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1st International Conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Dutta, Sourav Sharma, Anjali Rajgor, Jaymin Lovely Professional University Punjab Dept. of Computer Science and Engg. Kolkata India Jss Academy of Technical Education Dept. of Computer Science and Engg. Bangalore India Shan and Anchor Kutchhi Engineering College Dept. of Computer Science and Engg. Mumbai India

ISBN: (纸本)9798350307795

In recent times, blockchain technology, especially Ethereum, has gained tremendous popularity for supporting various financial transactions. Ethereum, the second most prominent cryptocurrency platform after Bitcoin, processes over one million transactions daily, known for its high security and ease of use. However, despite its robust security features, Ethereum faces challenges with illegal activities, which hinder its widespread adoption. To address this issue, this study focuses on detecting illicit activities on the Ethereum blockchain, particularly identifying illegal accounts using machine learning techniques. It proposes a novel method combining a convolutional neural network with an XGBoost classifier, aimed at differentiating between normal and illicit accounts based on transaction history. The XGBoost model, a tree-based ensemble classifier, not only improves accuracy but also prevents overfitting and enhances the model's generalizability. Its parallel tree-building feature also speeds up training, making the model more scalable. The model is trained on a balanced dataset of over 4,000 Ethereum accounts, encompassing both normal and fraudulent accounts. The results are promising, with the model achieving an accuracy of 99.7% and an average AUC of 0.9998 (std: 0.0008), outperforming standard machine learning models. This approach shows significant potential in combating illegal activities within the Ethereum blockchain. © 2024 IEEE.

关键词： Blockchain

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Cloud Cryptography: Mechanism of Different Encryption Standards 18

Cloud Cryptography: Mechanism of Different Encryption Standa...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Lanke, Ravikumar Md Zubair Rahman, A.M.J. Bhardwaj, Rajat Somashekhara Reddy, D. Jain, Parth Mahesh, T.R. Dept. of Computer Science and Engineering Bangalore India Al-Ameen Engineering College Karundevanpalayam Erode India Aset Amity University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9789380544519

The term 'cloud cryptography' describes the application of cryptographic methods and protocols to protect communications and data in cloud computing settings. The provision of computing services, including storage, processing power, and applications, via the internet is known as cloud computing. Although cloud computing has numerous advantages, such as cost-effectiveness and scalability, it also poses security risks because sensitive data is processed and stored outside of the conventional network borders of an organization. Cloud cryptography plays a pivotal role in cloud security by safeguarding against unauthorized access, data disclosure, modification, or destruction. Keeping up with the most recent cryptographic methods and protocols is crucial to addressing new threats and weaknesses in this industry, which is always changing. Organizations can safeguard their data from unauthorized access, modification, and destruction by utilizing encryption techniques. This article explores the cloud cryptography along with its different encryption standards. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Architecture Benefits Cloud Computing Cryptography Encryption

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Federated Learning for Cybersecurity: A Promising Frontier 2

Federated Learning for Cybersecurity: A Promising Frontier

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Ananya, G. Bindu Madhavi, V. Murthy, Monisha Guruprasad, N. Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Buddha Institute of Technology Computer Science and Engineering Uttar Pradesh Gorakhpur India

ISBN: (纸本)9798350368109

In today's world, Artificial Intelligence and Machine Learning are transforming many industries, but they rely on huge amounts of data leading to privacy issues. A better alternative is Federated learning. Federated Learning involves training the model from multiple sources using the decentralization technique, meaning each device trains the model on its local data thereby reducing the strain on the single server. This is useful in cases where the data is too large to be sent and maintained on a central server or in handling privacy and security concerns. Cybersecurity is a prime domain where vulnerable attacks and breaches can be prevented using this technique. Other domains like finance, healthcare, IoT etc. have transformed the idea of data-driven decisions. This paper gives an insight into the concept of Federated learning and why it is a better choice. It includes the various algorithms that can be implemented in multiple applications depicted through a comparative analysis. The choice of algorithm depends upon its efficiency and the desired cybersecurity application. © 2024 IEEE.

关键词： Federated learning

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JARVIS - Smart AI Support System 3

JARVIS - Smart AI Support System

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3rd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Ramya Manaswi, V. Saketh, Meduri Ravi Tejha, Mandumula Sai Karthikeya, Atluri Sai Prakash, Dharani Chandra Alkhayyat, Ahmad Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science Engineering Hyderabad India Gitam University Dept. of Computer Science Engineering with Specialisation of Cybersecurity Hyderabad India K.G Reddy College of Engineering and Technology Dept. of Electronics & Communication Engineering Telangana Hyderabad India College of Technical Engineering The Islamic University Dept. of Computers Techniques Engineering Najaf Iraq

ISBN: (纸本)9798350354379

The JARVIS AI Support System represents a remarkable fusion of modern technology, blending a sophisticated GUI design, seamless voice control, and inventive features like the captivating 'Air Canvas' facilitated by OpenCV. This AI-driven virtual assistant offers users a natural and intuitive experience, allowing them to effortlessly perform tasks such as browsing the web, interacting with a chatbot, and executing dynamic voice- controlled actions. Moreover, the system showcases advanced capabilities including motion detection and facial recognition with an accuracy of 95% in multiple runs. Leveraging the power of computer vision, the Air Canvas feature empowers users to express creativity through fluid hand gestures, while voice commands effortlessly manage diverse tasks. This innovative project presents an approachable way to interact with technology in the world of AI. © 2024 IEEE.

关键词： Face recognition

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Predicting DDoS Attacks: A Machine Learning Approach using ALDDoS Dataset 15

Predicting DDoS Attacks: A Machine Learning Approach using A...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shookdeb, Shantanu Muduli, Debendra Bhatta, Sandesh Adhikari, Aayush Sapkota, Aaron Chaturvedi, Prabhat C V Raman Global University Dept. of computer Science and Eng. Bhubaneswar India C V Raman Global University Dept. of computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350370249

The DDoS attack stands as Distributed Denial of Service attack refers a harmful effort to intercept on the regular traffic of a targeted web service or server. DDoS attack interferes with the normal network flow of the targeted server, potentially causing significant disruption to its *** send an incoming packet to every node except the arrived node,it used a non-adaptive routing algorithm. DDoS attacks are executed by using multiple compromised systems, which included combination of multiple computers system and other IoT device resources to make a large traffic on targeted network, rendering it inaccessible to legitimate users. In our study we experimented with various machine learning model like as RandomForest, DecisionTree, Xgbbost etc. with a high accuarcy of 99.99% to predict the DDoS attack with publicly available ALDDoS (Application-Layer DDoS) Dataset by Warda (Owner). © 2024 IEEE.

关键词： ALDDos Cyber defense DDoS RandomForest Web-proxy

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Enhanced Classification of Alzheimer's Disease Using Deep Learning and Machine Learning Models with MRI Scans

Enhanced Classification of Alzheimer's Disease Using Deep Le...

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2024 International Conference on Frontiers of Information Technology, FIT 2024

作者： Salahudin, Amna Qadeer, Eeman Ahmed, Ayesha Fahad, Labiba Gillani Dept. of Software Engineering FAST National University of Computer and Emerging Sciences Islamabad Pakistan Dept. of Computer Science FAST National University of Computer and Emerging Sciences Islamabad Pakistan

ISBN: (纸本)9798331510503

Alzheimer's disease (AD) is a slowly progressive neurodegenerative disease, which necessitates early and accurate diagnosis for effective management. This research presents a comprehensive approach to the classification of Alzheimer's disease using Magnetic Resonance Imaging (MRI) scans, focusing on four distinct classes: nondemented, mild, very mild, and moderate demented. Our main goals are to combine diverse datasets to improve the robustness and adaptability of the model and to fully investigate Alzheimer's disease in all four stages. To achieve these goals, we employed a combination of machine learning and deep learning models. The models utilized include Histogram of Oriented Gradients (HOG) with Support Vector Machine (SVM), Gabor filters with SVM, Gabor-Gray Level Co-occurrence Matrix (GLCM) with SVM, ResNet50, AlexNet, and InceptionV3. Among these, the InceptionV3 model achieved the highest accuracy of 96.20% using 5-fold cross-validation followed the Resnet50 model achieving accuracy of 95.43%. Our approach addresses key challenges such as class imbalance, high intra-class variation, inter-class similarities, and dataset variability, demonstrating significant potential in improving Alzheimer's disease classification and detection. © 2024 IEEE.

关键词： Gabor filters

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A Time-varying Velocity Control Strategy for Balancing Exploration and Exploitation Behaviour of a Binary PSO

A Time-varying Velocity Control Strategy for Balancing Explo...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Siddique, Md. Abu Bakkar Islam, Md. Jakirul DUET Gazipur Dept. of Computer Science and Engineering Gazipur Bangladesh

ISBN: (纸本)9798350316001

Combinatorial optimization problems (COPs) are NP-hard, and their solution space is discrete, which poses a significant challenge for optimization algorithms. Binary Particle Swarm Optimization (BPSO) is wel-known optimization technique that has been widely applied in various type of COPs. However, one of the challenges in BPSO is to balance exploration and exploitation to locate the optimal solution. This paper presents a Time-varying Velocity Control (TVC) strategy to balance exploration and exploitation in a BPSO. The TVC strategy utilizes a time-varying control parameter to adjust the velocity of the particles, allowing for more exploration in the early phases of the optimization process and more exploitation as the search progresses. The proposed strategy is evaluated on a set of benchmark combinatorial optimization problems, and the results demonstrate that it outperforms other commonly used BPSO strategies in terms of both efficiency and solution quality. Furthermore, the TVC strategy is shown to be robust to changes in the problem landscape, making it a promising approach that can be used for solving a wide range of combinatorial optimization problems. © 2023 IEEE.

关键词： Combinatorial optimization

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Suitable and Style-Consistent Multi-Texture Recommendation for Cartoon Illustrations

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ACM Transactions on Multimedia Computing, Communications and Applications 2024年第7期20卷 1-26页

作者： Wu, Huisi Wang, Zhaoze Li, Yifan Liu, Xueting Lee, Tong-Yee College of Computer Science and Software Engineering Shenzhen University No. 3688 Nanhai Road Nanshan District Guangdong Shenzhen518060 China Dept. of Computer Science and Information Engineering National Cheng-Kung University No. 1 University Road Tainan70101 Taiwan

Texture plays an important role in cartoon illustrations to display object materials and enrich visual experiences. Unfortunately, manually designing and drawing an appropriate texture is not easy even for proficient artists, let alone novice or amateur people. While there exist tons of textures on the Internet, it is not easy to pick an appropriate one using traditional text-based search engines. Although several texture pickers have been proposed, they still require the users to browse the textures by themselves, which is still labor-intensive and time-consuming. In this article, an automatic texture recommendation system is proposed for recommending multiple textures to replace a set of user-specified regions in a cartoon illustration with visually pleasant look. Two measurements, the suitability measurement and the style-consistency measurement, are proposed to make sure that the recommended textures are suitable for cartoon illustration and at the same time mutually consistent in style. The suitability is measured based on the synthesizability, cartoonity, and region fitness of textures. The style-consistency is predicted using a learning-based solution since it is subjective to judge whether two textures are consistent in style. An optimization problem is formulated and solved via the genetic algorithm. Our method is validated on various cartoon illustrations, and convincing results are obtained. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Textures

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Unique Approach of Contextual Uniqueness Assessment of Research Papers Using Multi-Label Classification and Similarity Detection 26

Unique Approach of Contextual Uniqueness Assessment of Resea...

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

作者： Tanvir, Sifat Islam, Md. Zihadul Moon Taher, Sidrat Muztahid, Shaikh Adib Biswas, Arpon Shopnil, Md. Shahariar Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

Research papers should possess a significant amount of novelty. However, proper paraphrasing of entire paper may not be identified as plagiarism. Therefore, in this work a unique approach of similarity detection technique has been proposed. At first the abstracts are used to classify which area the paper belongs to, then papers of the same area are extracted and then contextual similarity is detected using cosine similarity. For training and testing purposes, two datasets have been created using data from IEEE Xplore. Since, almost every paper has some elements of paraphrased contents, therefore, a maximum acceptable threshold has also been proposed in this paper. Maintaining the maximum threshold, how well cosine similarity was able to distinguish the novel, paraphrased, and fully plagiarised works has also been analyzed. © 2023 IEEE.

关键词： Contextual Similarity Detection Cosine Similarity Deep Learning Multi-Label Classification

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Accurate Detection of Aquatic Debris in Ocean Surfaces using AlexNet Algorithm over ResNet-50 Algorithm 3

Accurate Detection of Aquatic Debris in Ocean Surfaces using...

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3rd International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2023

作者： Kamal, T.M. Bharatha Devi, N. Saveetha School of Engineering Dept. of Computer Science Chennai India

ISBN: (纸本)9798350306989

This research study intends to detect the aquatic debris accurately on Ocean surfaces using Novel AlexNet algorithm in comparison with ResNet-50 algorithm. Novel AlexNet and ResNet-50 are the two groups considered for this research with 20 iterations done for each group whose G-power is 80% and confidence level is 95%. The dataset consists of 724 JPG file format images depicting clear ocean and 324 JPG file format images depicting aquatic debris, including images with noise. This work demonstrates that the Novel AlexNet algorithm achieves an accuracy of 93.77% which identifies objects accurately over the ResNet50 algorithm with an accuracy of 91.47% and there is no statistical significance difference between the Novel Alexnet Algorithm and ResNet-50 Algorithm for the value of p=0.076 (Independent Sample T-test p © 2023 IEEE.

关键词： Debris

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