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Towards Resilient Darknet Security: Integrating Transfer Learning and Dynamic Ensemble Techniques

Towards Resilient Darknet Security: Integrating Transfer Lea...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Biradar, Prajakta Prasad, Paarth Momidil, Midhilesh Polavarapu, Saketh Kumar, Vinod Mittal, Sonu Capita Computer Science Engineering Pune India Adgips Delhi India Adgips Dell Technologies Bangalore India SRM Institute of Science and Technology Delhi India Amity University Aiit Uttar Pradesh India

ISBN: (纸本)9798350375237

The persistent nature of darknets provides a ground for illicit activities, demanding continuous advancements. This research extends the current paradigm by introducing a comprehensive evaluation of diverse deep learning & machine learning techniques, with an emphasis on the application of transfer learning, to enhance proficiency of darknet traffic detection and the classification of underlying application types. To augment the adaptability and generalizability of the detection system, transfer learning techniques are incorporated. By training the model on one darknet dataset and evaluating its performance on others, a more generalized and adaptable detection system is aimed at identifying emerging threats across different darknet environments. Additionally, to improve the transparency and interpretability of the model, explainable artificial intelligence (XAI) techniques are integrated. Furthermore, the research introduces an innovative approach by integrating dynamic ensemble learning, where the ensemble's composition adapts in real-time based on the evolving characteristics of darknet traffic. In summary, this study advances the state-of-the-art in darknet traffic detection and introduces transfer learning as a key element in developing more generalized and adaptable detection systems. © 2024 IEEE.

关键词： Learning systems

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Towards Resilient Darknet Security: Integrating Transfer Learning and Dynamic Ensemble Techniques

Towards Resilient Darknet Security: Integrating Transfer Lea...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Prajakta Biradar Paarth Prasad Midhilesh Momidil Saketh Polavarapu Vinod Kumar Sonu Mittal Senior Software Developer Capita Computer Science Engineering Pune India ADGIPS Computer Science Engineering Delhi India Dell Technologies ADGIPS Bangalore India Computer Science Engineering SRM Institute of Science and Technology Delhi India Computer Science Engineering ADGIPS Delhi India Amity University AIIT Uttar Pradesh India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

关键词： Training Support vector machines Resistance Adaptation models Prevention and mitigation Transfer learning Real-time systems

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Integrating AI and Cybersecurity: Advancing Autonomous Vehicle Security and Response Mechanisms 4

Integrating AI and Cybersecurity: Advancing Autonomous Vehic...

引用

4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Namburi, Venkata Lakshmi Adapa, Srinivasa Rao Chamala, Shanti Swaroop Kumar Yerram, Manish Gupta, Pranjal Upreti, Kamal Software Systems Engineer Danlaw Inc MI United States Software Engineer OpenLogix LLC MI United States Software Developer Software Bots Inc MI United States Computer Science with IOT VNRVJIET Hyderabad India Computer Science Engineering ADGIPS Delhi India Dept. of Computer Science Christ University Delhi NCR Campus India

ISBN: (纸本)9798350353266

The rapid evolution of autonomous and connected vehicles has led to their integration with numerous technologies and software, rendering them vulnerable targets for cybersecurity attacks. While efforts have traditionally focused on preventing these attacks, the escalating risk underscores the importance of also vindicating their wallop. Nevertheless, this procedure is often onerous & facade scalability confronted, particularly due to connectivity issues in automobiles. This research advises a vehicle-based vibrant imposition response scheme, enabling swift responses to a variety of incidents and reducing reliance on external security centers. The classification encompasses an inclusive range of probable retorts, a procedure for evaluating retorts, & innumerable assortment approaches. Implemented on an embedded platform, the solution was evaluated using two distinct cyberattack use cases, highlighting its adaptability, responsiveness, volume for dynamic arrangement constraint alterations & nominal memory trail. Concurrently, this paper presents an innovative (AVSF) that synergistically integrates (AI) and cybersecurity techniques to fortify AV resilience against evolving threats. Additionally, the framework incorporates advanced cybersecurity measures such as encryption, authentication, and intrusion detection to mitigate vulnerabilities and safeguard critical AV systems. The fusion of AI and cybersecurity not only enhances AV security posture but also enables intelligent cyber threat monitoring and response capabilities. Extensive simulations and experimental evaluations demonstrate the efficacy of the AVSF in real-time scenarios, contributing to the development of robust security solutions for autonomous vehicle deployment and advancing safer transportation systems in the era of AI-driven mobility. © 2024 IEEE.

关键词： Autonomous vehicles

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Design and Implementation of an Automated Conveyor Belt System with Speed Control, Size Sortation, and IoT Data Publication 5th

Design and Implementation of an Automated Conveyor Belt Syst...

引用

5th International Conference on Recent Developments in Control, Automation and Power engineering, RDCAPE 2023

作者： Thakur, Aryan Gautam, Devraj Bhatia, Sandeep Kumar, Surender Department of ECE ADGIPS Shastri Park Delhi New Delhi110053 India Department of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida201310 India

ISBN: (纸本)9789819600465

In this research work, an automated conveyor belt system with a speed controller and a size sorting mechanism is designed and put into operation. The system aims to optimize the efficiency of material handling processes by dynamically adjusting the conveyor belt speed and accurately sorting items based on their dimensions. Furthermore, the system leverages the power of Internet of Things (IoT) technology by seamlessly publishing real-time data to the ThingSpeak IoT platform. The paper discusses the system's architecture, the integration of speed control and size sortation mechanisms, and the communication protocols used for data publication. Experimental results demonstrate the effectiveness of the proposed system in enhancing productivity, reducing errors, and providing valuable insights for process optimization. The research contributes to the field of automation and IoT-based solutions for industrial applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fuel storage

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Advanced Sentiment Analysis: From Lexicon-Enhanced BERT to Dimensionality Reduction Using NLP 4

Advanced Sentiment Analysis: From Lexicon-Enhanced BERT to D...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Kumar, Vinod Chauhan, Priyanka S. Lella, Jai Advitheeya Momidi, Midhilesh Gupta, Pranjal Upreti, Kamal SEAS GWU WashingtonDC United States Global Payments Atlanta United States Electrical & Comm. Engg. National Institute of Technology Calicut India Dell Technologies Bangalore India Computer Science Engineering ADGIPS Delhi India Christ University Dept. of Computer Science Delhi NCR Campus India

ISBN: (纸本)9798350353266

Social media platforms serve as vital connections for communication, generating massive quantities of data that represent an array of perspectives. Efficient sentiment analysis is necessary for understanding public opinion, particularly in domains such as product reviews and socio-political discussion. This paper develops a novel sentiment analysis model that is customized for social media data by integrating machine learning algorithms, language processing techniques with part-of-speech tagging, and dimensionality reduction methods. The model will improve sentiment analysis performance by tackling challenges like noise and data domain variations. To further improve sentiment representation, it includes convolutional neural networks (CNNs), BERT embeddings, N-grams, and sentiment lexicons. The model's effectiveness is determined on a variety of datasets, which enhances sentiment analysis in social media discussion. This paper goes beyond sentiment analysis in code-mixed, multilingual text and highlights the importance of careful data before treatment and an extensive variety of ML algorithms. This study attempts to explain the nuances of sentiment analysis and its use in social media discussions through methodical research. © 2024 IEEE.

关键词： Dimensionality reduction

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Deep Learning for Contextual Bug Detection and Automated Fixes in Software Systems

Deep Learning for Contextual Bug Detection and Automated Fix...

引用

Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： Kushal Gaddamwar Yatharth Srivastava Jyoti Parashar Ritwij Aryan Parmar Aditya Kumar Pandey Virendra Singh Kushwah Computer Science and Engineering PDPM IIIT Jabalpur Madhya Pradesh India Computer & Communication Eng. The LNM Institute of Info. Tech. Jaipur India Computer Science & Engineering ADGIPS New Delhi India Computer Science & Engineering Manipal Institute of Technology Karnataka India School of Computing Science & AI VIT Bhopal University Sehore India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

Modern software systems are becoming more and more complicated, which calls for sophisticated techniques to ensure code quality and dependability. The subtleties of complex software systems are difficult for traditional techniques to problem discovery and fixing to handle as they frequently rely on manual code reviews and static analysis tools. With a new focus on context-aware bug identification, this research explores the use of deep learning models to automate the detection and correction of problems in software code. The suggested models are made to find issues in the context of the software system by using a huge dataset of code annotated with bug information and associated remedies, together with metadata like project architecture, dependencies, and runtime circumstances. Various neural network architectures, including transformers and graph neural networks (GNNs), are explored to capture both the syntactic and semantic aspects of code while considering the contextual interactions between different code modules. This context-aware approach enhances the model’s ability to detect bugs that may arise from the interaction between components, runtime behaviors, or specific usage scenarios often missed by traditional methods. Additionally, the integration of these models into continuous integration/continuous deployment (CI/CD) pipelines is examined, enabling real-time bug detection and automated patch generation. The proposed system aims to improve code quality, reduce debugging time, and minimize human intervention, accelerating the software development lifecycle. Preliminary results demonstrate that the inclusion of contextual information significantly improves the accuracy and relevance of bug detection and fixing, paving the way for more robust and self-healing software systems.

关键词： Deep learning Codes Runtime computer bugs Pipelines Software systems Transformers Real-time systems Context modeling Software development management

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Integrating AI and Cybersecurity: Advancing Autonomous Vehicle Security and Response Mechanisms

Integrating AI and Cybersecurity: Advancing Autonomous Vehic...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Venkata Lakshmi Namburi Srinivasa Rao Adapa Shanti Swaroop Kumar Chamala Manish Yerram Pranjal Gupta Kamal Upreti Software Systems Engineer Danlaw Inc Michigan USA Software Engineer OpenLogix LLC Michigan USA Software Developer Software Bots Inc Michigan USA Computer Science with IOT VNRVJIET Hyderabad India Computer Science Engineering ADGIPS Delhi India Dept. of Computer Science Christ University Delhi NCR Campus India

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

关键词： Scalability Software Smart transportation Safety Time factors computer security Vehicle dynamics Artificial intelligence Autonomous vehicles Resilience

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Advanced Sentiment Analysis: From Lexicon-Enhanced BERT to Dimensionality Reduction Using NLP

Advanced Sentiment Analysis: From Lexicon-Enhanced BERT to D...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Vinod Kumar Priyanka S. Chauhan Jai Advitheeya Lella Midhilesh Momidi Pranjal Gupta Kamal Upreti SEAS GWU Washington DC USA Global Payments Atlanta USA Electrical & Comm. Engg. National Institute of Technology Calicut India Dell Technologies Bangalore India Computer Science Engineering ADGIPS Delhi India Dept. of Computer Science Christ University Delhi NCR Campus India

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

关键词： Dimensionality reduction Sentiment analysis Analytical models Machine learning algorithms Social networking (online) Reviews Tagging Data models Vectors Convolutional neural networks

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