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PhishGuard: A Convolutional Neural Network-Based Model for Detecting Phishing URLs with Explainability Analysis 3

PhishGuard: A Convolutional Neural Network-Based Model for D...

引用

3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Islam, Md Robiul Islam, Md Mahamodul Afrin, Mst. Suraiya Antara, Anika Tabassum, Nujhat Amin, Al Uttara University Dept. of Computer Science and Engineering Dhaka Bangladesh University of Oslo Dept. of Informatics Oslo Norway Dept. of Building Engineering and Construction Management Khulna Bangladesh BRAC University Dept. of Electrical and Electronics Engineering Dhaka Bangladesh North South University Dept. of Electrical and Computer Engineering Dhaka Bangladesh Dept. of Computer Science American International Dhaka Bangladesh

ISBN: (纸本)9798350372120

Cybersecurity is one of the global issues because of the extensive dependence on cyber systems of individuals, industries, and organizations. Among the cyber attacks, phishing is increasing tremendously and affecting the global economy. Therefore, this phenomenon highlights the vital need for enhancing user awareness and robust support at both individual and organizational levels. Phishing URL identification is the best way to address the problem. Various machine learning and deep learning methods have been proposed to automate the detection of phishing URLs. However, these approaches often need more convincing accuracy and rely on datasets consisting of limited samples. Furthermore, these black-box intelligent models' decision to detect Suspicious URLs needs proper explanation to understand the features affecting the output. To address the issues, we propose a 1 D Convolutional Neural Network (CNN) and trained the model with extensive features and a substantial amount of data. The proposed model outperforms existing works by attaining an accuracy of 99.85 %. Additionally, our explainability analysis highlights certain features that significantly contribute to identifying the phishing URL. © 2024 IEEE.

关键词： Convolutional neural networks

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Expert Patient Interaction Language Model (EPILM)

Expert Patient Interaction Language Model (EPILM)

引用

Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Pratham Gala Pooja Vartak Jay Vithlani Naman Chheda Dept. of Computer Science and Engineering (Data Science) Dwarkadas J Sanghvi College of Engineering Mumbai India Dept. of Computer Science and Engineering (Data Science) Mumbai India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Today’s youth encounter numerous mental health challenges, many of which can be effectively addressed through professional intervention. However, there is a notable reluctance among young individuals to seek professional mental health services. The integration of language models in providing mental health support offers a non-intrusive and accessible approach for young people to engage with professional assistance for their mental health concerns. Although existing language models like GPT 3.5, Falcon-7B model have been effective, they also present certain challenges like high dependability of data from sources like Reddit, computational obstacles hindering access, and a preference for certain models over superior alternatives have impeded advancements in this area. that require thorough examination. Issues related to biases and the model’s performance across diverse scenarios underscore the necessity for more intelligent strategies. The proposed method involves enhancing the Falcon-7B model through the application of quantization low rank adaptation (Q-LoRA). This technique reduces the computational requirements and simplifies the accessibility for individuals equipped with the necessary technology while providing the average bertSCORE 85%. By addressing these issues, paper’s objective is to refine mental health support systems to benefit a broader audience.

关键词： Training Adaptation models Quantization (signal) Social networking (online) Computational modeling Graphics processing units Mental health Chatbots data models Tuning

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Machine Learning-based Decision Making for Charging/Discharging Cost Optimization of PREV in Smart City 1

Machine Learning-based Decision Making for Charging/Discharg...

引用

1st International Symposium on Parallel Computing and Distributed Systems, PCDS 2024

作者： Neelakantam, Gone Ecole Centrale School of Engineering Mahindra University Dept. Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350349658

Smart cities, equipped with smart grid infrastructure, establish advanced communication networks that facilitate interactions among multiple entities. It is well-known that smart grid technology ensures the reliable transmission of electricity. Additionally, Plug-in Hybrid Electric Vehicles (PHEVs) significantly contribute to the efficient utilization of energy in mobility-aware environments. The Intelligent Transportation System (ITS) enables Vehicle-to-Infrastructure (V2I) communication, which is crucial for providing transportation services and managing PHEV recharging at user-preferred locations. This capability is a key element of smart city infrastructure. To optimize the use of PHEV services in a smart city, addressing the cost minimization of charging and discharging is essential. Therefore, this paper proposes a decision tree machine learning-based algorithm within a fog computing platform aimed at minimizing charging and discharging costs. Performance evaluation demonstrates that the model outperforms existing algorithms in terms of accuracy. These results indicate that our model can accurately predict the costs associated with charging and discharging PHEVs in smart city environments. © 2024 IEEE.

关键词： Adversarial machine learning

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Detecting adversarial examples using constraint-based learning

Detecting adversarial examples using constraint-based learni...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Kumar, Vivek Kumar, Kamal Singh, Maheep National Institute of Technology Dept. of Computer Science -and Engineering Uttarakhand Pauri India IGDTUW Department of Information Technology New Delhi India Doon University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350394412

Deep learning models have been applied in various fields and continue producing exceptional results. However, these models are vulnerable to adversarial attacks, which are modified data samples maliciously crafted to cause misclassification by a target model. Fields like Network intrusion detection systems (NIDS) are security-critical;therefore, adversarial attacks pose a high risk in such applications. The attacker not only aims for evasion but also tries to launch an attack once the adversarial example has evaded detection by a NIDS. This paper presents a novel technique for detecting adversarial examples targeted towards a NIDS by learning the dependencies between features known as domain constraints. Our proposed method detects adversarial examples that violate these constraints learned during training. Compared with prevalent detection techniques, our method does not require generating adversarial examples for training, which reduces its computational requirement. We assume a grey-box threat model where the attacker can access the training data. We perform experiments on the CICIDS2017 dataset against five state-of-the-art adversarial attacks: FGSM, BIM, PGD, Deep fool and BIM. The results show that our method can detect up to 100% adversarial attacks, outperforming the contemporary schemes. © 2024 IEEE.

关键词： Adversarial machine learning

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Merging Realms: Classical and Quantum-Enhanced Deep Learning Techniques for CIFAR-10 Classification

Merging Realms: Classical and Quantum-Enhanced Deep Learning...

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Bobbili, Sai Jayanth Chandrappa, S. Reddy, V. Keshava Guru Prasad, M.S. Deepa, K. Sharma, Mansi Dept. of Computer Science and Engg. Dehradun India Dept. of Computer Science and Engineering Bengaluru India VTU Department of ECE India NMAMIT Department of ECE Nitte India

ISBN: (纸本)9798331517892

Classical and quantum-enhanced deep learning techniques are revolutionizing areas of artificial intelligence. This leads to substantial innovations in various domains, including the image processing. The CIFAR-10 dataset is a widely used and benchmarked dataset for assessing the performance of deep learning models. In this proposed method, the CIFAR-10 dataset is used for image classification in an effort to improve model performance. The modern and strong models are employed, such as CNN, InceptionV3, DenseNet, and a Hybrid Quantum-DenseNet. The precision, recall, accuracy, loss, and F1-score metrics are used in this analysis to assess these models. From the analysis, it is observed that CNN and Hybrid Quantum-DenseNet attained 77%-78% accuracy. The InceptionV3 reached the maximum accuracy of 90.15%. The performance of the developed hybrid model was exceptionally well across a range of parameters. DenseNet achieved an accuracy of up to 84%. © 2024 IEEE.

关键词： Adversarial machine learning

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Developing a Low-Cost Prosthetic Hand with Real-Time Machine Learning Capabilities 23

Developing a Low-Cost Prosthetic Hand with Real-Time Machine...

引用

8th International Conference on Sustainable Information engineering and Technology, SIET 2023

作者： Adani, M. Syakhisk Naufal Widasari, Edita Rosana Setiawan, Eko Dept. Computer Engineering Faculty of Computer Science Brawijaya University Indonesia

ISBN: (纸本)9798400708503

The global prevalence of arm amputations, particularly transradial or below-elbow amputations, presents a pressing challenge due to the high costs of commercially available prosthetic hands. This issue is particularly acute in developing countries, where financial constraints hinder access to functional prosthetic solutions. Addressing this gap, this research introduces an innovative approach that merges electromyography (EMG) signal recognition and machine learning to develop an affordable and versatile bionic below-elbow prosthetic hand. The integration of EMG signals for control, coupled with machine learning algorithms, enables accurate classification of hand movements, resulting in enhanced movement precision and a broader range of gestures. This solution overcomes limitations in existing prosthetic hands, offering advanced EMG-based control and comprehensive movement capabilities, empowering users to perform intricate tasks and regain functional autonomy. The study undertakes a comprehensive analysis of the developed system's performance, encompassing hardware, software, and machine learning components. Results show promising accuracy levels for movement classification using the K-Nearest Neighbors (KNN) machine learning method. Execution time variations are observed among subjects and gestures, influenced by factors such as subject sensitivity and gesture complexity. Energy consumption analysis reveals disparities in power usage among different movements, emphasizing the impact of movement complexity on power requirements. A cost analysis demonstrates the affordability of the prototype, positioning it as a cost-effective alternative to existing prosthetic hand options. Comparing this solution to other research and commercial prosthetic hands highlights its uniqueness, particularly its emphasis on affordability. © 2023 Owner/Author.

关键词： Nearest neighbor search

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Unveiling the Arsenal of User data Protection Tools and Practices 1

Unveiling the Arsenal of User Data Protection Tools and Prac...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Moiz, Syed Abro, Abdul Ahad Ebrahim, Mansoor Abro, Abdul Basit Iqra University Main Campus Dept. of Computer Science Karachi Pakistan Iqra University Main Campus Dept. of Software Engineering Karachi Pakistan Indus University Dept. of Computer Science Karachi Pakistan

ISBN: (纸本)9798350373042

In our rapidly evolving digital landscape, the imperative of safeguarding personal data has surged in significance. As lives increasingly intertwine with digital technologies, personal information has grown markedly, parallel to the escalating threats posed by cyberattacks and data breaches. This research paper provides a comprehensive exploration of data security and privacy, aiming to impart a profound understanding of the intrinsic value of data. It meticulously examines essential methodologies such as encryption, access control, multifactor authentication, and systems, all of which collectively form the bedrock of a resilient security framework. Additionally, this study delves into the intricate regulatory landscape that governs data protection, placing particular emphasis on well-established frameworks such as the General data Protection Regulation and the Health Insurance Portability and Accountability Act. It highlights emergent trends in data security, including the integration of artificial intelligence and machine learning for threat detection, while also addressing the distinctive challenges posed by the proliferation of Internet of Things (IoT) devices. In conclusion, this paper underscores the paramount importance of personal data protection in our digital age, offering invaluable insights and guidance for organizations and individuals endeavoring to bolster their defenses and uphold data integrity within our interconnected world. © 2024 IEEE.

关键词： data privacy

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Optimizing Job Search: A Multi-Model Approach with DRNN-BES, Collaborative Filtering, and LLM-NER Resume Analysis 3

Optimizing Job Search: A Multi-Model Approach with DRNN-BES,...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Jayakumar, Naveenkumar Patil, Netra Pokal, Roshankumar S. Dev, Rudra Pratap Nair, Sidharth Vellore Institute of Technology School of Computer Science and Engineering Vellore India College of Engineering Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350354348

This research introduces an innovative job recommendation platform designed to optimize the job search process through advanced technology integration. The platform features a sophisticated Resume Analyzer leveraging NLP and ML to meticulously extract candidate information. Coupled with a Recommendation System that employs mixture models such as DRNN-BES and collaborative filtering, it delivers personalized job suggestions tailored to individual profiles. Additionally, the system utilizes state-of-the-art web scraping technologies to continuously update its database with diverse and current job listings from various online sources, ensuring a wide range of opportunities are accessible. This integration of technologies not only refines job-matching precision but also significantly improves the efficiency of connecting job seekers with appropriate opportunities. Initial experimental results underscore the enhanced effectiveness and accuracy of our recommendation system, particularly when incorporating user behavioral data into the collaborative filtering algorithm. This paper demonstrates the transformative potential of integrating AI into job seeking, offering a scalable, unbiased, and time-efficient approach to modern employment challenges. © 2024 IEEE.

关键词： Collaborative filtering

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Challenges of Integrating Blockchain Technology into Educational Institutions

Challenges of Integrating Blockchain Technology into Educati...

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Ali, Abbas Hussein Yavanoǧlu, Uraz Graduate School of Informatics Gazi University Dept. Of Computer Science Ankara Turkey Gazi University Faculty of Engineering Dept. of Computer Engineering Ankara Turkey

ISBN: (纸本)9798350364910

The latest advancements in blockchain technology have significantly influenced several sectors, such as banking, healthcare, and supply chain networks. Because of its distinct attributes, like decentralization, trustworthiness, and security, this technology has recently been employed in the field of education. While the blockchain is bright, several problems, such as legal, immutability, and scalability questions, continue to hinder its adoption in education. In this paper, we look at educational application system in terms of blockchain-based solutions that have been created for educational purposes, as well as the potential challenges of blockchain technology in the field of education. © 2024 IEEE.

关键词： Blockchain technology educational application

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AI-Enabled Cyber Physical System And Battery Life Estimation For Smart Grid Applications 49

AI-Enabled Cyber Physical System And Battery Life Estimation...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： N.k, Anantha Padmanabhan Kumar, Aman Chitransh, Varun Singh, Rajeev Kumar Lal, Vivek Nandan Singh, Sanjay Kumar Dept. of Computer Science and Engineering Varanasi India Dept. of Electrical Engineering Varanasi India

ISBN: (纸本)9798350331820

In this paper, the development of an efficient three- layer Cyber Physical System (CPS) using a multi-output power electronic interface integrated with IoT-enabled modules and AI- based algorithms is presented. The power electronic interface layer of the proposed CPS eliminates the need of bulky electrolytic capacitors and reduces the switching loss. This layer facilitates DC to DC and DC to three-phase AC power conversion for battery charging and smart-grid applications respectively. AI-based algorithms are utilized for accurately predicting the Remaining Useful Life (RUL) of batteries. The performance of the proposed CPS is validated through simulation and experimental measurement results using a 700 W prototype system. The measurement results show that the proposed CPS enables real-time monitoring, accurate prediction of RUL, predictive maintenance decisions, data analytics, and optimized power flow. © 2023 IEEE.

关键词： Cyber Physical System

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