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Secured control systems through integrated IoT devices and control systems

Measurement: Sensors 2022年 24卷

作者： Rajendran, N. Singh, Rupinder Moudgil, Manu Raj Turukmane, Anil V. Umadevi, M. Glory, K.B. Department of Information Technology B.S.Abdur Rahman Crescent Institute of Science and Technology Chennai India Department of Computer Applications Chitkara University Rajpura140417 India Department of CSE Bhai Gurdas Institute of Engineering and Technology Punjab Sangrur148001 India Department of Computer Science & Engineering VIT- AP University Vijaywada Amaravati India Department of Computer Science Sri Adi Chunchanagiri Women's College Tamilnadu Cumbum India Department of Engineering English Koneru Lakshmaiah Education Foundation Andhra Pradesh State Vaddeswaram522302 India

The Internet of Things (IoT) provides customers with seamless integration and interoperability between gadgets, technologies, or products, and across multiple networks focus on control schemas. Regardless of the integration node, whether it is a smartphone, tablet, laptop, TV, automobile, or other Internet-compatible gadget, end-users want a fast and clear connection. Intelligent IoT devices can act as flexible connectors with variable structures, allowing rapid responses and possibly universal access. Consequently, gadgets could pose serious security issues. When they are used to manage accounts receivable at IoT terminals, these methods are likely to replay, provoke human attacks in the middle, or by denial of service. Responding to the real-time challenge for safe control operations, this study provides a methodology that blends the characteristics of intelligent IoT systems with power system interfaces. A proposed method combines computation, cryptography, signal/image processing, and communication networks of authentication and authorization tasks, using both terminals and gateways. Unlike conventional methods, they provide a safer, more adaptable, and resilient setting for real-time performance. The researchers also describe Intel's IoT framework for power system inputs that are used in management, industrial, transport, and detailed monitoring contexts. © 2022 The Authors

关键词： Control systems

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Detecting and Preventing Brute Force Attacks on Mobile Devices Using Advanced Encryption Techniques

Detecting and Preventing Brute Force Attacks on Mobile Devic...

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Automation and Computation (AUTOCOM), International Conference on

作者： Ramya G Franklin Savinder Kaur Binita Kumari Mohit Gupta Gopalakrishna K Trapty Agarwal Maharishi Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Applications Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Engineering and Technology Electronics and Communication Engineering JAIN (Deemed to be University) Bangalore Karnataka India School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Brute pressure attacks are a not unusual approach utilized by malicious actors to gain unauthorized rights of entry to touchy records on mobile devices. Those assaults involve trying out multiple combinations of usernames and passwords until the precise one is located. This poses a huge risk to mobile security, as those devices often incorporate a tremendous quantity of personal and private information. The proposed solution is to detect and save brute force attacks on cellular gadgets by enforcing superior encryption techniques. Those techniques contain complex algorithms and methods to protect touchy records from being accessed by way of unauthorized events. This may make it easier for attackers to decipher the encrypted documents, even though they're capable of getting entry to them. The device will constantly display the get entry to attempts and locate any unusual styles or suspicious pastimes. If a brute pressure assault is detected, the device will robotically block further entry attempts and cause an alert to the consumer. It'll additionally encrypt the information in the tool to save it from being compromised. This answer will appreciably increase the security of cell gadgets because it will make it almost impossible for hackers to benefit from unauthorized admission to sensitive data. It's going to supply user's peace of thoughts, understanding that their statistics are covered, and offer an extra layer of security to cell gadgets.

关键词： Privacy Protocols Passwords Probability Robot sensing systems Mobile handsets Brute force attacks Mobile security Encryption Protection

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Augmented 3D ResUNETR: Combining residual learning and transformer encoding with multi-scale feature extraction for 3D medical image segmentation

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Biomedical Signal Processing and Control 2025年 110卷

作者： Anant Kumar Jayswal Sunil Pratap Singh Sneha Mishra Prashant Vats Kamaljit Kaur Anil Kumar Dubey Department of Computer Science and Engineering Amity University Noida UP 201301 India Department of Computer Science and Applications Bharati Vidyapeeth's Institute of Computer Applications and Management New Delhi 110063 India. Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad UP 201015 India Department of Computer Science & Engineering Manipal University Jaipur Jaipur Rajasthan 303007 India Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar Punjab 143005 India Department of Computer Science and Engineering ABES Engineering College Ghaziabad Uttar Pradesh 201009 India

Accurate 3D medical image segmentation is critical for applications such as tumor identification and organ localization. However, widely used models like U-Net and V-Net face significant limitations, including their inability to effectively extract multi-scale features in volumetric data, poor boundary delineation due to weak contextual learning, and susceptibility to overfitting when trained on a limited dataset. In order to overcome these existing issues, a novel augmented 3D ResUNETR model that integrates data augmentation, 3D residual learning (ResU-Net), with a transformer approach captured both local and global contextual features for 3D medical image segmentation. The method incorporates dilated convolutions for enhanced multi-scale feature extraction without resolution loss and introduces pyramid max pooling layers to further improve hierarchical information capture. To mitigate overfitting and increase data diversity, a generative adversarial network (GAN) model is employed for data augmentation, while advanced pre-processing filters have been applied to enhance image quality. Evaluations on four benchmark datasets, such as AMOS, KiTS23, BTCV, and Brats 2020, demonstrate superior segmentation performances. The proposed model attained dice scores of 93.5 %, 94.5 %, 96.2 %, and 92.15 %, respectively, for the four datasets. These results highlight the model’s effectiveness in robust 3D segmentation tasks with potential implications for improving computer-aided diagnosis systems in clinical practice.

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Comprehensive analysis on lightweight cryptographic algorithms for low resource devices

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Test Engineering and Management 2019年第11-12期81卷 3747-3760页

作者： Girija, M. Manickam, P. Ramaswami, M. Department of Computer Science American College Madurai India Department of Computer Science Thiagarajar College Madurai India Department of Computer Applications Madurai Kamaraj University Madurai India

Lightweight cryptography is a prominent research area in network security. It provides high security and is ideal for resource constrained based smart devices in smart environment. Smart gadgets such as RFID, Sensor Networks, and embedded systems form the smart environment and it helps in sharing information among objects at anytime, anywhere. Contribution of lightweight security algorithms and schemes in smart objects are great due to their properties such as computational power, memory size, etc. This paper presents the merits and demerits of lightweight ciphers. This paper also presents complete information about existing lightweight cryptography algorithms and the structure of lightweight cryptography. © 2019 Mattingley Publishing. All rights reserved.

关键词： Sensor networks

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Anomaly Detection in IoT Data Streams Based on Long Short-Term Memory

Anomaly Detection in IoT Data Streams Based on Long Short-Te...

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Intelligent Algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Layth Hussein Mohan M P. Rajeswari D. Gurupandi N. Naga Saranya Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Dept of ISE Nitte Meenakshi Institute of Technology Bangalore India Department of ECE Karpagam Institute of Technology Coimbatore India Department of ECE Panimalar Engineering College Chennai India Department of Computer Applications Saveetha College of Liberal Arts & Science SIMATS Chennai India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

Nowadays, the swift growth of Ambient Intelligence (AmI) in Internet of Things (IoT), sensing devices have been generating many data streams in intelligent scenarios. The IoT deployment issues, complexity of systems is leading to abnormal behavior in data streams. Moreover, the dynamic and consecutive data streams generated in IoT systems leads to difficulty in determining anomaly detection. Therefore, this paper presents a Long Short-Term Memory (LSTM) and Sparse Autoencoder method for classification of data to determine anomaly behavior in IoT data streams. Firstly, a min-max normalization technique is employed to scale the input data for better results and Sparse Autoencoder utilizes sparsity penalty to capture relevant features. The LSTM network is employed for prediction tasks which leverages the ability to handle sequential IoT data streams and dropout layers are utilized to prevent overfitting. Finally, K-means clustering mechanism is used to establish an error threshold for anomaly detection. The performance of proposed LSTM and Sparse Autoencoder is evaluated on SKAB dataset which illustrated higher precision (0.824), recall (0.982) and f1 scores (0.847). The proposed method outperformed existing anomaly detection methods such as Neural Network (NN), Adaptable and Interpretable Framework for Anomaly Detection (AID).

关键词： Deep learning Heuristic algorithms Noise Sensors Decoding Internet of Things Streams Long short term memory Anomaly detection Computational intelligence

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QoE-Based Resource Allocation in Wireless Networks: An Analysis

QoE-Based Resource Allocation in Wireless Networks: An Analy...

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Automation and Computation (AUTOCOM), International Conference on

作者： Trapty Agarwal Madhur Grover Madhusmita Sahu Varun Ojha Sunil. MP M. Saravanan Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Applications Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Engineering and Technology Electronics and Communication Engineering JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Wireless networks are developing quickly and can avoid providing an increasing number of services such as video streaming, online games, or real-time communication. However, the quality of experience when accessing content over these networks is still at stake; end-users are concerned with constraints like network congestion, channel quality, and resources provided. In the literature, several proposals, such as QoE-aware resource allocation strategies, have been suggested to tackle this issue. This technical abstract considers QoE-driven approaches for resource allocation in wireless networks. These approaches are employed to facilitate users' perceived QoE by selectively provisioning the users with adequate network resources according to their QoS demands. For example, the analysis takes a look at some of these strategies as well as the main factors that inform them when designing for user preferences, network conditions, and resource availability. We then discuss the benefits and limitations of other approaches, such as Fuzzy logic-based methods and game theory-based methods.

关键词： Reviews Wireless networks Quality of service Games Streaming media Real-time systems Resource management Quality of experience Proposals Standards

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AI-Enhanced Cybersecurity for Intelligent Systems (IS) Towards Criminals

AI-Enhanced Cybersecurity for Intelligent Systems (IS) Towar...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Kumaran U S.Padmanaban Rakesh Kumar Ramy Riad Al-Fatlawy Manpreet Kaur Sukanya Konatam Department of CSE Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Science and Humanities Karpagam Institute of Technology Coimbatore Department of Computer Engineering and Applications GLA University Mathura India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India The University of Texas Austin Texas USA

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

One of the strongest examples for this scenario is artificial intelligence (AI) in which AI interacts between individuals and their environment. Artificial intelligence powering smart homes is also still young, but it has huge promise in the eyes of experts. In this article, we will be looking at a very straightforward yet an elegant AI-powered home automation solution. The focus of this research is to develop the AI-based home lighting system that appears to operate just like normal; when you're still out in your cars at night. We used Arduino hardware for the physical building of AI based smart house and coded its software to make it real-world compatible. Over 5,500 over result vectors and nine inputs were subseqentially used to establish a unique dataset that mimic actual evening light patterns in the real world as well as presented an appearance of presence within home. Ta. In plain English: AI can, yes indeed understand and control your home night light. Comparison of Performance: The proposed system is evaluated against common machine learning classification techniques like multi perceptually layered neurons, the Gaussian naive Bayes method, linear discriminant analysis methods and vector machines. This is an encouraging realization that AI can indeed make the home safer and bolster defenses against break-ins..

关键词： Prevention and mitigation Neurons Lighting Smart homes Machine learning Vectors Software Telecommunication computing Naive Bayes methods Artificial intelligence

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A Routing Optimization in Wireless Sensor Networks Based on Reverse Elite Sparrow Search Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第4期6卷 763-775页

作者： Ning, Chun-Yuan Liao, Lyu-Chao Nguyen, Trong-The Huang, Kuan-Chun Pan, Jeng-Shyang Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China Multimedia Communications Lab University of Information Technology VNU-HCM Viet Nam Department of Industrial and Systems Engineering Chung Yuan Christian University Taoyuan320314 Taiwan College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

This study suggests a solution to the routing optimization insufficiency based on the reverse elite sparrow search algorithm (RESSA) for energy balancing and network lifetime prolonging in wireless sensor networks (WSN). The node’s consumption and the remaining energy priority to balance consumption in the data forwarding to the cluster head are considered the optimal path evaluation function. The obtained results of the experimental the suggested scheme are compared with the other methods show that the proposed scheme increases data forwarding round numbers and consumes less energy, the remaining energy more, and higher efficiency of node energy consumption. © The Authors.

关键词： Energy utilization

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A Survey on Hardware Prefetching in Shared-Memory Multiprocessors

A Survey on Hardware Prefetching in Shared-Memory Multiproce...

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East-West Design & Test Symposium (EWDTS)

作者： Shuaibu Musa Adam Usman Ibrahim Musa Kamran Siddique Jie Zhang Jingchen Wang Danny Hughes Ka Lok Man imec-DistriNet KU Leuven Belgium School of Computer Applications Lovely Professional University Punjab India Department of Computer Science and Engineering University of Alaska Anchorage Anchorage AK USA School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou China Kazimieras Simonavičius University Lithuania Faculty of Informatics Vytautas Magnus University Lithuania

Memory latency presents a significant obstruction to computer performance. To hide the memory latency, prefetching mechanism is used to retrieve data from the memory before the processor requests it, anticipating near-term data demands. An effective prefetching scheme can reduce cache miss rates and hide memory latency. Prefetching has yielded major advances in both industry and academia; modern high-performance processors nearly universally implement hardware prefetchers. In this paper, we introduce the fundamental concepts underpinning hardware prefetching and survey state-of-the-art hardware prefetching schemes. We delineate common prefetching approaches, analyse their relative merits and limitations, identify key challenges and open research questions. Overall, this paper aims to provide a comprehensive overview of hardware prefetching that highlights critical trends and technologies in this crucial performance optimisation domain.

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Data Optimization in Cloud-IOT Environment with Enhanced SVM Technique 4

Data Optimization in Cloud-IOT Environment with Enhanced SVM...

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

作者： Pal, Souvik Jhanjhi, N.Z. Akila, D. Masud, Mehedi Muzzaffar, Saira Kumar, Gaurav Verma, Rajan Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical & Technical Sciences Dept. of Management IS Chennai602105 India Sister Nivedita University Department of CS and Engineering Kolkata700156 India School of CS SCS Taylor's University Subang Jaya47500 Malaysia Saveetha College of Liberal Arts & Sciences SIMATS Deemed University Dept. of Computer Applications Chennai602105 India Taif University Department of Computer Science Saudi Arabia College of CS & IT King Faisal University Saudi Arabia Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR institute of technology and sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

The number of people using smart devices is growing exponentially, and this is resulting in a rise in the volume of data being generated from different smart devices. This data is different from big data in terms of all the key classification approaches. The massive amount of data that several connected devices generate causes major performance problems for an Internet of Things system. Edge-cloud computing and networking function virtualization (NFV) approaches are two potential ways to improve resource usage and the flexibility of responsive services in an IoT system. A data optimization technique in a cloud environment is proposed in this paper and also to access the data with state-of -art methodologies. The proposed method in evaluated by terms of performance ratios, reliability percentage, coverage ratios, and sensing error. And the proposed model outperforms the state-of art method with mean performance ratio of 79.2 © 2024 IEEE.

关键词： Cloud platforms

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