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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Akkus, Merve Karabatak, Murat Tekin, Ramazan Batman University Department of Computer Engineering Batman Turkey Firat University Department of Software Engineering Elaziǧ Turkey

ISBN: (纸本)9798350330366

The electrocardiogram (ECG) plays a critical role in the early prevention and diagnosis of cardiovascular diseases. The signals obtained from the electrocardiogram device used in the diagnosis of heart diseases are obtained by healthcare professionals and used for arrhythmia detection. Many cardiac abnormalities show up on the ECG, including arrhythmia, which means abnormal heart rhythm. The basis of arrhythmia diagnosis is the identification and correct classification of normal and abnormal heartbeats based on ECG morphology. Autoencoder can compress ECG signals to obtain time series signal features, which can be used for classification purposes. This work is realized using a layered network architecture that includes a convolutional autoencoder for ECG signal compression and a long short-term memory (LSTM) that drives the encoder output for classification. In this architecture, ECG signals are used after preprocessing to achieve higher arrhythmia classification accuracy. The open access MitBih arrhythmia dataset from PhysioNet was used in the study. The results showed that high accuracy, precision and recall were achieved. Compared to traditional methods, this method has better performance in arrhythmia classification. © 2024 IEEE.

关键词： Electrocardiograms

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Work-in-Progress: ESOps - An Agile Pipeline for Next-Generation Embedded Systems Development 24

Work-in-Progress: ESOps - An Agile Pipeline for Next-Generat...

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24th ACM SIGBED International Conference on Embedded software, EMSOFT 2024

作者： Al Maruf, Md Azim, Akramul Ontario Tech University Department of Electrical Computer and Software Engineering ON Canada

ISBN: (纸本)9798350356410

Embedded Systems Operations (ESOps) introduces a feature-driven development pipeline for next-generation embedded system, leveraging domain-specific Large Language Models (LLMs) to expedite the development process. Targeted at industries such as automotive, aerospace, and healthcare, ESOps overcomes traditional development barriers by enabling a profound understanding of legacy codebase and integrating innovative AI-driven methodologies. This pipeline ensures systems predictability and reliability, meets stringent constraints, and shortens time-to-market by optimizing both the development and operational phases. © 2024 IEEE.

关键词： Artificial intelligence

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A Systematic Literature Review of Latest Cloud Forensic Frameworks, Tools and Challenges 2

A Systematic Literature Review of Latest Cloud Forensic Fram...

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2nd International Conference on Emerging Trends in Electrical, Control, and Telecommunication engineering, ETECTE 2023

作者： Farooq, Ummer Shaukat, Arslan Butt, Wasi Haider Hassan, Ali Department of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350305654

Background: Cloud computing is widely used in this era of IoTs. Cloud users utilize cloud computing to access various cloud services. The defects in cloud services are exploited by suspicious actors. On the other hand, cloud forensics help against such suspicious actors. It has been observed on many occasions that cloud services were not well designed. Objective: Cloud computing with its services exposed to cyber threats, due to the bad implementation of cloud services, helps suspicious actors to exploit vulnerabilities in the cloud environment. The main objective is to identify different cloud forensic frameworks. Method: We have used the method of systematic literature review to find and analyze different research studies, which mainly are published between 2010-2022. Results: In this paper, we have discovered 36 different cloud forensic frameworks, with some forensic tools. In this SLR we also mentioned some challenges of cloud forensics with recommendations. Conclusion: This systematic literature review unveils the latest developments in cloud forensic frameworks, tools, and challenges. The insights provided in this paper will serve as an invaluable resource for future researchers in this domain, facilitating the enhancement of cloud security practices. © 2023 IEEE.

关键词： Web services

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Ontology Based Test Case Reduction in Regression Testing 26

Ontology Based Test Case Reduction in Regression Testing

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Farooq, Ummer Qamar, Usman Shaukat, Arslan Bakar, Abu Ashraf, Zubia Naz, Mehwish Department of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350359015

Ontology-based test case reduction in regression testing during software testing is a sophisticated approach that leverages semantic knowledge representation to optimize the selection of test cases. By harnessing domain-specific ontologies, this methodology facilitates a more intelligent and efficient reduction process, ensuring that only the most relevant test cases are retained for subsequent testing cycles. This research paper explores the application of ontology-based technique to enhance the efficiency of regression testing in software development. By leveraging ontological knowledge representations, the study introduces a novel approach for reducing the number of test cases while preserving test coverage, thereby significantly optimizing the regression testing process. The proposed framework not only streamlines testing efforts but also enhances the overall quality assurance of software systems, making it a valuable contribution to software engineering practices. © 2023 IEEE.

关键词： ontology regression testing semantic knowledge software development software testing test case reduction

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Design of high output DC/DC boost circuit without boost transformer 8

Design of high output DC/DC boost circuit without boost tran...

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8th International Conference on Communication and Information Systems, ICCIS 2024

作者： Hong, Qin Zhou, Liang Jia, Zhongning Liu, Chun Liu, Jianbo Software Engineering Institute of Guangzhou Department of Computer Science Guangzhou China

ISBN: (纸本)9798331531171

The development of IOT smart devices has necessitated increasingly stringent requirements for DC power sources in terms of output voltage, size, and cost. For instance, Avalanche Photodiodes (APDs), utilized as photodetectors, require a significantly high reverse bias voltage during the detection of feeble optical signals to achieve optimal gain and high signal-to-noise ratio, thereby ensuring exceptional sensitivity. To address the diverse demands of various smart devices for DC power, a high-output DC/DC boost circuit without a step-up transformer has been innovatively designed. This circuit is comprised of four fundamental components: an oscillator circuit, a comparator circuit, a voltage multiplier circuit, and a constant-current and voltage-stabilizing circuit. The paper conducts a comprehensive theoretical analysis of the circuit's topology, parameter selection, and operational process, which is further validated through Multisim simulation. Additionally, a PCB circuit was designed, fabricated, and underwent rigorous practical testing. The test results conclusively demonstrate that the designed boost circuit proficiently provides APDs with the appropriate bias voltage, eliminating the need for boost transformer. It boasts high output voltage, a simplistic structure, small size, and low cost, enabling it to seamlessly share the same input power source with the APD detection circuit and be integrated into the same PCB layout. © 2024 IEEE.

关键词： avalanche photodiode boost circuit direct current stabilized power supply oscillator voltage multiplier circuit

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Fake News Classification: Past, Current, and Future

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computers, Materials & Continua 2023年第11期77卷 2225-2249页

作者： Muhammad Usman Ghani Khan Abid Mehmood Mourad Elhadef Shehzad Ashraf Chaudhry Department of Computer Science University of Engineering and TechnologyLahore54890Pakistan Department of Computer Science&Information Technology Abu Dhabi UniversityAbu Dhabi59911United Arab Emirates Department of Software Engineering Faculty of Engineering and ArchitectureNisantasi UniversityIstanbulTurkey

The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social *** can quickly fabricate comments and news on social *** most difficult challenge is determining which news is real or ***,tracking down programmed techniques to recognize fake news online is *** an emphasis on false news,this study presents the evolution of artificial intelligence techniques for detecting spurious social media *** study shows past,current,and possible methods that can be used in the future for fake news *** different publicly available datasets containing political news are utilized for performing *** supervised learning algorithms are used,and their results show that conventional Machine Learning(ML)algorithms that were used in the past perform better on shorter text *** contrast,the currently used Recurrent Neural Network(RNN)and transformer-based algorithms perform better on longer ***,a brief comparison of all these techniques is provided,and it concluded that transformers have the potential to revolutionize Natural Language Processing(NLP)methods in the near future.

关键词： Supervised learning algorithms fake news classification online disinformation transformers recurrent neural network(RNN)disinformation transformers recurrent neural network(RNN)

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Detecting and Mitigating the Weakest Cybersecurity Link in an Information System 23

Detecting and Mitigating the Weakest Cybersecurity Link in a...

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23rd International Conference on New Trends in Intelligent software Methodologies, Tools and Techniques, SoMeT 2024

作者： Sadeghian, Ali Mejri, Mohamed Department of Computer Science and Software Engineering Laval University Quebec Canada

ISBN: (纸本)9781643685380

In today's digital landscape, the prevention of cyber attacks has become exceptionally crucial. This is especially true for safety-critical systems, where safeguarding against these threats is of paramount importance. To address this concern, the MITRE Corporation has developed ATT&CK, an extensive framework comprising data matrices. This framework serves the purpose of assessing a company's security preparedness and pinpointing vulnerabilities that may exist within its infrastructure. By leveraging the capabilities of MITRE ATT&CK, including its tactics and techniques, in conjunction with the LDA4CPS tool, we have devised a novel approach to identify the most critical vulnerabilities in a susceptible system. Furthermore, incorporating MITRE ATT&CK mitigations tailored to the discovered vulnerability empowers the blue team (defensive side) with tangible, practical measures to fortify their security posture. This approach enhances their capability to effectively counter cyber threats, bolstering their overall defensive capabilities. © 2024 IOS Press. All rights reserved.

关键词： Cyber attacks

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Modified Bat Algorithm: a newly proposed approach for solving complex and real-world problems

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Soft Computing 2024年第13-14期28卷 7983-7998页

作者： Umar, Shahla U. Rashid, Tarik A. Ahmed, Aram M. Hassan, Bryar A. Baker, Mohammed Rashad Department of Software College of Computer Science and Information Technology University of Kirkuk Kirkuk Iraq Computer Science and Engineering Department School of Science and Engineering University of Kurdistan Hewler KRI Erbil Iraq Department of Computer Science College of Science Charmo University Sulaimani Chamchamal46023 Iraq

Bat Algorithm (BA) is a nature-inspired metaheuristic search algorithm designed to efficiently explore complex problem spaces and find near-optimal solutions. The algorithm is inspired by the echolocation behavior of bats, which acts as a signal system to estimate the distance and hunt prey. Although the BA has proven effective for various optimization problems, it exhibits limited exploration ability and susceptibility to local optima. The algorithm updates velocities and positions based on the current global best solution, causing all agents to converge toward a specific location, potentially leading to local optima issues in optimization problems. On this premise, this paper proposes the Modified Bat Algorithm (MBA) as an enhancement to address the local optima limitation observed in the original BA. MBA incorporates the frequency and velocity of the current best solution, enhancing convergence speed to the optimal solution and preventing local optima entrapment. While the original BA faces diversity issues, both the original BA and MBA are introduced. To assess MBA’s performance, three sets of test functions (classical benchmark functions, CEC2005, and CEC2019) are employed, with results compared to those of the original BA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Dragonfly Algorithm (DA). The outcomes demonstrate the MBA’s significant superiority over other algorithms. In addition, MBA successfully addresses a real-world assignment problem (call center problem), traditionally solved using linear programming methods, with satisfactory results. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Swarm intelligence

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Artificial Intelligence Enabled Future Wireless Electric Vehicles with Multi-Model Learning and Decision Making Models

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Tsinghua Science and Technology 2024年第6期29卷 1776-1784页

作者： Gajula Ramesh Anil Kumar Budati Shayla Islam Louai A.Maghrabi Abdullah Al-Atwai Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering&TechnologyHyderabad 500090India Institute of Computer Science and Digital Innovation(ICSDI) UCSI UniversityKuala Lumpur 56000Malaysia also with Department of ECE KoneruLakshmaiah Education FoundationHyderabad 500090India ICSDI UCSI UniversityKuala Lumpur 56000Malaysia Department of Software Engineering College of EngineeringUniversity of Business and TechnologyJeddah 21448Kingdom of Saudi Arabia Department of Computer Science Applied CollegeUniversity of TabukTabuk 47512Kingdom of Saudi Arabia

In the contemporary era,driverless vehicles are a reality due to the proliferation of distributed technologies,sensing technologies,and Machine to Machine(M2M)***,the emergence of deep learning techniques provides more scope in controlling and making such vehicles energy *** existing methods,it is understood that there have been many approaches found to automate safe driving in autonomous and electric vehicles and also their energy ***,the models focus on different aspects *** is need for a comprehensive framework that exploits multiple deep learning models in order to have better control using Artificial Intelligence(AI)on autonomous driving and energy *** this end,we propose an AI-based framework for autonomous electric vehicles with multi-model learning and decision *** focuses on both safe driving in highway scenarios and energy *** deep learning based framework is realized with many models used for localization,path planning at high level,path planning at low level,reinforcement learning,transfer learning,power control,and speed *** reinforcement learning,state-action-feedback play important role in decision *** simulation implementation reveals that the efficiency of the AI-based approach towards safe driving of autonomous electric vehicle gives better performance than that of the normal electric vehicles.

关键词： wireless vehicles deep learning multi-model learning reinforcement learning Artificial Intelligence(Al)

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Early Detection of Cyber-Physical Attacks on Electric Vehicles Fast Charging Stations Using Wavelets and Deep Learning

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2024年 2卷 220-231页

作者： Abu-Nassar, Ahmad M. Morsi, Walid G. Electrical Computer and Software Engineering Department Faculty of Engineering and Applied Science OshawaONL1G 0C5 Canada

Transportation electrification plays an important role in the operation of the smart grid through the integration of the electric vehicle fast charging stations (EVFCSs), which allows the electric vehicles to provide regulation services to the grid through the vehicle-to-grid (V2G) concept. However, such an integration makes smart grid assets prone to cyber vulnerability threats. In this paper, a cyber-physical attack detection approach is developed to early detect such attacks. The proposed approach combines the continuous wavelet transform (CWT) and the convolution neural network (CNN) to provide an effective detection technique. The proposed detection approach has undergone rigorous testing that considered 420 realistic operational scenarios. Unlike in previous work, the proposed detection approach was found to be effective in automatically learning the salient features from the data as well as identifying the frequency bands that hold such features and using them in the classification process. Furthermore, this work investigated the cyber-attack detection accuracy using different time resolutions of smart meters. The results have shown that the proposed approach effectively detects cyber-physical attacks with an accuracy of 99.76% and a low computational time of 1.8 seconds. © 2024 IEEE.

关键词： Feature extraction

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