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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Mekikis, Prodromos-Vasileios Bouzinis, Pavlos Mitsiou, Nikos Tegos, Sotiris Papanikolaou, Vasilis Tyrovolas, Dimitrios Diamantoulakis, Panagiotis Karagiannidis, George K. Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki Greece Cyber Security Systems and Applied AI Research Center Lebanon

ISBN: (纸本)9781665494274

Powering Internet of Things (IoT) devices in hard to reach or hazardous locations could be prohibitive in terms of cost and safety. In this work, we demonstrate a thorough solution that tackles this problem using a network of unmanned aerial vehicles (UAVs) with wireless power transfer (WPT) capabilities. Given the large-scale IoT deployments of the future and the energy needs of the UAVs, we consider an infrastructure of charging stations for the UAVs that allow an uninterrupted and flexible UAV deployment based on the current energy needs of the IoT devices. Then, we exhibit an orchestrator that communicates with the entire infrastructure and handles the UAV traffic and energy decisions, as well as the digital representation and interaction of the network with the user. © 2023 IEEE.

关键词： User interfaces

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Credit Card Fraud Detection using ML: A Survey

Credit Card Fraud Detection using ML: A Survey

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Bonkoungou, Samiratou Roy, Nihar Ranjan Ako, Nomel Haymes Axel-Elie Batra, Usha Sharda University Department of Computer Science and Engineering India Center of Cyber Security and Cryptography Sharda University India MRIIRS Faridabad India

ISBN: (纸本)9781665462631

The popularity of credit card fraud is rising as the number of cashless transactions increases. Online transactions are expanding as more people use credit cards and mobile wallets. The growth of internet commerce has also accelerated the spread of credit card fraud. Examining the cardholder's spending patterns can help identify credit card theft. Any odd behavior is grounds for the transaction to be deemed fraudulent. There are many difficulties in detection, such as frequent profile changes between fraudulent and normal transactions. Researchers use numerous techniques to detect credit card fraud, among them are hidden markov model, naives bayes classifier, decision tree, k-nearest neighbour classifier, logistic regression. The aim of this research paper is to examine divergent approaches of credit card fraud detection. © 2023 IEEE.

关键词： Hidden Markov models

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Utilizing Smartwatches and Deep Learning Models for Enhanced Avalanche Victim Identification, Localization, and Efficient Recovery Strategies: An In-depth Study 5

Utilizing Smartwatches and Deep Learning Models for Enhanced...

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Jha, Khyati Pasbola, Mansi Rai, Hari Mohan Amanzholova, Saule Department of Computer Science Dronacharya Group of Institutions Greater Noida India Faculty of Computer Technologies and Cyber Security International University of Information Technology Almaty Kazakhstan

ISBN: (纸本)9798400709418

This article explores the transformative impact of modern technology on avalanche response and safety, addressing the longstanding challenges associated with avalanches in mountainous regions. Historically, avalanches have presented significant dangers, compounded by the difficulties of conducting rescue operations in remote and harsh environments. However, recent technological advancements, including smartwatches with SOS features and specialized mobile applications, have ushered in a new era of avalanche safety. Smartwatches have emerged as crucial tools in avalanche safety. Equipped with SOS features and synchronized with mobile apps, these devices have revolutionized the way we approach avalanche emergencies. In this study, we propose a methodology that leverages smartwatch data and deep learning models to detect individuals buried under avalanche debris. This approach aims to significantly improve the chances of successful and timely rescue operations. The proposed methodology involves collecting data from smartwatches, which are equipped with various sensors to monitor location, movement, and environmental conditions. Deep learning models are then deployed to analyze this data, identifying patterns and anomalies associated with avalanche incidents. By harnessing these advanced technologies, our methodology offers the potential for real-time avalanche detection, thereby enhancing overall safety outcomes. One of the key strengths of this methodology is its adaptability to diverse avalanche scenarios. The study envisions a future where technology-driven avalanche safety becomes an integral component of outdoor activities, mitigating risks and enabling more effective response strategies in the face of this natural hazard. © 2023 ACM.

关键词： Deep learning

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Transformation technique for derivation of similarity scores for signatures

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Iran Journal of computer Science 2022年第4期5卷 317-328页

作者： Adewuyi, Joel Adeyanju Iwasokun, Gabriel Babatunde Gabriel, Arome Junior Department of Computer Science Federal University of Technology Akure Nigeria Department of Software Engineering Federal University of Technology Akure Nigeria Department of Cyber Security Federal University of Technology Akure Nigeria

The human signature is a widely acceptable biometric characteristic that provides a very secure means for authorizing legal documents. However, signature-based authorization is characterized by problems of counterfeiting and forgery. Hence, the need for adequate verification and protection of signatures. On this note, this paper presents a signature similarity score model that is based on the transformation technique. The model comprises stages for pre-processing, feature extraction, and matching. The pre-processing stage performs Gabor and median filtering, normalization, rescaling, histogram equalization, binarization, and thinning. Gabor and Fourier’s transforms were used for feature extraction and Principal Component Analysis (PCA) for dimensionality reduction towards preventing information loss. The spatial frequency domain property of the input textures was extracted to form the texture features, while Fourier transform was applied to the texture features to obtain their rotation-invariant version. Finally, Euclidean distance-based feature analysis was carried out. Analysis of results from the experimental study of the model on the Netherlands Forensic Institute (NFI) standard signature dataset revealed that with the free or minimal noise level, the algorithm performed well. It was also established that each stage of the enhancement process is important for performance optimization. The comparison of experimental results with what was obtained for some similar and recent models showed an encouraging and superior performance level of the proposed model. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Biometrics Forensics Image processing Information security Signal processing Signature recognition

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Tracking Objects and Activities with Attention for Temporal Sentence Grounding 48

Tracking Objects and Activities with Attention for Temporal ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Xiong, Zeyu Liu, Daizong Zhou, Pan Zhu, Jiahao Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Peking University Wangxuan Institute of Computer Technology China

ISBN: (纸本)9781728163277

Temporal sentence grounding (TSG) aims to localize the temporal segment which is semantically aligned with a natural language query in an untrimmed video. Most existing methods extract frame-grained features or object-grained features by 3D ConvNet or detection network under a conventional TSG framework, failing to capture the subtle differences between frames or to model the spatio-temporal behavior of core persons/objects. In this paper, we introduce a new perspective to address the TSG task by tracking pivotal objects and activities to learn more fine-grained spatio-temporal behaviors. Specifically, we propose a novel Temporal Sentence Tracking Network (TSTNet), which contains (A) a Cross-modal Targets Generator to generate multi-modal templates and search space, filtering objects and activities, and (B) a Temporal Sentence Tracker to track multi-modal targets for modeling the targets' behavior and to predict query-related segment. Extensive experiments and comparisons with state-of-the-arts are conducted on challenging benchmarks: Charades-STA and TACoS. And our TSTNet achieves the leading performance with a considerable real-time speed. © 2023 IEEE.

关键词： computer vision

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Bitcoin Price Prediction Using Cuckoo Search Algorithm for Feature Selection with LSTM Model 2

Bitcoin Price Prediction Using Cuckoo Search Algorithm for F...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Radmand, Elnaz Pirgazi, Jamshid Odeh, Mahmoud Sorki, Ali Ghanbari University of Science and Technology of Mazandaran Faculty of Electrical and Computer Engineering Behshahr Iran School of Information Technology Zarqa University Cyber Security Department Zarqa Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798350373363

Deep learning is now a top method for predicting the price of Bitcoin, providing an excellent and well thought out strategy. This technique makes use of a wealth of historical data from reliable sources covering the first ten years of Bitcoin. Technical data and complex patterns are deeply integrated into the dataset to enhance the model's comprehension of complex Bitcoin price patterns. These additions greatly deepen our understanding of the various factors affecting the price of Bitcoin. This method is based on a strategic procedure that involves the careful selection of certain high-performing characteristics. These traits go through a stringent screening process that uses advanced algorithms such as the Cuckoo algorithm. The chosen features are then smoothly incorporated into an encoder-decoder model of LSTM (Long Short-Term Memory), improving the model's ability to predict Bitcoin prices. In this case, it is critical to apply deep learning methods, especially the LSTM encoder-decoder model. Through the integration of technical indicators and sophisticated selection techniques with historical Bitcoin data, this approach leverages deep learning capabilities to identify intricate patterns in Bitcoin pricing movements. In the end, this gives the model the ability to forecast future changes in the price of Bitcoin with accuracy. © 2023 IEEE.

关键词： Forecasting

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IoT Smart Devices Risk Assessment Model Using Fuzzy Logic and PSO

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computers, Materials & Continua 2024年第2期78卷 2245-2267页

作者： Ashraf S.Mashaleh Noor Farizah Binti Ibrahim Mohammad Alauthman Mohammad Almseidin Amjad Gawanmeh School of Computer Sciences Universiti Sains MalaysiaPenangMalaysia Department Computer Center Al-Balqa Applied UniversitySaltJordan Department of Information Security Faculty of Information TechnologyUniversity of PetraAmmanJordan Department of Computer Science Tafila Technical UniversityTafilaJordan College of Engineering and IT University of DubaiDubaiUnited Arab Emirates

Increasing Internet of Things(IoT)device connectivity makes botnet attacks more dangerous,carrying catastrophic *** IoT botnets evolve,their dynamic and multifaceted nature hampers conventional detection *** paper proposes a risk assessment framework based on fuzzy logic and Particle Swarm Optimization(PSO)to address the risks associated with IoT *** logic addresses IoT threat uncertainties and ambiguities *** component settings are optimized using PSO to improve *** methodology allows for more complex thinking by transitioning from binary to continuous *** of expert inputs,PSO data-driven tunes rules and membership *** study presents a complete IoT botnet risk assessment *** methodology helps security teams allocate resources by categorizing threats as high,medium,or low *** study shows how CICIoT2023 can assess cyber *** research has implications beyond detection,as it provides a proactive approach to risk management and promotes the development of more secure IoT environments.

关键词： IoT botnet detection risk assessment fuzzy logic particle swarm optimization(PSO) cybersecurity interconnected devices

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LSTM - Recurrent Neural Network Model to Forecast the Risk Factors in Recurrent Cervical Carcinoma 2

LSTM - Recurrent Neural Network Model to Forecast the Risk F...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Geeitha, S. Renuka, P. Thilagavathi, C. Ananth, S. Ramya, S. Sinduja, K. M.Kumarasamy College of engineering Department of Information Technology Karur India Mahendra Engineering College Department of Ai&ds Namakkal India Paavai Engineering College Department of Cyber Security Pachal India Sri Krishna College of engineering and technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798350368413

Cervical carcinoma is a healthcare issue globally, and despite advancements in prevention, diagnosis and treatment, recurrence remains as a challenge. Understanding the risk factors associated with cervical cancer recurrence is crucial for optimizing patient care and improving outcomes. Multiple factors contribute to the possibility of cervical carcinoma metastasis. The important prognostic factor is the stage of the disease at diagnosis, with advanced stages are associated with a higher likelihood of recurrence. Other clinical chronic attributes like size of a cyst, inclusion of lymph nodes and what's the kind of histology is also influence recurrence risk. Human papillomavirus (HPV) infection, particularly with HPV that poses a danger genotypes, danger element for the emergence of cervical cancer. HPV persistence following initial treatment is strongly associated with increased recurrence rates. The risk factors of recurrence cervical cancer is found by Long Short Term Memory which is advancement in recurrent neural network classifier. Gated Recurrent Unit (GRU) and Deep Neural Network (DNN) classifiers are compared with LSTM. In which LSTM Model attains the highest accuracy. In conclusion, cervical cancer recurrence is influenced by an intricate connection amongst standard of living, molecular and clinical variables. These risk factors is important for forecasting individuals at higher risk of recurrence and tailoring personalized treatment strategies. © 2024 IEEE.

关键词： Lung cancer

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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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A Comprehensive Threat Model for Autonomous Vehicle Communication security 6

A Comprehensive Threat Model for Autonomous Vehicle Communic...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Meegammana, Niranjan W. Fernando, Harinda Shilpa Sayura Foundation Cyber Security Research Lab Kandy Sri Lanka Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

The rapid advancement of autonomous vehicles (AVs) promises to enhance transportation efficiency, reliability, and safety. However, these systems, particularly multi-channel vehicular communications, are vulnerable to cyber attacks that threaten authenticity, authorization, confidentiality, integrity, availability, and non-repudiation. The 2014 Chrysler Jeep hack highlights the urgent need for robust security measures in vehic-ular communications. This study utilizes the STRIDE, DREAD, and MITRE ATT&CK security frameworks to develop a comprehensive threat model that identifies and assesses critical threats and risks to AV communications. By detailing specific attack vectors and their multidimensional impacts and criticality, the study establishes a baseline of threats for communication security in vehicular networks. It produces visual threat maps, impact graphs of various attacks, and a color-coded threat grid to guide stakeholders in developing effective cyber security measures for protecting AV communications, thereby help stakeholders to improve the reliability and safety of AV navigation for fostering growth in the AV industry. © 2024 IEEE.

关键词： cyber attacks

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