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1st International Conference on Cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Saranya, P. Viji, D. Jangiti, Aditya School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai India School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9798350361780

Nowadays, smart healthcare appliances generate vast amount of medical data. Such huge amount of data requires classification methodologies, through which disease diagnostics can be carried out. Chronic diseases like cancer can be predicted in advance to help the patients to take treatment in initial stages of the disease. This article proposes an improvised feature selection and classification -method for cancer prediction. Here we propose an enhanced fruit fly optimization algorithm in KNN classifier for searching strategy with wrapper methods for feature selection. Lung cancer data set, breast cancer data set and leukemia data set has been taken from the UCI repository for testing the performance of the proposed model. For Lung cancer data set, accuracy, specificity, sensitivity, and fscore value observed are 96.97 %, 96.28 %, 96.35 %, and 96.34% respectively. For breast cancer dataset, accuracy, specificity, sensitivity, and fscore value observed are 99.97 %, 98.2 %, 96.85 %, and 90.35% respectively. For Breast cancer dataset, accuracy, specificity, sensitivity, and fscore value observed are 96.67%, 98.28 %, 95.85 %, and 93.85% respectively. The proposed method works well with various dataset and shows prominent accuracy improvement over the existing systems. © 2024 IEEE.

关键词： Optimization

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NetProbe: Deep Learning-Driven DDoS Detection with a Two-Tiered Mitigation Strategy 25

NetProbe: Deep Learning-Driven DDoS Detection with a Two-Tie...

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26th International Conference on Distributed computing and Networking, ICDCN 2025

作者： Jha, Prianshu Singh, Gurpreet Kumar, Amit Agrawal, Dewesh Patel, Yashwant Singh Forough, Javad Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Computing Science Umea University Umeå Sweden

ISBN: (纸本)9798400710629

Web servers are the backbone of modern Internet infrastructure, serving as the primary medium for online information distribution. Despite their critical role, web servers are susceptible to cyber-attacks. While current firewall mechanisms provide some level of protection against cyber threats, the evolving nature of these attacks and emerging vulnerabilities continue to pose significant risks. One of the most prevalent yet lethal attacks known today is DDoS (Distributed Denial of Service) attacks. These growing risks emphasize the urgent need for dynamic and robust threat detection and mitigation systems. This paper presents a comparative analysis of ensemble learning models (e.g., Random Forest, XGBoost, and LightGBM) and neural network-based models (e.g., Graph Neural Networks (GNN), Long Short-Term Memory networks (LSTM) with attention layers, and Gated Recurrent Units (GRU)) for DDoS attack detection and classification. Based on this analysis, we propose a real-time DDoS attack detection system integrated with a mitigation mechanism. The proposed system utilizes a two-tiered mitigation strategy assisted by UFW (Uncomplicated Firewall) and Apache server configuration files to block the incoming and outgoing traffic associated with suspicious IP addresses. The system's overall complexity, integrating both detection and response processes, ensures its efficiency in real-time environments while handling large volumes of traffic. Furthermore, the proposed approach achieves 15% improvement in detection accuracy and 20% reduction in false positives compared to traditional techniques, making it an effective and scalable solution for modern web server security. © 2025 Copyright held by the owner/author(s).

关键词： Decision trees

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VitalLink: NodeMCU IoT Solution for Vital Signs Surveillance 7

VitalLink: NodeMCU IoT Solution for Vital Signs Surveillance

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Ramitha Rajeswari, J. Chamanthi, Reddy Mary Posonia, A. Albert Mayan, J. Sathyabama Institute of Science And Technology Department of Computer Science And Engineering Chennai India Sathyabama Institute of Science And Technology School of Computing Chennai India

ISBN: (纸本)9798350359299

This research study proposes the development of comprehensive vital sign surveillance designed for real-time patient assessment. The system integrates multiple sensors, including the LM35 temperature sensor, Heartbeat Rate (HBR) and Blood Oxygen (SpO2) Sensor, DHT11 humidity and temperature sensor, OLED display, and gyroscope sensor, to provide a holistic approach to vital signs. The LM35 sensor is employed for accurate temperature measurement, providing crucial data for monitoring the patient's thermal status. This information is essential for detecting fever or abnormal temperature variations, aiding in the early identification of potential health issues. The HBR and SpO2 sensor plays a vital role in cardiovascular vital signs. The system can assess the patient's cardiac and respiratory well-being by measuring the heart rate and blood oxygen levels. Abnormalities in these parameters can trigger timely alerts, enabling swift medical intervention. The DHT11 sensor enhances the system's capabilities by monitoring ambient temperature and humidity. This additional environmental data contributes to a more comprehensive understanding of the patient's surroundings, ensuring that external factors are considered in the health assessment process. Integrating an OLED display serves as an intuitive user interface, providing real-time feedback on the measured parameters. This ensures that patients and healthcare providers can easily interpret and respond to the health data presented by the system. The display can showcase temperature, humidity, heart rate, blood oxygen levels, and other relevant information in a user-friendly format. Furthermore, the gyroscope sensor adds a layer of sophistication to vital sign surveillance by enabling the assessment of body movement and posture. This feature is particularly beneficial for patients with mobility issues or those prone to falls. The gyroscope data can be analyzed to detect sudden movements or abnormal postures, triggering alerts

关键词： Heart

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Unravelling State-wise Crime Patterns with Ensemble Machine Learning and Data Visualization for Public Safety 2

Unravelling State-wise Crime Patterns with Ensemble Machine ...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Sri Harsha, M.V.R. Akhil, M.H.S. Ronald Doni, A. Albert Mayan, J. Yovan Felix, A. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sathyabama Institute of Science and Technology School of Computing Chennai India

ISBN: (纸本)9798350349221

This study, detailed below, introduces a new method of handling crimes in particular states, using ensemble machine learning and data visualization techniques in particular, as a way to enhance public safety. Crime detection is a hard task for two concerns, the complexities of the crime and the enormity of the data. By means of random forest and AdaBoost ensemble learning techniques we can model sophisticated problems of the relationship between features such as crime data as a way to match predictions. The other point we stress is data visualization which allows us to comprehend the patterns and connections that have been derived from the ensemble learning process. In addition to the analysis of local crime data, the hybrid approach that we propose involves the inclusion of national crime data to make the identification of hidden crime patterns at state level possible. This move will enable the law enforcement agencies to gain a better understanding of the activities and to effectively respond to the crimes. The proposed methodology represents a, robust and efficient solution to statewise crime detection utilizing the advantages of machine learning and mapping tools. © 2024 IEEE.

关键词： Crime

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SecuDroid : Android Malware Detection using ML classifier on Static Features 2

SecuDroid : Android Malware Detection using ML classifier on...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Vanusha, D. Singh, Shashank Jha, Abhijeet Kumar Delsi Robinsha, S. School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350365269

The Android operating system's widespread use has unfortunately made it a target for malware attacks, underlining the necessity for robust detection strategies. Existing literature has explored the potential of machine learning in combating Android malware, often employing a mix of static and dynamic analyses. However, a major portion of these malwares are zero-day vulnerabilities. In this context, our study makes a significant contribution by focusing on using static features extracted from Android application package (APK) files to differentiate between benign and malicious applications. Specifically, all training and testing in this study were performed on the Drebin-215 dataset, a comprehensive collection known for its relevance in Android security research. Improving malware detection using static features allows for detection of malwares before they are run on devices. We analyzed five ML classification algorithms, beginning with a Decision Tree, followed by SVM (Support Vector Machine) with an RBF kernel, then moving to Logistic Regression and K-Nearest Neighbor, and finally, we introduced our proposed model, the SecuDroid Neural Network - employing a methodology that employs precise hyper-parameter tuning, and evaluation through stratified 4-fold cross-validation. The evaluation on an unseen test dataset, based on accuracy, F1-score, and AUC metrics, revealed the SecuDroid Neural Network surpassed its counterparts by reaching an accuracy of 0.993, an F1-score of 0.991, and an AUC of 0.999. Our findings not only offer a comparative analysis of different machine learning models for malware detection but also introduce an efficient approach, thereby enriching the arsenal against Android malware threats, with all empirical validations grounded in the Drebin-215 dataset. © 2024 IEEE.

关键词： Android malware

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Performance Evaluation of Biometric Authentication Using Fragment Jaya Optimizer-Based Deep CNN with Multi-kernel SVM

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SN Computer science 2024年第4期5卷 337页

作者： Umasankari, N. Muthukumar, B. Shanmuganathan, C. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India School of Computing Department of CSE Sathyabama Institute of Science and Technology Chennai India Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

The earlier research clearly indicated that the bimodal authentication system has more efficiency than unimodal and multimodal. This is due to the reason for the best intact biometric traits of fingerprint and retina. There is a chance to additionally improve further performance of the proposed biometric trait combination by additional or alternate algorithms or methodologies. Therefore, in this research work, the multi-kernel support vector machine (MK-SVM), a machine learning classification approach, is proposed and is used for the implementation. In addition, a hybrid algorithm of fragment Jaya optimizer-based deep convolutional neural network (FJO-DCNN) approach is also used to improve the performance value for bimodal biometric authentication and classification. The recognition systems analyze both biometrics independently, and their conclusions are combined to determine whether to give or refuse access to the user in the end. According to the findings of the implementation, this work demonstrates more dependability than the cascaded biometric system. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Bimodal biometric authentication Deep convolutional neural network Fragment Jaya optimizer convolutional neural network Multi-kernel support vector machine

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Adversarial Attacks Using Carlini Wagner

Adversarial Attacks Using Carlini Wagner

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Baddukonda, Tarun Aditya Kanakam, Ronit Kumar Yasotha, B. Srm Institute of Science and Technology Department of Computer Science and Engineering Kaatankulathur Tamilnadu Chennai India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kaatankulathur Tamilnadu Chennai India

ISBN: (纸本)9798350382693

Adversarial attacks in the Artificial Intelligence have received extensive coverage in the latest years. Such attacks have affected deep learning models and neural networks due to the weaknesses that are part of the AI systems. This exposure has seen classification errors compromises and the undermining of the model performance. Understand the critical mechanisms of adversarial attacks is critical to developing strong defense mechanisms that would protect AI systems from the malevolent interventions. This abstract provides a summary of adversarial attacks as a prevalent threat to the dependability and resilience of AI technologies thus the need for continuous research and development. © 2024 IEEE.

关键词： Adversarial machine learning

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Security of Images Using Pascal's Triangle Chaotic Encryption Scheme and with Biometric Authentication in Cloud Storage 4

Security of Images Using Pascal's Triangle Chaotic Encryptio...

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4th International Conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Gupta, Raghav Gullapalli, Manasa Suseela, G. School of Computing Science and Engineering Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Kattankulathur603203 India School of Computing Science and Engineering Faculty of Engineering and Technology Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350343670

A key component of cloud computing is cloud storage, which allows data to be stored on remote servers. The result is a reduction in the amount of storage space required on local devices, allowing data to be accessed from anywhere with an internet connection. Furthermore, cloud storage is more secure than local storage due to the use of encryption and other security measures by professional providers. This is a practical and cost-effective solution to store large images required to be safeguarded from malicious black hat hackers. Images are the most valuable yet vulnerable form of data that can be used to track down user activities and taken advantage by hackers, used to commit identity theft, fraud. Issues concerning the security and privacy of images saved in the cloud have arisen as cloud storage has grown in popularity. The proposed method will focus on the encryption of images using a chaotic encryption scheme based on Pascal's triangle. The user will authenticate their identity before uploading images to cloud storage and likewise confirm their authentication in the process of retrieval of an image. Such a secure system is better than traditional security methods for password settings as it cannot be easily stolen. Additionally, this method is more secure than other methods due to its complex encryption mechanisms, making it impossible to guess or steal the encryption key. In addition to providing extra security for sensitive images, biometric authentication will offer a stronger method of verifying and validating users on the cloud. © 2024 IEEE.

关键词： AWS Rekognition Chaotic systems Cloud Security Confusion Diffusion Facial Authentication Image Encryption Image Shuffling Pascal's Triangle Transform

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Bridging the Semantic Gap: A Deep Learning Framework for Semantic Video Search Integrating Vision and Language for Enhanced Video Retrieval 10

Bridging the Semantic Gap: A Deep Learning Framework for Sem...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Annamalai, J. Arjun Sneha, S. Sharon Ashokkumar, C. Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Computing Technologies Chennai Kattankulathur603203 India

ISBN: (纸本)9798350353068

Traditional video search engines often rely on tags or manual annotations for content retrieval, limiting the accuracy and efficiency of search results. Moreover, keyword-centric searches may not adeptly capture the nuanced and intricate queries users pose when seeking specific video content. The envisioned video search system integrates machine learning and natural language processing components to enable efficient and effective video retrieval based on user queries. The pipeline includes video processing employing Vision Transformer with GPT-2 (ViT-GPT2) architecture to analyze intricate details within video frames, Speech-to-Text (STT) models for transcribing spoken content, and the combination of transcriptions with video captions to form textual descriptions. These descriptions are then embedded using BERT, a transformer-based model, to grasp contextual relationships. Semantic matching is achieved through deep learning models, and a ranking mechanism based on similarity scores facilitates efficient retrieval. Leveraging datasets such as the MSR-VTT dataset, spanning visual recognition domains, contributes to the evaluation and training of the system's components. The system's efficacy is evaluated using metrics such as precision, recall, F1-score, and ranking metrics, with datasets spanning visual recognition, speech transcription, and semantic similarity domains. The comprehensive evaluation approach ensures the robustness and relevance of the video search system, aligning with user expectations and real-world use cases. © 2024 IEEE.

关键词： Semantics

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H-hop independently submodular maximization problem with curvature

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High-Confidence computing 2024年第3期4卷 75-81页

作者： Yang Lv Chenchen Wu Dachuan Xu Ruiqi Yang Beijing Institute for Scientific and Engineering Computing Beijing University of TechnologyBeijing 100124China College of Science Tianjin University of TechnologyTianjin 300382China

The Connected Sensor Problem(CSP)presents a prevalent challenge in the realms of communication and Internet of Things(IoT)*** primary aim is to maximize the coverage of users while maintaining connectivity among K *** the challenge of managing a large user base alongside a finite number of candidate locations,this paper proposes an extension to the CSP:the h-hop independently submodular maximization problem characterized by curvatureα.We have developed an approximation algorithm that achieves a ratio of 1−e−α/(2h+3)α.The efficacy of this algorithm is demonstrated on the CSP,where it shows superior performance over existing algorithms,marked by an average enhancement of 8.4%.

关键词： Internet of things UAV communication Approximation algorithms

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