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IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 623-643页

作者： Farzaan, Mohammed A. M. Ghanem, Mohamed Chahine El-Hajjar, Ayman Ratnayake, Deepthi N. PRIAM CYBER AI Limited London United Kingdom University of Liverpool Cybersecurity Institute Liverpool United Kingdom Cyber Security Research Centre London Metropolitan University United Kingdom University of Westminster Cyber Security Research Group London United Kingdom Cybersecurity and Computing Systems Research Group University of Hertfordshire Hatfield United Kingdom

The growing complexity and frequency of cyber threats in cloud environments call for innovative and automated solutions to maintain effective and efficient incident response. This study tackles this urgent issue by introducing a cutting-edge AI-driven cyber incident response system specifically designed for cloud platforms. Unlike conventional methods, our system employs advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques to provide accurate, scalable, and seamless integration with platforms like Google Cloud and Microsoft Azure. Key features include an automated pipeline that integrates Network Traffic Classification, Web Intrusion Detection, and Post-Incident Malware Analysis into a cohesive framework implemented via a Flask application. To validate the effectiveness of the system, we tested it using three prominent datasets: NSLKDD, UNSW-NB15, and CIC-IDS-2017. The Random Forest model achieved accuracies of 90%, 75%, and 99%, respectively, for the classification of network traffic, while it attained 96% precision for malware analysis. Furthermore, a neural network-based malware analysis model set a new benchmark with an impressive accuracy rate of 99%. By incorporating deep learning models with cloud-based GPUs and TPUs, we demonstrate how to meet high computational demands without compromising efficiency. Furthermore, containerisation ensures that the system is both scalable and portable across a wide range of cloud environments. By reducing incident response times, lowering operational risks, and offering cost-effective deployment, our system equips organizations with a robust tool to proactively safeguard their cloud infrastructure. This innovative integration of AI and containerised architecture not only sets a new benchmark in threat detection but also significantly advances the state-of-the-art in cybersecurity, promising transformative benefits for critical industries. This research makes a significant contribution to the field of AI

关键词： Computer forensics

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Ethical discussions for autonomous robotic surgeries 2

Ethical discussions for autonomous robotic surgeries

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2nd International Conference on Industrial IoT, Big Data, and Supply Chain 2021

作者： Chang, Victor Kamanooru, Mohana R. Darko, Gilbert Tsibu Cybersecurity Information Systems and AI Research Group School of Computing Teesside University Middlesbrough United Kingdom

ISBN: (数字)9781510651333

ISBN: (纸本)9781510651326

Artificial Intelligence is extensively applied and evolving in every field with emerging new techniques and approaches. Similarly, in the health care sector, robotic surgeries are expanding too. The significant leap from the fourth generation to the fifth generation of robots in the medical sector involves crucial decision making, robust infrastructure, and addressing ethical and legal obligations. This research addresses the ethical concern that may arise if surgeons are replaced with autonomous Robots. In this research, we performed a mixed-method approach involving quantitative (various literature reviews) and a qualitative survey which involved 60 participants and was conducted online. 52% of the respondents were not ready for complete automation of surgeries, and 77% were opposed to the possibility of the robot replacing surgeons. 75% of respondents recommended that surgeons monitor the interaction and that robots are aided than being entirely autonomous. Although surgeon substitution is not an ethical choice, these skills should be included in anesthetic and surgical preparation curricula and improved in a simulation environment. The future of this area requires exposure to continuous technological advancement and costing models and healthcare benefit networks for the next wave of robotic systems to achieve a foothold in the new healthcare industry. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Robotic surgery

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Ethical issues with development status of modern agricultural production 2

Ethical issues with development status of modern agricultura...

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2nd International Conference on Industrial IoT, Big Data, and Supply Chain 2021

作者： Chang, Victor Gao, Xianghua Cybersecurity Information Systems and AI Research Group School of Computing Teesside University Middlesbrough United Kingdom

ISBN: (数字)9781510651333

ISBN: (纸本)9781510651326

This article analyzes the legally collected real data and explains the analysis process and results of the unregulated third-party traceable agricultural product sales data in the current environment. We use a hybrid method that combines qualitative and quantitative analyses to process the regional data and quantitative analysis to process the count and proportion of data. To the greatest extent, it helps businesses obtain more information. At the same time, it discusses the development status and ethical issues of traceable agricultural products in China based on the literature and analysis process. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Agricultural products

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Corrections to “The Recent Technologies to Curb the Second-Wave of COVID-19 Pandemic”

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IEEE Access 2025年 13卷 5436-5436页

作者： M. Poongodi Mohit Malviya Mounir Hamdi Hafiz Tayyab Rauf Seifedine Kadry Orawit Thinnukool College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar Department of CTO 5G Wipro Ltd. Bengaluru India Centre for Smart Systems AI and Cybersecurity Staffordshire University Stoke-on-Trent U.K. Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway Research Group of Embedded Systems and Mobile Application in Health Science College of Arts Media and Technology Chiang Mai University Chiang Mai Thailand

Presents corrections to the paper, (Corrections to “The Recent Technologies to Curb the Second-Wave of COVID-19 Pandemic”).

关键词： COVID-19 Pandemics

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Parallel Image Encryption Algorithm Using Partitioned Cellular Automata on Graphic Processor Unit

Parallel Image Encryption Algorithm Using Partitioned Cellul...

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2023 IEEE International Conference on Omni-Layer Intelligent systems, COINS 2023

作者： Fazlali, Mahmood Alihoseini, Amirhossein Zarei, Ebrahim Tabani, Myasar Pourmoafi, Seyedali University of Hertfordshire Cybersecurity and Computing Systems Research Group Hatfield United Kingdom Shahid Beheshti University Faculty of Mathematical Sciences Dept. of Data and Computer Sciensce Tehran Iran Khansar Campus University of Isfahan Dep. of Computer Science Isfahan Iran

ISBN: (纸本)9798350346473

Image encryption is a reliable method for securely transmitting images over a network. The time required to encrypt and decrypt an image in online applications is also very important. Although cellular automata cryptography is an appropriate technique for parallelizing and accelerating cryptographic methods, its capacity cannot be demonstrated only in multi-core platforms. Thus, it is needed to parallelize cellular automata cryptography on Graphic Processor Units (GPUs) in order to significantly decrease the encryption/decryption time. In this paper, we propose a new parallel algorithm for two-dimensional cellular automata cryptography that is implemented on GPU. The proposed algorithm uses multiple threads at once to accelerate the bit-level permutation and substitution operations by taking into account the capacity of cellular automata in parallel processing. According to the study experimental findings, the proposed algorithm performs faster on GPU compared to a multicore platform while maintaining the same level of security in comparison to the serial algorithm. © 2023 IEEE.

关键词： Graphics processing unit

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Exploiting Federated Learning for EEG-based Brain-Computer Interface System

Exploiting Federated Learning for EEG-based Brain-Computer I...

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2023 IEEE International Conference on Omni-Layer Intelligent systems, COINS 2023

作者： Ghader, Mohammadnavid Farahani, Bahar Rezvani, Zahra Shahsavari, Mahyar Fazlali, Mahmood Shahid Beheshti University Department of Computer Science Tehran Iran Cyberspace Research Institute Shahid Beheshti University Tehran Iran Tehran Iran Alzahra University Department of Computer Science Tehran Iran Donders Institute Radboud University Nijmegen Netherlands University of Hertfordshire Cybersecurity and Computing Systems Research Group Hatfield United Kingdom

ISBN: (纸本)9798350346473

Motor imagery (MI) is a widely used technique in brain-computer interface (BCI) systems, which allows users to control external devices using their brain signals. Electroen-cephalogram (EEG) signals are commonly used to detect and classify MI tasks. However, the lack of annotated data hampers the performance of machine learning (ML) algorithms. Federated learning (FL) is a promising approach to address these challenges, as it allows models to be trained on decentralized datasets without exchanging data. This study proposes an FL approach for MI-EEG signal classification using a convolutional neural network (CNN) on the PhysioNet dataset containing EEG recordings of left and right-hand imagery movements. We evaluate the performance of the FL approach using two different aggregation methods, namely FedAvg and FedProx, and compare it to the centralized ML approach. Furthermore, we explore the effect of increasing the number of clients who participate in the learning process on the performance of the model. Our findings demonstrate that FL maintains consistent classification accuracy comparable to the centralized ML approach while reducing data leakage risks. Thus, FL shows great promise as an essential instrument for MI-EEG signal classification and BCI systems, enabling distributed training of a comprehensive model when privacy-sensitive data is dispersed across multiple clients. © 2023 IEEE.

关键词： Electroencephalography

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Enabling Privacy-Preserving Edge AI: Federated Learning Enhanced with Forward-Forward Algorithm

Enabling Privacy-Preserving Edge AI: Federated Learning Enha...

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Omni-layer Intelligent systems (COINS), International Conference on

作者： Mohammadnavid Ghader Saeed Reza Kheradpisheh Bahar Farahani Mahmood Fazlali Department of Computer Science Shahid Beheshti University Tehran Iran Cybersecurity and Computing Systems Research Group University of Hertfordshire Hatfield United Kingdom

ISBN: (数字)9798350349597

ISBN: (纸本)9798350349603

Artificial Intelligence (AI) has emerged as a pivotal technology across various sectors, including healthcare, trans-portation, and the development of smart cities, revolutionizing service delivery and operational efficiency. However, the adoption and introduction of new data-driven services leveraging central-ized training models have been hindered by significant concerns over privacy and data security, as these traditional techniques potentially expose sensitive information to breaches. Federated Learning (FL) presents a compelling solution to this dilemma, enabling decentralized data processing without compromising privacy. Integrating Edge AI into this framework, FL enables the collaborative training of models based on data distributed across different clients. Nevertheless, implementing FL on edge devices introduces a set of challenges due to the limited computational and memory resources available on such tiny devices. Specifically, the backpropagation (BP) phase of training models is notably resource-intensive, posing a barrier to efficient deployment. To address this, we replaced the backpropagation phase with a Forward-Forward (FF) algorithm. Moreover, we integrated and compared several loss functions, namely Hinton, Symba, and Swish, to assess their compatibility and efficiency in the context of forward-forward training within the federated learning framework. The study indicates that our novel method leads to a slight decrease in accuracy for large and complex datasets compared to the traditional BP technique. However, it has the potential to enhance runtime and reduce memory overhead. The proposed technique represents a promising path toward the broader adoption of Edge AI by effectively addressing critical technical challenges, namely privacy concerns and on-chip model training.

关键词： Training Backpropagation Data privacy Accuracy Federated learning Smart cities Computational modeling

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Smart Homes Under Siege: Assessing the Robustness of Physical Security Against Cyber Attackers

SSRN

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SSRN 2023年

作者： Allen, Ashley Mylonas, Alexios Vidalis, Stilianos Gritzalis, Dimitris Cybersecurity and Computing Systems Research Group Dept. of Computer Science University of Hertfordshire United Kingdom Dept. of Informatics Athens University of Economics & Business Greece

Nowadays domestic smart security devices, such as smart locks, smart doorbells, and security cameras, are becoming increasingly popular with users, due to ease of use, convenience, and declining prices. Unlike conventional user security devices, such as alarms and locks, current standards, such as the British TS 621, are not easily understandable by end users due to the technical language employed. Users also have very few sources of unbiased information regarding product performance in real world conditions and protection against attacks and, as a result, tend to take manufacturer claims at face value. This means that, as this work proves, their users may be exposed to threats, such as theft, impersonation (should an attacker steal their credentials), and even physical injury, if the device fails and is used to prevent access to hazardous environments. As such, this paper deploys several attacks using popular wireless attack vectors (i.e., 433MHz radio, Bluetooth, and RFID) against domestic smart security devices to assess the protection offered against cyber attackers-burglars. Our results suggest that users are open to considerable cyber physical attacks, irrespective if they use generic (i.e., no name) or branded smart security devices, due to the poor security offered by these devices. © 2023, The Authors. All rights reserved.

关键词： Bluetooth

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AI-Enabled System for Efficient and Effective Cyber Incident Detection and Response in Cloud Environments

arXiv

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arXiv 2024年

作者： Farzaan, Mohammed Ashfaaq M. Ghanem, Mohamed Chahine El-Hajjar, Ayman Ratnayake, Deepthi N. PRIAM CYBER AI Limited London United Kingdom Department of Computer Science University of Liverpool Liverpool United Kingdom Cyber Security Research Centre London Metropolitan University United Kingdom Cyber Security Research Group University of Westminster London United Kingdom Cybersecurity and Computing Systems Research Group University of Hertfordshire Hatfield United Kingdom

The escalating sophistication and volume of cyber threats in cloud environments necessitate a paradigm shift in strategies. Recognising the need for an automated and precise response to cyber threats, this research explores the application of Artificial Intelligence (AI) and Machine Learning (ML) and proposes an AI-powered cyber incident response system for cloud environments. The proposed system, tested in three major datasets, namely NSL-KDD, UNSW-NB15, CIC-IDS-2017, encompassing Network Traffic Classification, Web Intrusion Detection, and post-incident Malware Analysis (built as a Flask application), achieves seamless integration across platforms like Google Cloud and Microsoft Azure. The Random Forest model demonstrated accuracies of 90%, 75%, and 99% on the respective datasets for network traffic classification, and 96% for the malware analysis application, while the neural network model for the malware analysis system achieved 99% accuracy. Our research highlights the strengths of AI-powered cyber security, with Random Forest excelling at classifying cyber threats, offering an efficient and robust solution. Deep learning models significantly improve accuracy, and their resource demands can be managed using cloud-based Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs). Cloud environments themselves provide a ideal platform for hosting these AI/ML systems, while container technology ensures both efficiency and scalability. These findings demonstrate the contribution of the AI-led system in guaranteeing a robust and scalable cyber incident response solution in the cloud. © 2024, CC BY.

关键词： Cloud security

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Parallel Image Encryption Algorithm Using Partitioned Cellular Automata on Graphic Processor Unit

Parallel Image Encryption Algorithm Using Partitioned Cellul...

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Omni-layer Intelligent systems (COINS), International Conference on

作者： Mahmood Fazlali Amirhossein Alihoseini Ebrahim Zarei Myasar Tabani Seyedali Pourmoafi Cybersecurity and Computing Systems Research Group University of Hertfordshire Hatfiled UK Dept. of data and Computer Sciensce Faculty of Mathematical sciences Shahid Beheshti University Tehran Iran Dep. of Computer Science Khansar Campus University Of Isfahan Isfahan Iran

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