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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Madala, Ravikiran Amardeep, Rashmi Sowmya, S.R. Supriya, R.K. Nikshepa, T. Surabhi, K.R. University of Cumberlands Network and Data Center Engineer Ph.D Information Technology DurhamNC United States Dayananda Sagar Academy of Technology and Management Department of Information Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350304480

Cyber security tactics must be more potent and cunning due to the complexity of cybercrimes, which is always expanding. This will enable guard components to make real-time decisions that successfully counter sophisticated threats. The two scientists and professionals should be familiar with current methods for ensuring cyber security in order to aid in this. Implementing automated and expert cycles for recognizing cyber threats is one of the key aspects of cyber security. In light of artificial brain association, we describe an artificial intelligence (AI) cyber risk detection method in this analysis. They gave arrangement utilizes progressed learning-based detection procedures to improve cyber danger distinguishing proof and completely changes countless gathered security occasions into individual occasion profiles. The innovation centers around recognizing valid and bogus positive alerts with the goal those security reviewers can rapidly answer cyber attacks. The exploratory consequences of this study affirm the reasonableness of our proposed approach as a learning-based model for network shortcoming detection, and in any event, when this show is applied to real-world applications;it isn't equivalent to regular AI. It shows that it is better than the technique. © 2023 IEEE.

关键词： AI Artificial Intelligence Automated Cyber Attack Detection Real-Time Security System

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Real-Time Cyber Attack Detection Based on Automated Ai Security System

Real-Time Cyber Attack Detection Based on Automated Ai Secur...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Ravikiran Madala Rashmi Amardeep Sowmya. S. R Supriya R. K Nikshepa. T K. R. Surabhi Network and Data Center Engineer Ph.D Information Technology University of Cumberlands Durham North Carolina United States Department of Information Science and Engineering Dayananda Sagar Academy of Technology and Management Bengaluru Karnataka India

Cyber security tactics must be more potent and cunning due to the complexity of cybercrimes, which is always expanding. This will enable guard components to make real-time decisions that successfully counter sophisticated threats. The two scientists and professionals should be familiar with current methods for ensuring cyber security in order to aid in this. Implementing automated and expert cycles for recognizing cyber threats is one of the key aspects of cyber security. In light of artificial brain association, we describe an artificial intelligence (AI) cyber risk detection method in this analysis. They gave arrangement utilizes progressed learning-based detection procedures to improve cyber danger distinguishing proof and completely changes countless gathered security occasions into individual occasion profiles. The innovation centers around recognizing valid and bogus positive alerts with the goal those security reviewers can rapidly answer cyber attacks. The exploratory consequences of this study affirm the reasonableness of our proposed approach as a learning-based model for network shortcoming detection, and in any event, when this show is applied to real-world applications; it isn't equivalent to regular AI. It shows that it is better than the technique.

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Cognitive Defense Cyber Attack Prediction and Security Design in Machine Learning Model 6

Cognitive Defense Cyber Attack Prediction and Security Desig...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Devadarshini, P.T. Chandrashekar, B. Pundir, Sumit Tiwari, Mohit Madala, Ravikiran Indhuma, E. Easwari Engineering College Department of Artificial Intelligence and Data Science TamilNadu Chennai India St. Martin's Engineering College Department of Computer Science and Engineering Kompalli Telangana Secunderabad India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttrakhand Dehradun India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India Network and Data Center Engineer Ph.D Information Technology University of Cumberlands DurhamNC United States St.Martln's Engineering College Department of Computer Science and Engineering Telangana Secunderabad India

ISBN: (纸本)9798350304480

Companies of all sizes in today's linked digital world are susceptible to cyber dangers. In this study, we provide a novel approach to cyber defence that integrates machine learning with cognitive defensive techniques to identify and prevent cyber attacks in their tracks. We built three ML models that use cognitive defensive strategies and other cutting-edge security measures like encryption and intrusion detection. A 95% accuracy rate was reached by our models, which is far greater than where things stand right now models. Our work has the potential to have a huge effect since the models we've developed may one day help businesses take a more preventative stance against cyber threats. Our findings may potentially be used to fields outside than cyber security. Overall, our study shows that machine learning and cognitive defense are useful for foreseeing and stopping cyber threats, and points the way toward fruitful future study in this area. © 2023 IEEE.

关键词： Accuracy Advanced security features Cognitive defense Cyber security Cyber-attacks Encryption Intrusion detection Machine learning Prediction Prevention Proactive approach Threat landscape

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Automated AI Research on Cyber Attack Prediction and Security Design 6

Automated AI Research on Cyber Attack Prediction and Securit...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Madala, Ravikiran Vijayakumar, N. Nandini, N. Verma, Shanti Chandvekar, Samidha Devendra Singh, Devesh Pratap Network and Data Center Engineer Ph.D Information Technology University of Cumberlands DurhamNC United States Karpagam College of Engineering Department of Computer Science and Technology Coimbatore India Dr. Ambedkar Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru India L J University Department of Computer Application Gujarat Ahmedabad India Department of Information Technology Maharashtra New Panvel India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttrakhand Dehradun India

ISBN: (纸本)9798350304480

A fast expanding topic of study on automated AI is focused on the prediction and prevention of cyber-attacks using machine learning algorithms. In this study, we examined the research on applying machine learning algorithms to the problems of strategic cyber defense and attack forecasting. We also provided a technique for assessing and choosing the best machine learning models for anticipating cyber-attacks. Our findings show that machine learning methods, especially random forest and neural network models, are very accurate in predicting cyber-attacks. Additionally, we discovered a number of crucial characteristics, such as source IP, packet size, and malicious traffic that are strongly associated with the likelihood of cyber-attacks. Our results imply that automated AI research on cyber-attack prediction and security planning has tremendous promise for enhancing cyber-security and averting cyber-attacks. © 2023 IEEE.

关键词： automated AI research cyber-attack prediction feature correlation machine learning neural network random forest security design

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Research and Innovation in Next Generation Security and Privacy in Industry 5.0 Iot 6

Research and Innovation in Next Generation Security and Priv...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Navale, Geeta S. Madala, Ravikiran Managuli, Manjunath Jayalakshmi, N. Kadiravan, G. Rawat, Ruchira Sinhgad Institute of Technology and Science Department of Computer Engineering Maharashtra Pune India Ph.D Information Technology Network and Data Center Engineer University of Cumberlands DurhamNC United States KLS Gogte Institute of Technology Department of Electronics and Communication Engineering Karnataka Belagavi India PSNA College of Engineering and Technology Master of Computer Applications Tamilnadu Dindigul India KL Deemed to Be University Department of Computer Science and Information Technology Andhra Pradsh India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttrakhand Dehradun India

ISBN: (纸本)9798350304480

The objective of the newest manufacturing paradigm, known as 'Industry 5.0,'is to combine human intelligence with cutting-edge technology like the Internet of Things (IoT), artificial intelligence (AI), and robots to develop production systems that are incredibly effective and adaptable. But as we rely more on these technologies, we face additional privacy and security issues that might affect how dependable and trustworthy these systems are. In this report, we examine the most recent advancements in security and privacy for Industry 5.0 IoT and pinpoint the newest trends, obstacles, and possibilities. We also cover how these changes may affect manufacturing in the future and provide advice to scholars, practitioners, and policymakers. © 2023 IEEE.

关键词： artificial intelligence cyber-physical systems human-machine collaboration Industry 5.0 Internet of Things IoT manufacturing privacy robotics security

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Research and Innovation to Market Development: Artificial Intelligence in Business

Research and Innovation to Market Development: Artificial In...

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International Conference on Science Technology engineering and Management (ICONSTEM)

作者： Nikitha Yathiraju P. Raman Ravikiran Madala Prashant Surgonda Patil Ashok Kumar S. Ashwin Ph. D in Information Technology University of Cumberlands Kentucky USA Department of Management Studies Panimalar Engineering College Poonamallee Chennai TamilNadu India Network and Data Center Engineer Ph.D Information Technology University of Cumberlands Durham North Carolina United States Department of Management Bharati Vidyapeeths IMRDA Sangli Sangli Maharashtra India Department of Mechanical Engineering New Horizon College of Engineering Bangalore Karnataka India

Showcasing has become complicated over the most recent multi decade with the presentation of Artificial Intelligence. Artificial Intelligence Promoting is a strategy of ideally using technology to upgrade client's insight. Nowadays, it has become really important for firms to recognise and understand the needs of their customers. Machine intelligence Nd their assumptions regarding items as well as administrations. The deductions acquired from the exploration will give a better comprehension of the developments and the effect of computer-based intelligence on businesses and society overall. It will likewise give a superior comprehension of how simulated intelligence can change the business tasks and hence the worldwide economy. We have dissected the information utilizing different recurrence table and diagrams and one way ANOVA strategy. Four distinct business boundaries are seen as necessary for the examination in order to understand how computer-based intelligence affects business. Artificial intelligence will assist the businesses with preparing themselves for confronting the difficulties because of the fast mechanical progressions in human existence and business. Man-made intelligence has been demonstrated to affect all the business activities decidedly, as it upgrades supportability and market administration.

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Cognitive Defense Cyber Attack Prediction and Security Design in Machine Learning Model

Cognitive Defense Cyber Attack Prediction and Security Desig...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： P.T. Devadarshini B Chandrashekar Sumit Pundir Mohit Tiwari Ravikiran Madala E. Indhuma Department of Artificial Intelligence and Data Science Easwari Engineering College Chennai TamilNadu India Department Of Computer Science and Engineering St. Martin's Engineering College Kompalli Secunderabad Telangana Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttrakhand India Department of Computer Science and Engineering Bharati Vidyapeeth's College of Engineering Delhi Network and Data Center Engineer Ph.D Information Technology University of Cumberlands Durham North Carolina US Department of Computer Science and Engineering St.Martln's Engineering College Secunderabad Telangana India

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Research and Innovation In Next Generation Security and Privacy In Industry 5.0 Iot

Research and Innovation In Next Generation Security and Priv...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Geeta S. Navale Ravikiran Madala Manjunath Managuli N. Jayalakshmi G. Kadiravan Ruchira Rawat Department of Computer Engineering Sinhgad Institute of Technology and Science Pune Maharashtra India Network and Data Center Engineer Ph.D Information Technology University of Cumberlands Durham North Carolina United States Department of Electronics and Communication Engineering KLS Gogte Institute of Technology Belegavi Karnataka India Master of Computer Applications PSNA College of Engineering and Technology Dindigul Tamilnadu India Department of Computer Science and Information Technology KL Deemed to be University Andhra Pradsh India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttrakhand India

The objective of the newest manufacturing paradigm, known as “Industry 5.0,” is to combine human intelligence with cutting-edge technology like the Internet of Things (IoT), artificial intelligence (AI), and robots to develop production systems that are incredibly effective and adaptable. But as we rely more on these technologies, we face additional privacy and security issues that might affect how dependable and trustworthy these systems are. In this report, we examine the most recent advancements in security and privacy for Industry 5.0 IoT and pinpoint the newest trends, obstacles, and possibilities. We also cover how these changes may affect manufacturing in the future and provide advice to scholars, practitioners, and policymakers.

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Automated AI Research On Cyber Attack Prediction And Security Design

Automated AI Research On Cyber Attack Prediction And Securit...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Ravikiran Madala N. Vijayakumar Nandini N Shanti Verma Samidha Devendra Chandvekar Devesh Pratap Singh Network and Data Center Engineer Ph.D Information Technology University of Cumberlands Durham North Carolina United States Department of Computer Science and Technology Karpagam College of Engineering Coimbatore India Department of Computer Science and Engineering Dr. Ambedkar Institute of Technology Bengaluru Karnataka India Department of Computer Application L J University Ahmedabad Gujarat India Department of Information Technology Changu Kana Thakur Arts Commerce and Science College New Panvel (Autonomous) New Panvel Maharashtra India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttrakhand India

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Finding shortest and nearly shortest path nodes in large substantially incomplete networks

arXiv

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arXiv 2022年

作者： Kitsak, Maksim Ganin, Alexander Elmokashfi, Ahmed Cui, Hongzhu Eisenberg, Daniel A. Alderson, David L. Korkin, Dmitry Linkov, Igor Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Network Science Institute Northeastern University BostonMA02115 United States Department of Systems and Information Engineering University of Virginia CharlottesvilleVA22904 United States U.S. Army Engineer Research and Development Center Contractor Concord MA01742 United States Simula Metropolitan Center for Digital Engineering Oslo Norway Bioinformatics and Computational Biology Program Worcester Polytechnic Institute WorcesterMA01609 United States Institute for Genomic Medicine Columbia University Medical Center New YorkNY United States Operations Research Department Naval Postgraduate School MontereyCA93943 United States Data Science Program Worcester Polytechnic Institute WorcesterMA01609 United States Computer Science Department Worcester Polytechnic Institute WorcesterMA01609 United States U.S. Army Engineer Research and Development Center Environmental Laboratory Concord MA01742 United States

Dynamic processes on networks, be it information transfer in the Internet, contagious spreading in a social network, or neural signaling, take place along shortest or nearly shortest paths. Unfortunately, our maps of most large networks are substantially incomplete due to either the highly dynamic nature of networks, or high cost of network measurements, or both, rendering traditional path finding methods inefficient. We find that shortest paths in large real networks, such as the network of protein-protein interactions (PPI) and the Internet at the autonomous system (AS) level, are not random but are organized according to latent-geometric rules. If nodes of these networks are mapped to points in latent hyperbolic spaces, shortest paths in them align along geodesic curves connecting endpoint nodes. We find that this alignment is sufficiently strong to allow for the identification of shortest path nodes even in the case of substantially incomplete networks. We demonstrate the utility of latent-geometric path finding in problems of cellular pathway reconstruction and communication security. Copyright © 2022, The Authors. All rights reserved.

关键词： Proteins

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