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Shallow-Sec: Malware Detection in Real-Time Devices using Feature Weightage and Shallow Learning Models

Shallow-Sec: Malware Detection in Real-Time Devices using Fe...

引用

Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Panaganti Priyanka Udhayakumar K. Deepak Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

In the domain of mobile security, particularly in APK (Android Application Package) malware detection, ensuring robust threat identification is critical for safeguarding user devices and sensitive data. This study addresses the need for a reliable Mobile Intrusion Detection System (MIDS) by harnessing advanced machine learning techniques. We advocate for employing tested models such as Logistic Regression, Decision Tree, and XGBClassifier, alongside the top-performing Random Forest (RF) model, which achieved an impressive accuracy of ninety-eight-point six percent. To enhance model performance, we utilize feature selection techniques like Recursive Feature Elimination (RFE) and hyperparameter optimization. The datasets chosen for training and evaluation, including real-world malicious and benign APKs, yield accuracies around ninety five percent. Furthermore, diverse APK datasets, including benchmarks and real-time streams, are used for rigorous testing. By leveraging optimized models and features, our study ensures exceptional performance in detecting various forms of APK malware, thus fortifying the reliability and efficacy of MIDS in real-world scenarios.

关键词： Performance evaluation Training Accuracy Intrusion detection Feature extraction Real-time systems Malware Reliability Random forests Streams

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Petri Net Model of MITM Attack Based on NDP Protocol

Petri Net Model of MITM Attack Based on NDP Protocol

引用

2022 International Conference on Networking and Network Applications, NaNA 2022

作者： Zhang, Liumei Han, Yu Wang, Yichuan Quan, Ruiqin School of Computer Science Xi’an Shiyou University Xi’an China School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China Xinjiang Institute of Engineering China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

Neighbor Discovery Protocol (NDP) is one of the core protocols of IPv6 networks. Since NDP messages are an unauthenticated stateless protocol, they are vulnerable to various types of attacks, and Man-In-The-Middle (MITM) attacks are one of the most well-known attacks in the computer field. During NDP address resolution, attackers change their own IP-MAC mapping relationships by sniffing NA (Neighbor Advertisement) messages, thus spoofing the source host's neighbor cache table and compromising the confidentiality, integrity and availability of the IPv6 network. Therefore, this paper focuses on the MITM attack in the NDP address resolution process, and performs a fine-grained analysis and Petri Net modelling of the attack process. © 2022 IEEE.

关键词： Petri nets

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Precise Lake: A Feedback and Visualization System For Optimizing Code Health

Precise Lake: A Feedback and Visualization System For Optimi...

引用

International Conference on computing and Networking Technology (ICCNT)

作者： S Shreya Shree Priyanka Prakash Katariya S Sruthi Meena Belwal Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The creation of efficient and maintainable code is a crucial aspect of software development. It is a daunting task to write code that does not smell, especially for novice programmers who lack familiarity with best practices for writing clean code. Despite ongoing research into code optimization, there is a significant gap in teaching developers how to write code that minimizes runtime and memory usage. With Precise Lake, a novel code analysis tool, these challenges can be solved with the identification and optimization of latent code like unused imports, functions and variables, inefficient patterns, and much more in the form of a code health analyzer and optimizer. Obtaining an experimental average gain of 56% in terms of memory usage and 21% with respect to runtime, it aims at bridging the gap between the writing of inefficient code and the manual reviewing step done at the end by automating the process for programmers to scan their code files and receive a report on the areas of improvement.

关键词： Visualization Codes Runtime Education Manuals Writing Lakes Optimization Best practices Software development management

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A PySpark Based Scalable Model for Churn Prediction using Embedding Models

A PySpark Based Scalable Model for Churn Prediction using Em...

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Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Sreyas Ramesh B N Sukanth Sathyavarapu Sri Jaswanth Manju Venugopalan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

This study addresses the significant challenge of customer churn, the challenge of customers stopping their association with a service or product through the integration of advanced Natural Language Processing, PySpark, and machine learning techniques. Our dataset comprises a combination of structured and unstructured data, necessitating preprocessing of the data to enhance the predictive accuracy of churn. We employ TF-IDF for feature extraction and implement two distinct word embeddings methods, Word2Vec and SBERT. Leveraging PySpark’s distributed computing capabilities, we efficiently handle large datasets ensuring scalability. Utilizing five machine learning models which include Logistic Regression, Decision Tree, Random Forest, SVM, and Gradient Boosted Trees, we observe that Decision Tree and Gradient Boosted Tree consistently deliver promising results. Notably, the combination of Gradient Boosted Tree and SBERT emerges as the top-performing model across all methods, with accuracy and Fl-score at 0.94. This comprehensive approach, integrating preprocessing, advanced NLP, and PySpark’s scalability, offers businesses a powerful tool to analyze customer interactions, predict potential churn, and ultimately improve customer retention and overall performance.

关键词： Support vector machines Logistic regression Accuracy Computational modeling Scalability Predictive models Natural language processing

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Can Technical Festivals Help in Attaining Undergraduate engineering Programme Outcomes?

Can Technical Festivals Help in Attaining Undergraduate Engi...

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IEEE International Conference on Teaching, Assessment and Learning for engineering (TALE)

作者： Dhanya M Dhanalakshmy Ritwik Murali Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350376234

ISBN: (纸本)9798350376241

The curriculum framework of an undergraduate engineering programme contains clearly defined learning outcomes. It is expected that students who graduate from a specific degree / diploma are able to attain these goals. Towards this, while not mandatory, every institution is encouraged to also host technical events such as technical festivals, workshops, technical competitions, hackathons, etc. This work explores the challenges and merits in leveraging undergraduate technical festivals to enable an evaluation of students towards the attainment of qualitative Programme Outcome. Using qualitative analysis strategies including surveys and one-on-one interviews, this work shows that technical festivals provide unparalleled hands-on experience in medium to large-scale project management, encompassing crucial disciplines that are often overlooked in the classroom evaluation rubrics. The major takeaways from organizing a technical festival can be summarized as large-scale organizing of events, better time and people management, improved communication skills, and the positive attitude / ability to learn new things. This work also mapped the specific programme outcomes to the actual learning that could be attained though the organization of a technical festival. This work also highlights the importance of such events in order to bridge the gap between industry & academia while being able to assess qualitative outcomes effectively.

关键词： Industries Surveys engineering profession Project management Organizations Financial management Real-time systems Problem-solving Interviews Hackathon

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Gaze Based Online Examination Cheat Detection with future integration of Deep Learning Models

Gaze Based Online Examination Cheat Detection with future in...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： A Padmavathi D. Prasanth Kumar K. Nithish Kumar Reddy Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

The popularity of online exams increasing rapidly because of this rapid change in education has resulted in both convenience and integrity concerns. but with this raise in popularity of online exams comes a big problem. how to maintain exam security and integrity in a connected and ever-evolving technology environment? the students also finding their own strategies over the time for cheating in online examinations. Online exams cheating has been developed into complex and difficult problem which includes techniques like false behavioral actions and accessing the unauthorized sites to find the solutions to the exam questions.

关键词： Deep learning Visualization Education Market research Data models Calibration Security

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Comprehensive Music Genre Classification: Exploring Advanced Machine Learning Techniques with the GTZAN Dataset

Comprehensive Music Genre Classification: Exploring Advanced...

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Global Conference for Advancement in Technology (GCAT)

作者： Meghraj G K Y Nishanth Akshat Kuttan Manju Venugopalan Dept of Computer Science and Engineering Amrita School of Computing Bangalore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

Music is a universal language that comes in a wide variety of genres to suit different interests and moods. In order to achieve this organization, music genre classification—the process of automatically classifying a piece of music according to its auditory content—is essential. A multi-class music genre identification model has been constructed in the proposed study, and ten distinct classifiers, including K-NN, Random Forest, Cat Boost, XG Boost, Decision Tree, etc., have been tested. A popular benchmark dataset for classifying musical genres, the GTZAN dataset offers a wealth of information for developing and assessing classification algorithms. In contrast, CatBoost reports the best outcome, with an F1-score of 0.89.

关键词： Mood Music Organizations Benchmark testing Classification algorithms Decision trees Random forests

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Identifying Sentiment in Legal Case Judgments using Random Forest Classifier

Identifying Sentiment in Legal Case Judgments using Random F...

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International Conference for Convergence of Technology (I2CT)

作者： Bhupathi Vishva Pavani Desham Mahitha Priyanka Prabhakar Peeta Basa Pati Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

This paper explores sentiment analysis in legal judgments using machine learning techniques. Six machine learning models, including Naïve Bayes, K Nearest Neighbors, Logistic Regression, Support Vector Machine, Random Forest, and Decision Tree, are used along with three data embeddings namely T5, RoBerta, and LegalBert. The study aims to evaluate the effectiveness of these models in sentiment analysis tasks tailored to legal documents. Each model is trained and evaluated based on the unique characteristics of legal texts. Hyperparameter tuning has also been performed for all the models. The focus is on achieving high accuracy, F1 score and understanding the interpretability of the models' predictions. The highest accuracy achieved was by Random Forest of 67.5%. The study aims to provide insights into the applicability of various machine learning algorithms and data embeddings for sentiment analysis in the legal domain.

关键词： Support vector machines Sentiment analysis Analytical models Machine learning algorithms Law Predictive models Data models

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Dual-Attention Protein Secondary Structure Prediction (DAPSS-Pred)

Dual-Attention Protein Secondary Structure Prediction (DAPSS...

引用

International Conference on computing and Networking Technology (ICCNT)

作者： Gundala Pallavi R Prasanna Kumar Ir Oviya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Protein secondary structure prediction is a critical task in bioinformatics, essential for understanding protein function and aiding drug discovery. Traditional methods have achieved significant milestones, but there remains a need for further accuracy and efficiency improvements. In this paper, we propose a novel approach that combines Bidirectional Long Short-Term Memory BiLSTM networks and Transformer models to enhance prediction accuracy. Our dual-model strategy leverages the sequential processing strengths of BiLSTM and the robust attention mechanisms of Transformers, enabling a more comprehensive analysis of protein sequences. Our extensive experiments demonstrate that this hybrid approach achieves superior performance, with Q3 accuracy reaching 92.03% and Q8 accuracy at 85.26%, significantly surpassing existing methods. This enhancement is ascribed to the capacity of BiLSTM to capture long-range dependencies and the Transformer’s capability to focus on relevant parts of the sequence. The integration of these models provides a powerful tool for predicting protein secondary structures with high precision. The findings suggest that our approach not only advances the current state of PSSP but also offers valuable insights that can accelerate bioinformatics research and drug discovery processes. This work underscores the potential of combining deep learning models to tackle complex biological challenges more effectively.

关键词： Proteins Accuracy Recurrent neural networks Biological system modeling Computational modeling Predictive models Transformers Drug discovery Bioinformatics Long short term memory

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Bharatanatyam Pose Perfection: Deep Learning vs. Meta-Learning & Real-Time Pose Evaluation with MediaPipe and OpenCV

Bharatanatyam Pose Perfection: Deep Learning vs. Meta-Learni...

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Ubiquitous computing and Intelligent Information Systems (ICUIS), International Conference on

作者： P Sadhana Nandhitha Ravishankar Suja Palaniswamy Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Bharatanatyam is renowned for its ability to narrate stories and express emotions through a complex interplay of facial expressions, hand gestures, and body postures. However, the sheer complexity of these dance movements poses a significant challenge in terms of capturing and evaluating them with precision. This work seeks to leverage the power of computer vision techniques, such as MediaPipe and OpenCV, as well as deep learning and meta-learning models, to create a system that can accurately detect and analyze Bharatanatyam dance poses in real-time. By providing dancers with immediate feedback on their performance, the work aims to enhance the understanding and appreciation of this rich cultural heritage, ensuring that the divine stories and emotions conveyed through Bharatanatyam are presented with the utmost clarity and authenticity for virtual learning as well. The deep learning model (CNN) achieved 95% accuracy while the meta-learning model (Siamese Network) attained 90% accuracy. These results highlight the deep learning model's higher accuracy and the meta-learning model's efficient learning process. By providing dancers with immediate performance feedback, this system enhances the understanding and appreciation of Bharatanatyam, ensuring its cultural narratives are presented with clarity and authenticity.

关键词： Metalearning Deep learning Humanities Accuracy Computational modeling Pose estimation Ubiquitous computing Real-time systems Cultural differences Tuning

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