep Reinforcement Learning for Edge-DASH-Based Dynamic Video Streaming

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Deep Reinforcement Learning for Edge-DASH-Based Dynamic Vide...

2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Naseh, David Bozorgchenani, Arash Tarchi, Daniele University of Bologna Department of Electrical Electronic and Information Engineering 'Guglielmo Marconi' Bologna40136 Italy School of Computer Science University of Leeds LeedsLS2 9JT United Kingdom University of Florence Department of Information Engineering Firenze50139 Italy

ISBN: (纸本)9798350368369

Dynamic Adaptive Streaming over HTTP (DASH) is a promising solution to enhance the Quality of Experience (QoE) of mobile video services. In this paper, we consider an Edge-DASH scenario where two problems of Bitrate Allocation (BrA) and user-to-server allocation (USA) have been jointly formulated. Then, we exploit Deep Reinforcement Learning (DRL) algorithm to solve the USA problem and select the streaming point for users, which can be streaming from the Edge, Macro layer or cloud, and deliver the users the most appropriate bitrate respecting the QoE by solving the BrA problem. In the simulation results, we have demonstrated that our Deep Deterministic Policy Gradient (DDPG) outperforms the traditional solution in terms of bitrate allocation. © 2025 IEEE.

关键词： Deep reinforcement learning

Robust Distributed Secondary Control for DC Microgrids Enhancing Stability and Communication Delay Tolerance

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IEEE Transactions on Energy Conversion 2024年

作者： Hamad, Basil Al-Durra, Ahmed Zeineldin, Hatem Mohamed, Yasser Abdel-Rady I. University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G2V4 Canada Khalifa University of Science and Technology Advanced Power and Energy Center Ee Department Abu Dhabi127788 United Arab Emirates Cairo University Electric Power Engineering Department Giza12613 Egypt

Accurate power sharing and global voltage recovery are common secondary control objectives in DC microgrids (DCMGs). Distributed control outperforms centralized as it allows for plug-and-play capability and operates effectively with low-bandwidth communication. However, it remains sensitive to communication delays, compromising DCMG stability. This paper proposes an enhanced DCMG control architecture, improving its endurance against delays. Secondary control modifies droop nominal voltage to achieve its objectives through a PI controller. The PI gains have direct influences on stability and delay toleration. However, scheduling PI gains to counteract the delay comes with sluggish convergence. The proposed control hierarchy introduces derivative controllers in its structure to increase its degree of freedom. By broadening the PI gains range, communication delays are effectively addressed, thus offering more ample tuning choices that accelerate convergence. The small-signal linearized model is derived and the direct frequency domain method is used to accurately obtain the delay margin characterizing DCMG stability. Due to the transcendental delay terms in the characteristic equation, the roots' damping cannot be obtained. Thus, Pade approximation is engaged in obtaining a manageable number of roots facilitating dominant roots assessment. The proposed control hierarchy has been verified through a Controller-in-the-Loop (CIL) setup via the OPAL-RT environment. © 1986-2012 IEEE.

关键词： Frequency domain analysis