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33rd International Telecommunication Networks and Applications Conference, ITNAC 2023

作者： Ahmed, Amjed Abbas Hasan, Mohammad Kamrul Nafi, Nazmus Shaker Aman, Azana Hafizah Islam, Shayla Fadhil, Saif Aamer Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia Department of Computer Techniques Engineering Baghdad10011 Iraq Integrated Work Package KBR SouthbankVIC Australia Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia

ISBN: (纸本)9798350317138

Depending on a device's encryption mechanism, a wide variety of tangible details could be exposed. These leaks are used in side-channel analysis, which is used to get keys. Due to deep learning's sensitivity to the characteristics of the data being processed, using such algorithms can significantly improve the accuracy and efficiency of side channel analysis. However, classic neural networks are now used for the vast majority of the work that is being done. When the number of nodes in a network grows, so does the efficiency with which key recovery can function. However, the method's computing complexity grows in direct proportion. Overfitting, inadequate capacity for feature extraction, and inefficient training are all potential issues. In this study, we develop a compact convolutional neural network by enhancing a previously existing combination of neural networks. Novel neural network along with previous neural network both have their own implementations of the side-channel analysis used in comparative trials. Statistically, the new network has better accuracy, quicker convergence, and more robustness. As part of the research, heatmaps were provided as a means of data visualisation. The critical interval concentration is higher and the heat value is higher in the new network. Conventional neural networks, which serve as the foundation for various kinds of neural networks, perform much worse than side channel studies based on feature fusion networks. © 2023 IEEE.

关键词： Efficiency

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Unveiling the Landscape of Machine Learning and Deep Learning Methodologies in Network security: A Comprehensive Literature Review 2

Unveiling the Landscape of Machine Learning and Deep Learnin...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Saeed, Nouf Majid Sultan Eid Ibrahim, Amer Ali, Liaqat Al-Dmour, Nidal A. Mohammed, Abdul Salam Ghazal, Taher M. American University In The Emirates Dubai United Arab Emirates College Of Engineering And Technology University Of Science And Technology Of Fujairah Fujairah United Arab Emirates College Of Engineering Mutah University Department Of Computer Engineering Jordan Skyline University College University City Sharjah Shariah United Arab Emirates Khalifa University Centre For Cyber Physical Systems Computer Science Department United Arab Emirates Center For Cyber Security Faculty Of Information Science And Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350394962

The dynamic nature of cyber threats offers a continual problem in the field of cybersecurity in the context of the expanding internet environment. This study provides an in-depth assessment of the literature on machine learning (ML) and deep learning (DL) methodologies for network analysis for intrusion detection. This review curates, assesses, and distils method-specific findings while considering temporal or thermal correlations. It provides a recognition of the importance of data in ML and DL approaches, and a comprehensive overview of frequently used network datasets in ML/DL applications, as well as the inherent challenges of adopting ML/DL in the cybersecurity field. The study concludes with well-informed recommendations for future areas of research in this critical domine. © 2024 IEEE.

关键词： Intrusion detection

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Optimization Technique for Deep Learning Methodology on Power Side Channel Attacks 33

Optimization Technique for Deep Learning Methodology on Powe...

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33rd International Telecommunication Networks and Applications Conference, ITNAC 2023

作者： Ahmed, Amjed Abbas Hasan, Mohammad Kamrul Nafi, Nazmus Shaker Aman, Azana Hafizah Islam, Shayla Nahi, Mohammad Siab Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia Department of Computer Techniques Engineering Baghdad10011 Iraq GS-APAC Kellogg Brown and Root Melbourne Australia Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia

ISBN: (纸本)9798350317138

The first non-profiled side-channel attack (SCA) method using deep learning is Timon's Differential Deep Learning Analysis (DDLA). This method is effective in retrieving the secret key with the help of deep learning metrics. The Neural Network (NN) has to be trained numerous times since the proposed approach increases the learning cost with the key sizes, making it hard to assess the results from the intermediate stage. In this research, we provide three possible answers to the issues raised above, along with any challenges that could result from trying to solve these issues. We will start by offering an updated algorithm that has been modified to be able to keep track of the metrics during the intermediary stage. Next, we provide a parallel NN structure and training technique for a single network. This saves a lot of time by eliminating the need to repeatedly retrain the same model. The newly designed algorithm significantly sped up attacks when compared to the previous one. Thus, we propose employing shared layers to overcome memory challenges in parallel structure and improve performance. We evaluated our approaches by presenting non-profiled attacks on ASCAD dataset and a Chip Whisperer-Lite power usage dataset. Power utilisation was studied using both datasets. The shared layers strategy we created was up to 134 times more successful than the prior technique when used to the ASCAD database. © 2023 IEEE.

关键词： Side channel attack

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SIFT-based Approach for Dorsal Hand Veins Images Recognition 26

SIFT-based Approach for Dorsal Hand Veins Images Recognition

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26th International Conference on Digital Signal Processing and its Applications, DSPA 2024

作者： Egorov, Dobroslav P. Mizinov, Pavel V. Kravchenko, Oleg V. Konnova, Natalia S. Osipenko, Gavriil V. Kotel'nikov Institute of Radio Engineering and Electronics of Ras Moscow Russia Bauman Moscow State Technical University Department of Information Security Moscow Russia Computer Science and Control of Ras Federal Research Center Moscow Russia Moscow Aviation Institute Moscow Russia

ISBN: (纸本)9798350361728

In this paper we present a system for person identification using dorsal hand veins pattern images. A simple approach is developed based on scale-invariant feature transform (SIFT) method for image features extraction, fast approximate nearest neighbors algorithm (FLANN) for keypoints matching in a preselected region-of-interest (ROI), and subsequent utilization of dense layer neural network for accurate friend-or-foe predictions. The efficiency of the considered approach is evaluated using an open dataset, and confusion matrices are calculated. The obtained results confirm again that dorsal hand veins identification systems are particularly promising for high-security settings. The proposed approach is implemented in software for Orange pi single-board computers. © 2024 IEEE.

关键词： Nearest neighbor search

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An Extensive Analysis of Cybersecurity in the Era of Advanced Threats 2

An Extensive Analysis of Cybersecurity in the Era of Advance...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Krishnaveni, S. Geetha, B.T. Rashmitha Duvvada, Sree Sathe, Manoj Amrita Soni, Mukesh Adithya Institute of Technology Department of Management Studies Kovilpalayam Coimbatore India Saveetha School of Engineering Simats Department of Ece Tamil Nadu India University of Wisconsin Madison Department of Cse United States Computer Science Pes Modern Institute of Business Studies Pune India Sharda School of Engineering & Technology Sharda University Center for Cyber Security and Cryptology Computer Science & Engineering Uttar Pradesh Greater Noida India Dr. D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Tathawade Pune India

ISBN: (纸本)9798350384277

The scientific and business communities have taken an interest in the Internet of Things (IoT) because of its tremendous impact on human life. The IoTs have emerged as a state-of-the-art framework for creating intelligent spaces. IoT cybersecurity has gained much attention lately due to multiple cyber threats that revealed serious cybersecurity threats. Traditional techniques for safeguarding networks are widely recognized. However, typical security methods are ineffective in protecting IoT networks and systems from cyber threats due to the resource-constrained nature of IoT devices and the unique features of IoT regulations. IoT-specific databases, instruments, and techniques are required to raise the bar for IoT cybersecurity. Thus, safeguarding the IoT environment against cyber threats becomes crucial. The goal of this investigation is to extensively examine the era of advanced threats. It aids in the automation of procedures for transferring real-time crucial data between gadgets without the need for human involvement. It presents a statistical structure for protecting entities and networking traffic associated with IoT wireless connectivity. When a networking session is formed, it comprises a computerized cyber-defense authentication process that identifies and blocks security assaults. A comparison of the performance of machine learning that has been shown useful is applied to protect the transport Layer (TL) of IoT devices. As an outcome, models generate precise conclusions that can be used to predict and discover risks in IoT-based platforms. © 2024 IEEE.

关键词： Cyber attacks

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Medical Diagnoses: Heart Disease Prediction 2

Medical Diagnoses: Heart Disease Prediction

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2nd International Conference on Business Analytics for Technology and security, ICBATS 2023

作者： Mahfuri, Mahmoud Ghazal, Taher M. Mudassar, Muhammad Siddiqui, Shahan Yamin Farooq, Sajid Kanwal, Nayab Ahmad, Munir International University for Science and Technolgy Computer and Information Technolgy Department Faculty of Engineering Syria Center for Cyber Security Faculty of Information Science and Technology Selangor Bangi43600 Malaysia Skyline University College School of Information Technology University City Sharjah 1797 United Arab Emirates The University of Lahore Lahore Pakistan Minhaj University Department of Computer Science Lahore Pakistan School of Computer Science National College of Business Administration & Economics Lahore Pakistan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan

ISBN: (纸本)9798350335644

The successful and continuously increasing applications of machine learning (ML) and data mining (DM) in the wide range of fields such as e-businesses, public or private sectors, marketing and retails has led the ML and DM applications in other industries and health sectors. In the health sector the data is information wise too rich but not knowledge rich. It can be due to the lack of effective tools and research study in this era. In this research work, an efficient model is proposed based on the machine learning and data mining tools to predict a particular medical disease that is the heart disease. The solution for the problem which is addressed that data is not knowledge-rich, is presented in this paper. Waikato Environment and Python is used for to analyze the data, so that an automation system can be formed. Several experiments are to be implemented to compare the performance through Supervised Learning and Unsupervised Learning. Bayesian learning (Naïve Bayes), Tree's algorithm (Decision Tree), and Neural Networks (MLPNN) are applied on the same dataset. Comparatively, Decision Tree gives best accuracy when compared with Naïve Bayes, ANN performs more effective when compared with others two Bayesian algorithms (Naïve Bayes) and Tree's algorithm (Decision tree). © 2023 IEEE.

关键词： Decision trees

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Secure Blockchain Integration Approach for Knowledge Discovery in Industrial Internet of Things

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 4772-4787页

作者： Rohan Tripathi Ritam Rao Vivekananda Bhat K. Abhishek Kumar Pandey Ashok Kumar Das Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India Department of Computer Science and Engineering College of Informatics Korea University Seoul South Korea

The field of Industrial Internet of Things (IIoT) is now being duly recognized as a revolutionary area in industrial automation. With the budding interest of knowledge discovery that helps the businesses in applying technology on a large scale, researchers are now faced with several major security concerns. Blockchain integration is one of the suggested applications of technology that could help secure the data during transmission, storage, and knowledge discovery. Moreover, by integrating smart contracts, a secure architecture could also assimilate accountability during data exchange. Thus, we propose a four-layer security architecture that isolates IIoT devices from user-oriented layers and maintains a record of all IIoT devices registered in the organization to prevent malicious devices from corrupting the database and discovery process. Further, by choosing proof of authority (PoA), we ensure the fair functioning of the Blockchain nodes. Through proxy re-encryption for clients, PoA consensus for Blockchain nodes, and Whitelist-based access control for IIoT Devices, we ensure the legitimacy of all participating nodes. We also implement a prototype using a private Ethereum network with proof of authority consensus and present the time taken for the entire exchange and the gas (ethereum currency) consumed per exchange by the contracts. Also we implemented the secure knowledge discovery to understand the significance of the developed scheme. The results show that the exchange can be implemented in an IIoT environment and work with a reasonable amount of resource consumption.

关键词： Blockchains Industrial Internet of Things computer architecture security Knowledge discovery computer science Cloud computing Prototypes Authentication Analytical models

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An Automated Platform for Gathering and Managing Open-Source Cyber Threat Intelligence 2

An Automated Platform for Gathering and Managing Open-Source...

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2nd International Conference on Business Analytics for Technology and security, ICBATS 2023

作者： Al-Dmour, Nidal A. Kamrul Hasan, Mohammad Ajmal, Masood Ali, Muhammad Naseer, Iftikhar Ali, Atif Hamadi, Hussam Al Ali, Nasir Mutah University College of Engineering Department of Computer Engineering Jordan Center for Cyber Security Malaysia Riphah International University Islamabad Pakistan University of Central Punjab Pakistan The Superior University Faculty of Computer Science & Information Technology Lahore Pakistan Cyberjaya Malaysia College of Engineering and IT University of Dubai United Arab Emirates School of Computer Science NCBA&E Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan

ISBN: (纸本)9798350335644

The community has begun paying more attention to source OSCTI Cyber Threat Intelligence to stay informed about the rapidly changing cyber threat landscape. Numerous reports from the OSCTI frequently provide information about dangers. However, current OSCTI gathering and management tools have mainly concentrated on individual minor compromise indicators, despite the urgent need for high-quality OSCTI. The relationship between higher-level notions (including the strategies, methods, and processes) and the connections between them, which hold crucial information about dangerous behaviors and are crucial to revealing the full dangerous situation, have been disregarded. Therefore, we present securityKG, an automated OSCTI collection and administration system. securityKG collects OSCTI to extract high-fidelity knowledge about threat behaviours to address the void. Using a mixture of AI and NLP approaches, a security know-how graph is then constructed from a wide variety of sources. To facilitate knowledge graph exploration, securityKG provides a user interface (UI) that supports multiple forms of interactivity. © 2023 IEEE.

关键词： User interfaces

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Cyberbullying Detection in Hinglish Language

Cyberbullying Detection in Hinglish Language

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Rajiv Kumar Vipin Rai Shivani Joshi Daksh Raj Amrita Amrita Department of Computer Science & Engineering Galgotias University Greater Noida U.P. India Department of Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida U.P. India Center for Cyber Security and Cryptology Computer Science & Engineering Sharda School of Engineering & Technology Sharda University Greater Noida India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Cyberbullying represents one of the most recent challenges stemming from the widespread adoption of social media platforms. With the surge in social media usage, the right to express oneself is being misused. Statistics reveal that approximately 36.7% of individuals believe that they have encountered cyberbullying at some point in their lives. These figures have more than doubled since 2007, and a noticeable increase occurred from 2018 to 2019, indicating an unfavorable trend. While there are existing solutions in the market aimed at mitigating this issue, many of them come with usage limitations or employ fewer effective algorithms. The primary objective of this work is to explore innovative methods for comprehending and automatically identifying instances of cyberbullying across online text messages and comments on different social media platforms. For achieving this goal, real-time data are gathered, including comments, headlines and text messages from trending posts on twitter, and devised a labelling study for categorizing cyberbullying. An analysis is presented of this labelled data, which includes an examination of the relationships between various features and both cyberbullying and cyberaggression. The core focus of this work is the early detection of cyberbullying as it originates, while being drafted in real-time. The aim of this work is to enhance the efficiency of cyberbullying detection by using various machine learning techniques and natural language processing.

关键词： Training Support vector machines Accuracy Reviews Cyberbullying Feature extraction Real-time systems Classification algorithms Object recognition Surges

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Heart Disease Prediction Using Machine Learning 2

Heart Disease Prediction Using Machine Learning

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2nd International Conference on Business Analytics for Technology and security, ICBATS 2023

作者： Ghazal, Taher M. Ibrahim, Amer Akram, Ali Sheraz Qaisar, Zahid Hussain Munir, Sundus Islam, Shanza Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia Skyline University College University City School of Information Technology Sharjah1797 United Arab Emirates American University in the Emirates College of Computer and Information Technology Dubai United Arab Emirates NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan NFC Institute of Engineering and Technology Department of Computer Science Multan Pakistan Lahore Garrison University Lahore Department of Computer Science Lahore Pakistan Lahore Garrion University Lahore Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798350335644

The heart disease cases are rising day by day and it is very Important to predict such diseases before it causes more harm to human lives. The diagnosis of heart disease is such a complex task i.e., it should be performed very carefully. The work done in this research paper mainly focuses on which patients has more chance to suffer from this based on their various medical feature such as chest pain etc. We proposed a system of heart disease prediction that is used to diagnose whether the patient is a victim or not by using the previous medical features of the patient. Support vector machine and k-nearest neighbor algorithms of machine learning are used to predict and classify the patient with heart disease. The models gave satisfactory results and were capable for predicting a heart disease by using k-nearest neighbor and support vector machine which gave a good accuracy in contrast to the algorithms that were used in the previous research such as naive bayes etc. © 2023 IEEE.

关键词： Support vector machines

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