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Leveraging Machine Learning for Next-Generation Cyber Security

Leveraging Machine Learning for Next-Generation Cyber Securi...

Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS), IEEE International Conference on

作者： V Haripriya Vishal Sharma Sukhvinder Singh Dari C.S. NIVEDHA Shweta A. Tiwaskar Akhilendra Pratap Singh Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore Karnataka India Department of Mechanical Engineering Vivekananda Global University Jaipur India Symbiosis Law School Nagpur Symbiosis International (Deemed University) Pune India Department of Master of Business Administration Prince Shri Venkateshwara Padmavathy Engineering College Chennai-127 Department of Computer Engineering Vishwakarma Institute of Information Technology Pune INDIA Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

This paper outlines how machine learning can be used to enhance subsequent-generation cyber safety. We move over how the system gaining knowledge of events may be used to locate unusual behavior or ability threats faster than conventional techniques. We further discuss how the gadget mastering era can research from beyond located styles to better guard towards future security threats. Furthermore, we discuss how device getting to know can be applied to optimize cyber safety approaches with a purpose to make sure the best possible protection for systems. Finally, we talk some ability challenges that industry players should do not forget whilst leveraging gadget gaining knowledge of for subsequentera cyber protection.

关键词： Industries Machine learning Vectors Real-time systems Security Protection Information technology

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Empowering Safety: Designing of an IoT based Women Protective System using Artificial Intelligence enabled Smart Sensors

Empowering Safety: Designing of an IoT based Women Protectiv...

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

作者： R. Sonia T Rubesh Kumar G. Naresh S. Durga Devi N Juliet Department of Computer Applications B S Abdur Rahman Crescent Institute of Science and Technology Vandalur Chennai Department of Biomedical Engineering Rajalakshmi Engineering College Chennai Department of ECE Mohan Babu University (Erstwhile SreeVidyanikethan Engineering College) Tirupati Andhra Pradesh Department of CSE Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai Department of Master of Computer Applications S.A. Engineering College Chennai

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Worldwide, women endure a great deal of unethical forms of domestic violence. The lack of an efficient tracking mechanism is speeding this up. An AI-based system that provides protection to vulnerable women was the subject of this research. In critical situations, this framework can respond appropriately automatically and manually. No place is secured for women, but they are more at risk when venturing out on their own into remote areas and unfamiliar roads. After recognizing danger, current women’s handheld safety gadgets require human involvement to activate (e.g., pushing a button or shaking the device). We provide an approach that aims to provide women with false proof safety while also attempting to address the shortcomings of current systems. This system showcases an Arduino-powered wearable safety gadget designed specifically for women. This gadget is designed to protect ladies from harm in case they encounter any risk. The gadget is able to converse and deliver alerts using a wireless sensor network. The user’s precise whereabouts can be sent to designated authorities and stored contacts through the use of GPS and GSM. In the event of an emergency, the device’s switch can be used as a panic button to get a shock; when pressed, the buzzer and laser diode will both activate. This research presents a novel approach to women’s safety, the IoT Powered Women Protection (IoTPWP), and evaluates its efficacy by cross-validating it with the usual model, the Bluetooth enabled Women Safety System (BWSS). We also believe that shows with built-in cameras that record electric shock and broadcasting real-time footage can have an effect on women’s lives through the medium of digital technology.

关键词： Wireless sensor networks Technological innovation Electric shock Pressing Switches Watches Real-time systems

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Understanding the Potential of AI-Powered Image Recognition Systems for Real-Time Applications

Understanding the Potential of AI-Powered Image Recognition ...

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International Conference on Futuristic Technologies (INCOFT)

作者： R. Kamalraj P. Gajendran S. Rathika Dharmesh Dhabliya Sunil Kumar Jakhar Savita Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore Karnataka India Department of Electrical and Electronics Engineering Karpagam College of Engineering Coimbatore Department of Master of Business Administration Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra India Department of Mechanical Engineering Vivekananda Global University Jaipur India School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

This paper investigates the potential of synthetic Intelligence (AI)–powered image popularity systems for actual-time packages. It explores how these structures can beautify actual-time operations' efficiency, accuracy, and productivity. The paper examines the accuracy, velocity, and effectiveness of image reputation technologies, which include convolutional neural networks (CNNs), switch studying, and item detection. These technologies can create fashions to detect and classify gadgets in real-time and for self-reliant selectionmaking. Additionally, the paper will speak about the security threats associated with the lengthy-time utilization of these structures. Examples of AI-powered image reputation systems for numerous actual-time programs can also be included. Sooner or later, the paper will offer an end, which considers the potential pitfalls of using AI-powered image reputation systems for actual-time packages.

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HMLM: An Intelligent Artificial Intelligence Assisted Strategy to Identify UPI Frauds Based on Hybrid Markov Learning Methodology

HMLM: An Intelligent Artificial Intelligence Assisted Strate...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： S. Bharath G. Lakshmi Vara Prasad V. Sujatha S. Hemajothi D. Sharada Mani N.R Gladiss Merlin Department of ECE Rajalakshmi Institute of Technology Chennai Department of AIMLDS QIS College of Engineering and Technology Vengamukkapalem Ongole AndhraPradesh Department of Master of Computer Applications S.A. Engineering College Chennai Department of ECE Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Department of AIMLDS QIS College of Engineering and Technology Vengamukkapalem ONGOLE Andhra Pradesh India Department of Artificial Intelligence and Data Science R.M.K. Engineering College Chennai

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The proliferation of Unified Payments Interface (UPI) transactions in the digital age has considerably improved financial inclusion, but it has also increased the vulnerability to fraud. This manuscript introduces a novel approach that employs Artificial Intelligence (AI) to detect UPI frauds. The approach is based on deep learning and is referred to as the Hybrid Markov Learning Methodology (HMLM). To evaluate the suggested system, it is cross-checked against the traditional Hidden Markov Model (HMM). Our approach combines the benefits of modern artificial intelligence methods with Markov models to improve the accuracy of fraud detection. Combining deep learning algorithms with probabilistic state transitions allows the hybrid Markov Learning Methodology to dynamically analyze transaction patterns and behaviors. By modeling both historical and real-time transaction data, our approach improves the identification of unusual activity and fraud patterns that conventional algorithms may ignore. From experimental data, we prove our approach minimizes false positives and improves the rate for fraud detection against the current options available. Our work advances the current state of fraud detection systems using more flexible and robust fraud detection systems, thus enabling security and confidence in UPI transactions.

关键词： Deep learning Accuracy Heuristic algorithms Hidden Markov models Learning (artificial intelligence) Sensitivity and specificity Probabilistic logic Real-time systems Fraud Security

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Enhancing the Efficacy of Machine Learning-Based Intrusion Detection Systems on an Imbalanced and Contemporary Dataset

Enhancing the Efficacy of Machine Learning-Based Intrusion D...

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Advanced Computing Technologies (ICoACT), International Conference on

作者： A. Kiruthikkumar N. Uma Maheswari D. David Neels Ponkumar A. Niyas Ahamed S. Nazrin Salma R. Saravanakumar Department of Master of Computer Application Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics Engineering (VLSI Technology) R.M.K. Engineering College Thiruvallur Tamil Nadu India General Engineering R.M.K. College of Engineering and Technology Thiruvallur Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

As the Internet has increased in popularity, more computers are networked and becoming part of our daily lives. Common and less evident server vulnerabilities may be used by hackers to access systems and conduct complicated assaults. An Intrusion Detection System (IDs), a popular computer security tool, uses machine learning on a previously acquired dataset. The datasets were generated in distinct networks across brief periods and may not contain the latest information. They're bloated and can't hold enough data to survive an attack. Modern intrusion detection systems are less effective with inconsistent and outdated information for rare attack paths. This paper presents five machine-learning intrusion detection solutions. K Nearest Neighbor (KNN), Decision Tree (DT), Gradient Boosting (GB), Adaboost (AB), and Random Forest (RF) are used. The latest security dataset CSE-CIC-IDS2018 replaces earlier, more often-used datasets to improve Intrusion Detection Systems (IDS). An unbalanced dataset was selected. The imbalance ratio is reduced using the Synthetic Minority Oversampling Technique (SMOTE), a synthetic data model. The approach increases attack type-specific system performance while lowering false alarms and missing incursions. Minor classes increase their data amounts to reach the average data size. Experimental results demonstrate that the recommended strategy greatly enhances uncommon intrusion detection.

关键词： Radio frequency Adaptation models Runtime Intrusion detection Nearest neighbor methods Feature extraction Boosting Decision trees Random forests Testing

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Patient health monitoring system using smart IoT devices for medical emergency services

Patient health monitoring system using smart IoT devices for...

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Sreelakshmy, R. Sruthy, R. Rajeshwari, R. Thyla, B. Suresh Kumar, M. Veltech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai Avadi600062 India NSS Polytechnic College Department of Electronics & Communication Engineering PandalamPandalamPandalam689501 India S.A. Engineering College Department of Master of Computer Applications Thiruverkadu Chennai600077 India KCG College of Technology Department of Electronics and Communication Engineering KCG College Rd Tamil Nadu Karapakkam Chennai600097 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai Thandalam602105 India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

When devices, applications, monitors, and network services are connected together, the Internet of Things (IoT) is created, which allows these organizations to collect and share data more efficiently. The aware of the status of a person via the testing of numerous metrics, as well as the inference of a positive outcome from the past of this kind of continuous supervision, distinguishes the Internet of Things inside the medical system. A difficult endeavor, the prognosis of cardiovascular disease survival is important in assisting medical practitioners in making the best judgments possible for their individuals. Patients with heart failure need the knowledge and skill of health doctors to be properly cared for. The use of machine learning techniques may aid in the comprehension of the signs of cardiovascular disease. Hand - crafted feature development, on the other hand, is difficult and necessitates the use of specialized knowledge to determine the most suited approach. This research presents a smart health monitoring structure that makes use of the Internet of Things and cloud mechanism to enhance the mortality forecast of patients with chronic heart failure with no need for human classifier, as previously done. In addition, the smart IoT-based infrastructure analyzes individuals just on based entirely information and offers cardiac rehabilitation with fast, efficient, and high-quality medical care. Additionally, the suggested model analyses whether deep learning models are effective in distinguishing between heart failure patients who are alive and those who are died. The framework makes use of Internet of Things-based sensors to collect signals and transmit them to a cloud web application for analysis. Learning algorithms are used to further analyse these data in order to identify the condition of the patients. Health information and process monitoring are communicated with a medical expert who will respond to the patient in the event that emergency assistance is

关键词： Internet of things

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Research on Majorization Algorithm and Application of Geotechnical Engineering Structure Based on Granule Swarm Majorization

Research on Majorization Algorithm and Application of Geotec...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： A. Gnana Soundari Arun Balaji N Mohammed Hussien Al-Fatlawy Suseela D Jamvant Omkar V. Asha Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India New Prince Shri Bhavani College of Engineering and Technology Tamil Nadu India College of Technical Engineering The Islamic University Najaf Iraq Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Sathyamangalam Tamil Nadu India Department of Electronics & Communication IES College Of Technology Bhopal MP India Department of Master of Computer Applications New Horizon College of Engineering Bengaluru

The traditional majorization design method makes too many restrictions on the objective function to be optimized and its constraints, which brings a lot of inconvenience to solving majorization problems in practical projects. A new stochastic Optimization algorithm based on swarm intelligence - granule swarm algorithm (PSO) was put forward in, and it has been widely concerned by researchers. When the algorithm is initialized, the granules are randomly divided into several sub-granule groups, each sub-granule group evolves independently according to a given strategy, and random migration and adaptive mutation of granules are carried out in the specified period of evolution to maintain the diversity of the entire population., to avoid premature convergence. The inverse analysis of geotechnical engineering majorization is essentially a typical majorization problem of complex nonlinear functions. The use of global majorization algorithm is an ideal way to solve this problem. low productivity. The new algorithm is applied to the elastic-plastic parameter inversion of geotechnical materials. The results show that compared with the conventional granule swarm majorization algorithm, the improved algorithm significantly improves the search efficiency of parameters, and the results that meet the accuracy requirements can be obtained with less iterations, which reduces the amount of calculation of elastic- plastic back analysis of geotechnical engineering. It is a feasible parameter inversion method.

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Data optimisation and partitioning in private cloud using dynamic clusters for agricultural datasets

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International Journal of Dynamics and Control 2020年第3期8卷 1027-1039页

作者： Leena, H.U. Premasudha, B.G. Basavaraja, P.K. B.H. road TumakuruKarnataka572103 India Department of Master of Computer Applications Siddaganga Institute of Technology B.H. road TumakuruKarnataka572103 India AICRP on STCR Department of Soil Science and Agricultural Chemistry UAS GKVK Bengaluru560065 India

The contemporary soil analytical database processing techniques lack in optimization of databases and tables on storage grids, and are limited to the single instance of database transactions in handling large volumes of soil analytical data sets. Unfortunately, these scenarios increase data processing overheads in private agricultural cloud services. In this paper, we propose a Predictive Scalability Generator (PS-Gen) technique to optimize and partition the large data sets by creating indexed databases and tables dynamically on clustered storage grids. This intelligence is conceptualized by studying the k-means clustering algorithm which is highly used in cloud database processing systems. Our approach allows the creation of database and tables dynamically within a cluster by monitoring the cluster balancer defined in the system to handle large datasets. Alternatively, the proposed approach enables quick and dynamic movement of database and tables within clusters to manage load actively by performing the row-specific tuple management. This is achieved by integrating the horizontal sharding technique to our proposed method. The evaluated experimental results exhibit the effective management of large agricultural data in private cloud systems by effective load balancing across clusters. Further, the proposed approach is flexible for adopting network subsystems and to develop an efficient cloud-based application system. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Database systems

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AI-Enhanced Predictive Control in Power Electronics

AI-Enhanced Predictive Control in Power Electronics

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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： Ramanathan G M. Senthil Kumaran H. Mickle Aancy U. Rajesh Kumar Jyotirmayi Purohit S. Kaliappan Department of Computer Science Kristu Jayanti College Bengaluru Karnataka India Department of Electrical and Electronics Engineering Sri Sivasubramaniya Nadar College of Engineering Kalavakkam Tamil Nadu India Department of Master of Business Administration Panimalar Engineering college Chennai Tamil Nadu India Department of Physics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electrical Engineering Shree Ramchandra College of Engineering Pune Maharashtra India Division of Research and Development Lovely Professional University Jalandhar Punjab India

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

Using advanced preprocessing, highlight determination, and grouping techniques, this research article explores the use of man-made brainpower (computer-based intelligence) to improve predictive control frameworks in power hardware. The preprocessing step separates basic time-recurrence characteristics and denoises power signals for improved information quality using signal handling techniques like Fourier and Wavelet Changes. Highlight determination is achieved through the use of Tether regularization, which ensures the accuracy and interpretability of the predictive model by limiting overt repetitiveness while recognizing the most fundamental boundaries. Support learning (RL) enables dynamic and adaptable dynamic in power hardware through grouping and control augmentation. By interacting with the framework environment, the RL structure learns optimal control configurations, improving continuous predictive execution in handling confusing, nonlinear features. The findings of the trials show significant improvements in energy productivity, framework strength, and non-critical failure adaption. This study demonstrates how artificial intelligence-driven theories can change predictive control in power devices, addressing fundamental issues and laying the groundwork for more robust and intelligent systems.

关键词： Productivity Accuracy Limiting Optimal control Reinforcement learning Predictive models Solids Hardware Power electronics Predictive control

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2019 International Conference on Computing, Power and Communication Technologies, GUCON 2019

作者： Chakrabarty, Sudipta Chatterjee, Sayantan Islam, Md Ruhul Kumar Deva Sarma, Hiren Techno India Department of Master of Computer Application Salt Lake Kolkata West Bengal India Sikkim Manipal Institute of Technology Majhitar Department of Computer Science Engineering Rangpo East Sikkim India Sikkim Manipal Institute of Technology Majhitar Department of Information Technology Rangpo East Sikkim India

ISBN: (纸本)9789353510985

This paper proposes one mechanism that finds the similarity pattern between two or more than two songs by finding the top sixteen fundamental frequencies of the two songs and then calculates the Coefficient of Concurrent Deviation. If Coefficient of Concurrent Deviation is near to 1, then it can be concluded as the two music structures are similar pattern at a certain level. To achieve the work, some songs of Indian Classical Music and Indian Folk Music and finding the pattern similarity among them. The basic target of this work is to find the music pattern similarity in the field of Music Information Retrieval. The work establishes that the music similarity mapping pattern through fundamental frequencies is depends on the music style. In this paper there are two types of music styles - Indian Classical Music and Indian Folk Music have been applied and recognized that the music of the same style have the probability of greater pattern similarity in Computational Musicology. © 2019 Galgotias University.

关键词： Information retrieval

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