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Modified AI-based learning Approach to Secure Data using Deep learning algorithms

Modified AI-based learning Approach to Secure Data using Dee...

引用

2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Nallapareddy, Venkata Sai Swaroop Reddy Sony Interactive Entertainment Department of Software Engineering Austin United States

ISBN: (纸本)9798331508432

Intelligent cyber security systems and policies of today rely on deep learning, a technology derived from artificial neural networks (ANNs). Cyber risk analytics that use artificial intelligence (AI) to boost business resilience and understand cyber security risk: pros and cons. Make use of deep learning tools like generative adversarial networks, deep belief networks, deep transfer learning, and deep reinforcement learning to intelligently combat cyber threats. There is also a set of tools that includes generic adversarial networks, auto-encoders, multilayer perceptrons, convolutional neural networks, recurrent neural networks (also known as long short-term memory), and so on. It is also possible to use these networks in ensembles or hybrid techniques. The goal of the backpropagation method is to convert inputs into outputs by making the most of the network weights. When training, a number of optimisation methods are employed, including Adam, Stochastic Gradient Descent, and Limited Memory BFGS. A wide range of cybersecurity issues could be addressed by these neural networks. Building reliable Internet of Things (IoT) systems, detecting harmful botnet traffic, analyzing malware, and conducting security threat analyses are all possible with MLP-based networks. Finding a computationally expensive solution to a complex security model may be difficult with MLP since the model is hyperparameter sensitive and needs adjustment of many different parameters, including the number of hidden layers, neurons, and iterations. © 2024 IEEE.

关键词： Adversarial machine learning

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Machine Learning-Based Adaptive Cybersecurity Framework for the Internet of Things

Machine Learning-Based Adaptive Cybersecurity Framework for ...

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2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Nallapareddy, Venkata Sai Swaroop Reddy Sony Interactive Entertainment Department of Software Engineering Austin United States

ISBN: (纸本)9798331508432

Strong security measures are required to safeguard the fast growing Internet of Things (IoT) network, which already has billions of linked devices throughout the globe. Enhanced Secure Channel Authentication utilising random forests and software-defined networking (SCAFFOLD) is a proposed adaptive security framework in this article that is designed specifically for Internet of Things (IoT) settings. The architecture uses machine learning and software-defined networking (SDN) to set up safe routes of communication between Internet of Things (IoT) nodes. Contemporary constraint-based CPS programs suffer from security and privacy issues, which the proposed method aims to address. A possible result is a simple method for verifying the identity of cyber-physical things. A new kind of security algorithm ensures that cyber-physical systems can adapt and operate autonomously without compromising user data or device security. Developed on the Raspberry platform, a new middleware module allows CPS-based devices and services to communicate more easily, which increases both flexibility and scalability. Effortless, user-centric, The two CPS units can verify each other's identities using the proposed approach. The bigger a network gets, the more vulnerable it is to attacks. Attacks on the Internet of Things network are thus much more likely to succeed than on traditional networks. The proliferation of interconnected devices has coincided with an expansion in the variety of security vulnerabilities. Modern technology is crucial for defending the Internet of Things (IoT) ecosystem against the dangers that exist today. A minimal computational overhead and high efficiency level are prerequisites for the suggested method. It safeguards wireless transmissions by generating random session keys. Many different types of cyber threats cannot reach the system. Modern constraint-based CPS systems necessitate a fresh, minimal security paradigm. © 2024 IEEE.

关键词： Cyber attacks

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AI-Enhanced Cyber Security Proactive Threat Detection and Response Systems 4

AI-Enhanced Cyber Security Proactive Threat Detection and Re...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Nallapareddy, Venkata Sai Swaroop Reddy Katta, Siva Koteswara Rao Sony Interactive Entertainment Department of Software Engineering Austin United States KIETW Department of Mechanical Engineering India

ISBN: (纸本)9798331523923

The introduction of AI-Enhanced Cyber Threat Detection, which aids in improving organizational security through sophisticated algorithms, has caused a paradigm change in cybersecurity. The proactive posture of AI-driven detection systems that autonomously sift through massive data sets to identify patterns suggestive of cyber intrusions and attacks is explored in this research study. These tools relieve human analysts of routine tasks like threat identification and incident response, freeing them to focus on more strategic matters like making decisions and developing mitigation measures. Artificial intelligence (AI) based detection also makes monitoring network activities easier in real-time, increasing operational efficiency and decreasing dwell time. Another approach to accomplishing this in cybersecurity is combining different technologies, including threat intelligence platforms, with AI or SOAR systems. Further research and improved collaboration are needed to strengthen AI-enabled cyber threat identification in the face of obstacles like adversarial attacks and ethical concerns. Given the prevalence of modern e-crimes, this paper stresses the need for joint defence strategies and proactive cybersecurity measures to safeguard digital assets. © 2025 IEEE.

关键词： Cyber attacks

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Machine Learning-Based Adaptive Cybersecurity Framework for the Internet of Things

Machine Learning-Based Adaptive Cybersecurity Framework for ...

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Intelligent Computing and Emerging Communication Technologies (ICEC), International Conference on

作者： Venkata Sai Swaroop Reddy Nallapareddy Department of Software Engineering Sony Interactive Entertainment Austin

ISBN: (数字)9798331508432

ISBN: (纸本)9798331508449

关键词： Adaptation models Electric potential Data analysis Biological system modeling Computational modeling Ecosystems Data models Internet of Things Security software defined networking

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Modified AI-based learning Approach to Secure Data using Deep learning algorithms

Modified AI-based learning Approach to Secure Data using Dee...

引用

Intelligent Computing and Emerging Communication Technologies (ICEC), International Conference on

作者： Venkata Sai Swaroop Reddy Nallapareddy Department of Software Engineering Sony Interactive Entertainment Austin

ISBN: (数字)9798331508432

ISBN: (纸本)9798331508449

关键词： Deep learning Training Computational modeling Smart contracts Transfer learning Data models Internet of Things Computer crime Biological neural networks Long short term memory

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AI-Enhanced Cyber Security Proactive Threat Detection and Response Systems

AI-Enhanced Cyber Security Proactive Threat Detection and Re...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Venkata Sai Swaroop Reddy Nallapareddy Siva Koteswara Rao Katta Department of Software Engineering Sony Interactive Entertainment Austin Department of Mechanical Engineering KIETW India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

关键词： Explainable AI Organizations Telecommunication traffic Threat assessment Real-time systems Time factors Artificial intelligence Computer security Stress Stress measurement

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The present and future aspects of Digital Nudging and Digital Persuasion 18

The present and future aspects of Digital Nudging and Digita...

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18th International Conference on Persuasive Technology, Adjunct, PERSUASIVE-ADJ 2023

作者： Haque, Md Sanaul Vainionpää, Fanny Tripathi, Ashok Caraban, Ana Department of Software Engineering LUT University Finland INTERACT Research Unit University of Oulu Finland Instituto Superior Técnico + Interactive Technology Institute Portugal

This paper describes the current and future aspects of Digital Nudging and Digital Persuasion (DNDP). To find this, a case study approach has been followed, such as, we organized a workshop event aimed at supporting citizens and stakeholders in applying digital nudging and persuasion strategies to increase individuals' competency and self-efficacy beliefs for goal attainment. The workshop was structured with a total of three research papers presentation, a keynote talk, and an open discussion. Over 30 participants in a hybrid mode attended the event and engaged in solving different nudge and persuasion-based tasks via technology-mediated solutions. The results have shown that there needs work to enhance the capacity of using digital nudging and persuasion techniques to achieve a more sustainable behavioral change in individuals for a healthy digitalized community with a view to transparency from policy to practice. © 2023 Copyright for this paper by its authors.

关键词： gamification Nudging persuasion persuasive design

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The 2nd International Workshop on Digital Nudging and Digital Persuasion (DNDP 2023) 18

The 2nd International Workshop on Digital Nudging and Digita...

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18th International Conference on Persuasive Technology, Adjunct, PERSUASIVE-ADJ 2023

作者： Caraban, Ana Vainionpää, Fanny Tripath, Ashok Haque, Md Sanaul Department of Software Engineering LUT University Finland INTERACT Research Unit University of Oulu Finland Instituto Superior Técnico + Interactive Technology Institute Portugal

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HAG: Hierarchical Attention with Graph Network for Dialogue Act Classification in Conversation 48

HAG: Hierarchical Attention with Graph Network for Dialogue ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Fu, Changzeng Chen, Zhenghan Shi, Jiaqi Wu, Bowen Liu, Chaoran Ishi, Carlos Toshinori Ishiguro, Hiroshi Northeastern University Sstc China Osaka University Graduate School of Engineering Science Japan Riken Interactive Robot Research Team Guardian Robot Project Japan Peking University School of Software and Microelectronics China

ISBN: (纸本)9781728163277

The prediction of dialogue acts (DA) labels on utterance-level in conversations can be treated as a sequence labeling problem, which requires context- and speaker-aware semantic comprehension, especially for Japanese. In this study, we pro-posed a hierarchical attention with the graph neural network (HAG) to consider the contextual interconnections as well as the semantics carried by the sentence itself. Concretely, the model use long-short term memory networks (LSTMs) to perform a context-aware encoding within a dialogue window. Then, we construct the context graph by aggregating the neighboring utterances. Subsequently, a speaker feature transformation is executed with a graph attention network (GAT) to calculate the interconnections, while a context-level feature selection is performed with a gated graph convolutional network (GatedGCN) to select the salient utterances that contribute to the DA classification. Finally, we merge the representations of different levels and conduct a classification with two dense layers. We evaluate the proposed model on Japanese dialogue act dataset (JPS-DA). The experimental results show that our method outperforms the baselines. © 2023 IEEE.

关键词： Graph neural networks

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A Comparative Analysis of Multi-agent Simulation Platforms for Energy and Mobility Management 20th

A Comparative Analysis of Multi-agent Simulation Platforms ...

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Proceedings of the 20th European Conference on Multi-Agent Systems, EUMAS 2023

作者： Ali, Aliyu Tanko Leucker, Martin Schuldei, Andreas Stellbrink, Leonard Sachenbacher, Martin Institute for Software Engineering and Programming Languages University of Lübeck Ratzeburger Allee 160 Lübeck Germany Institute for Multimedia and Interactive Systems University of Lübeck Ratzeburger Allee 160 Lübeck Germany

ISBN: (纸本)9783031432637

Effective energy and mobility management benefits from multi-agent simulations (MAS) to model complex interactions among various agent types. Selecting the optimal MAS platform to implement and simulate these interactions is vital for achieving accurate results, scalability to realistic problem sizes, and efficient computational performance. This paper investigates the energy and mobility domain and identifies key parameters such as the number and complexity of agents, parallel computing power, CPU requirements etc., for developing MAS in the context of this domain. It then presents a comprehensive evaluation of various MAS development platforms. Using a multi-level selection and elimination approach, we narrowed down our evaluation to two final candidates. We then implemented key aspects of our model in both platforms to compare them in terms of practical relevance. Our findings reveal that the *** platform outperforms the Mesa platform in terms of runtime performance, has a smaller memory footprint for large numbers of agents, and offers scalability, making it the most suitable choice for developing MAS for integrated energy and mobility models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Scalability

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