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IoT Security Detection and Evaluation for Smart Cyber Infrastructures Using LSTMs with Attention Mechanism 3

IoT Security Detection and Evaluation for Smart Cyber Infras...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Pillai, Sanjaikanth E Vadakkethil Somanathan Polimetla, Kiran Prakash, Chidurala Sai Pareek, Piyush Kumar Pawar, Priyanka Pramod School Of Electrical Engineering And Computer Science University Of North Dakota Grand Forks United States Adobe Inc. Dept. Of Information Technology AustinTX United States Sr University Department Of Electrical And Electronics Engineering Warangal India Nitte Meenakshi Institute Of Technology Department Of Aiml And Ipr Cell Bengaluru India University Of The Cumberlands Department Of Information Technology WilliamsburgKY United States

ISBN: (纸本)9798350318609

A computer network may be impacted by malware, computer viruses, and other hostile attacks. One of the most important elements of network security as an active defence technology is Intrusion Detection System (IDS). Issues with traditional intrusion detection systems include low detection effectiveness, inaccurate results, and an inability to handle new types of intrusions. This paper implemented a deep LSTMs with attention mechanism approach for IDS. The proposed technique is determined on Bot-IoT dataset, and z-score normalization is performed to normalize data. Information Gain (IG) is utilized to choose the features. A deep LSTMs with attention mechanism is used to effectively identify and categorize IDS effectively. The IG-deep LSTMs with attention mechanism achieves better accuracy using of 99.70% and 99.60% in NSL-KDD and CIC-IDS2017 datasets respectively. © 2024 IEEE.

关键词： Internet of things

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Trust-aware Routing Protocol using Modified Sand Cat Swarm Optimization in Iot-Based WSN

Trust-aware Routing Protocol using Modified Sand Cat Swarm O...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Yamsani, Nagendar Anitha, D. Mahaboob John, Y.M. Jamuna Rani, M. Sowmya, M. Sr University School of Computer Science and Artificial Intelligence Warangal India Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur India Mahendra College of Engineering Ece Department Salem India Sona College of Technology Department of Ece Salem India Nitte Meenakshi Institute of Technology Department of Ai and Ds Bengaluru India

ISBN: (纸本)9798350313413

The trust-aware secure route selection leverages trust-based metrics for evaluating the credibility of nodes and routes which enables the selection of the most reliable and secure paths for data transmission. Secure routing is of major significance for preventing wireless devices from other threats and effective characteristics in WSN attain an efficient secure routing selection. This research proposed the trust secure route selection utilizing Modified Sand Cat Swarm Optimization (TSRS-MSCSO) for efficient route selection. Initially, the secure nodes are identified by using energy and delay of the fitness function. After identifying secure nodes, the route selection is performed through the Modified Sand Cat Swarm Optimization algorithm. The performance of the proposed model is estimated by metrics like packet delivery ratio, throughput, delay and energy consumption with various nodes of 50, 100, 150, and 200. The proposed model attained a high packet delivery ratio of 98.8%, less delay of 3s, high throughput of 96.27%, and less energy consumption of 2.14J for 50 nodes which ensures better performance than other existing methods like Dynamic Deep Neural Network-based Particle Swarm Optimization (DDNN-PSO), Chaotic Bumble Bees Mating Optimization- Trust Sensing Model (CBBMOR-TSM) and fuzzy TOPSIS based hybrid Jarratt Butterfly Optimization (FTOPSIS-HJBO) models. © 2023 IEEE.

关键词： Energy Efficiency Internet of Things Route Selection Sand Cat Swarm Optimization Secure nodes Throughput Wireless Sensor Networks

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Avoidance of Ship Collision Using Deep Reinforcement Learning with Bi-Directional Long Short-Term Memory in Continuous Action Spaces 2

Avoidance of Ship Collision Using Deep Reinforcement Learnin...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Krishnamurthy, Revatthy Adnan, Myasar Mundher Sunil Kumar, V. Srinivas, T. Aditya Sai Aluvala, Srinivas Rajalakshmi Engineering College Artificial Intelligence & Machine Learning Department Chennai India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Artificial Intelligence and Machine Learning Jayaprakash Naryan College of Engineering Mahabubnagar India School of Computer Science and Engineering Sr University Warangal India

ISBN: (纸本)9798350373110

Recent days, with the globalization of the world economy the ships count are increasing for marine transportation and the Waterways are growing increasingly overcrowded than previous. This situation may leads to problem of collision of ships which may cause losing of life and damage for property and to nature. Many automatically collision models of ships are implemented but many focused only on the ship-ship encounter situation only. By using a grid sensor which is virtual sensor, agents of Deep Reinforcement Learning (DRL) classify approach of a multiple ships. This framework introduces an automatically collision detection algorithm for ships using DRL in continuous action spaces. DRL is used for avoidance of collision with a maximum distance of safe passing between ships. A unique method is developed named inside Obstacle Zone by Target (OZT) used to change learning capability that expands the OZT. Using Bi-directional Long Short-Term Memory (BI-LSTM) cell, network is redesigned and continuous action spaces training is carried out to train a model with longer safe distance of ships. In collision detection model that the bow cross range is effective for COLREGs compliant collision avoidance that is proposed in this model. The propose model also validates a scenario that included more ships and have passed that Imazu problem. The proposed BI-LSTM model achieved 80.43 % of accuracy, 95.67% of precision, 85.89% of recall and 93.54% of f1 score values. © 2024 IEEE.

关键词： Deep reinforcement learning

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Mayfly Optimization Algorithm with Bidirectional Long-Short Term Memory for Intrusion Detection System in Internet of Things 3

Mayfly Optimization Algorithm with Bidirectional Long-Short ...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Pillai, Sanjaikanth E. Vadakkethil Somanathan Polimetla, Kiran Alsalami, Zaid Pareek, Piyush Kumar Kumar, Deepak School Of Electrical Engineering And Computer Science University Of North Dakota Grand Forks United States Adobe Inc. Dept. Of Information Technology AustinTX United States College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Aiml And Ipr Cell Bengaluru India University Of The Cumberlands Department Of Information Technology WilliamsburgKY United States

ISBN: (纸本)9798350318609

The rapid development of number of connected devices exchange sensitive and personal significant data through Internet of Things (IoT)-assisted global network, attacks which are targeting security services are also enhancing every day. This paper proposed a Mayfly Optimization Algorithm (MOA) with Bidirectional Long-Short Term Memory (BiLSTM) for Intrusion Detection System (IDS) in IoT. The MOA is used for feature selection which select relevant features through balancing exploration and exploitation. The BiLSTM is used for classifying network into attack and normal which process the data in both forward and backward directions, allowing them to capture dependencies between events that might be far apart in sequence. The min-max normalization is used to normalize the continuous features from NSL-KDD, ToN-IoT and UNSW-NB15 datasets. The recall, accuracy, f1-score and precision are considered to evaluate MOA-BiLSTM performance. The MOA-BiLSTM realizes accuracy of 99.25%, 89.61% and 99.35% accuracy for NSL-KDD, ToN-IoT and UNSW-NB15 datasets when compared to existing techniques such as LSTM and Deep Neural Network (DNN). © 2024 IEEE.

关键词： Internet of things

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Adaptive Voting Mechanism with Artificial Butterfly Algorithm based Feature Selection for IDS in MANET

Adaptive Voting Mechanism with Artificial Butterfly Algorith...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Parameshachari B.D Achyutha Prasad N Dhanraj Manjunath T N Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering East West Institute of Technology Bengaluru India

Mobile ad hoc networks (MANETs) have gained more interest from consumers and academics than ever before thanks to the proliferation of wireless networks and the expansion of the benefits and uses of communication networks in general. MANETs are useful in a wide variety of settings since they don't rely on a centralised server or other hardware to relay messages or process data packets. It's one of the primary justifications for implementing MANET in many different domains. However, there are also numerous difficulties that have arisen as a result of these networks' rising popularity, with network security being one of the most crucial. There have been challenges with data transmission and reception due to MANETs' weak regulatory and security frameworks; network infiltration has been identified as one of the most pressing concerns. In MANETs, wireless nodes serve as relays and routers, connecting the source and sink nodes. Accordingly, it is now possible for rogue nodes to penetrate networks and destroy data packets. In order to cope with this issue, modern intrusion detection systems (IDSs) are utilised for remote monitoring of the functioning and actions of nodes present in wireless sensor networks. As well as being able to identify hostile nodes in the network, IDSs can often predict how such nodes will act in the future. In this research work, NSL-KDD dataset is used as an input data. SMOTE and Z-score method are used during pre-processing to remove the irrelevant features and normalize the data. The optimal features are carried out by Artificial Butterfly algorithm and then, finally, ensemble classifiers s. Multilayer Perceptron (MLP), Boosted Regression Trees (BRT) and finally, the adaptive voting mechanism is used to select the best classifier. The results proves that the proposed ensemble model achieved 97.16% of accuracy, where the existing models achieved nearly 95% to 96% of accuracy.

关键词： Adaptation models Wireless sensor networks Wireless networks Intrusion detection Feature extraction Ad hoc networks Classification algorithms

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Enhanced Intrusion Detection System using Pearson Correlation based Long Short-Term Memory

Enhanced Intrusion Detection System using Pearson Correlatio...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Phadke, Rekha Kumar, K Sudheer Almusawi, Muntather Baskar, N. Almoussawi, Zainab Abed Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India SR University School of Computer Science & Artificial Intelligence Warangal India The Islamic University Najaf Iraq CSE Malla Reddy Engineering College for Women Hyderabad India Ahl Al Bayt University College of Islamic Science Karbala Iraq

ISBN: (纸本)9798350315455

The Intrusion Detection System (IDS) is one of the common deep learning (DL) use cases which is used to finding and identifying outliers to prevent adversarial attacks, fraud and network intrusions. These systems play an important role in securing computer networks. The improvement of effective performance and security is the most needed in the IDS system. This paper aims to implement an IDS utilizing DL method. The DL model is the most encouraging technique and is widely used to detect intrusions. This paper implemented a Pearson's Correlation based Long Short-Term Memory (LSTM) model for intrusion detection system. The proposed Pearson's Correlation is evaluated on NSL-KDD dataset, and Min-Max normalization is a pre-processing method employed for normalize the data. Filter method of Pearson Correlation is utilized in the implemented for selecting the features. The LSTM is utilized to enhance system capability to identify and classify intrusion accurately and effectively. The implemented Pearson's Correlation based LSTM achieves better accuracy using NSL-KDD dataset with the value of 99.50% in term of accuracy, respectively. © 2023 IEEE.

关键词： Deep Learning Intrusion Detection System Long Short-Term Memory Pearson Correlation Security

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Removing/Minimising Gaussian noise from real time CCTV footages using GAN

Removing/Minimising Gaussian noise from real time CCTV foota...

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Innovations in Communications, Electrical and computer engineering (ICICEC), International Conference on

作者： Prashanth B S Puneeth B H Manoj Kumar M V Sreenivasa B R Adyapadi Suraj Akshay Prashant Hegde Pranava Aithal K S Department of Information Science & Engineering Nitte Meenakshi Institute of Technology Bangalore Karnataka India Department of Computer Science and Business Systems Bapuji Institute of Engineering and Technology Davanagere Karnataka India Department of Computer Science and Design Bapuji Institute of Engineering and Technology Davanagere Karnataka India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

This paper describes a system that uses generative adversarial networks (GANs) to eliminate gaussian noise from CCTV of footages. Unwanted noise like gaussian noise frequently degrades the quality of images and makes them more difficult to interpret. In our approach, a discriminator network is used to direct the training of a generator network, whose job it is to produce denoised images from noisy inputs. This framework includes operations like picture enhancement, noise reduction, and evaluation with metrics like the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). The resultant denoised images show enhanced visual quality and have potential uses in image analysis and computer vision.

关键词： Training Degradation Visualization Technological innovation PSNR Gaussian noise Superresolution Generative adversarial networks Real-time systems Synthetic data

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Design and Development of a safety system for Motorbike incorporated with Artificial Intelligence using Image Processing 2

Design and Development of a safety system for Motorbike inco...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Mohanapriya, A. Banu, D. Faridha Abiramasundari, S. Trisheela, S. Sri Eshwar College of Engineering Dept. of Ece India Sri Eshwar College of Engineering Coimbatore India Dr Mahalingam College of Engineering and Technology Dept. of Ece India Nitte Meenakshi Institute of Technology Department of Computer Science & Engineering Bengaluru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

Automobile collisions are consistently ranked among the leading causes of mortality worldwide. Accidents involving bicycles have been dramatically increasing in a wide variety of countries throughout the globe. As a result, the development of a system that can identify and mitigate some of the common errors or slip-ups that a biker makes is of the utmost importance. The development of technology in the sector of transportation has helped to reduce the number of accidents that occur by identifying factors that are essential to a safe voyage, such as the presence of a diving licence, the detection of the helmet, and tiredness. There are methods based on machine learning that may assist in the determination of necessary criteria for safety drives. © 2022 IEEE.

关键词： Accidents

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A Rapid Object Recognition Scenario Based on Improved YOLO-V3 Network

A Rapid Object Recognition Scenario Based on Improved YOLO-V...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Kumar, Suvarna Sunil Shreenath, K.N. Sunil, G. Vishwanath, P. Shankar, Smitha Sharath Malla Reddy Engineering College for Women It Department Hyderabad India Siddaganga Institute of Technology Department of Cse Tumakuru India School of Computer Science & Artificial Intelligence Sr University Warangal India Sir M. Visvesvaraya College of Engineering Electronics and Communication Engineering Raichur India Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India

ISBN: (纸本)9798350313413

The object recognition is an essential field in computer vision which comprises locating and recognizing objects within a given input image. The basic principle of these techniques is utilized to recognize certain object and detect their location through generating a list of squares to surround the object. This paper proposed a You Only Look Once Version3 (YOLOV3)-MobileNet for recognizing the object. The Caltech101 dataset is utilized in this research and preprocessed through min-max normalization and contrast enhancement techniques. The preprocessed images are fed into the integration of YOLOV3 and MobileNet classification. The YOLOV3 is known for real-time object detection capabilities and using MobileNet makes it more effective. The performance of YOLOV3-MobileNet is estimated through the performance measures of accuracy, precision, sensitivity, specificity and f1-score. The YOLOV3-MobileNet attained high accuracy of 96.82%, precision of 93.66%, sensitivity of 93.47%, specificity of 93.21% and f1-score of 93.08% which is comparatively higher than existing methods like Fuzzy C-means and Saliency-Hierarchical Max-poling (SAL-HMAX). © 2023 IEEE.

关键词： Contrast Enhancement Min-Max Normalization MobileNet Object Recognition YOLO-V3

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An Energy Efficient and Reliable Strategies for Intra-Cluster and Inter-Cluster Communications in Wireless Sensor Networks

An Energy Efficient and Reliable Strategies for Intra-Cluste...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Balmuri, Kavitha Rani Sujatha, J. Arvind, Sudha Lal, Bechoo Kumari C, Sapna Cmr Technical Campus Department of Information Technology Hyderabad India H. K. E. Society's S. L. N. College of Engineering Department of Computer Science & Engineering Raichur India Cmr Technical Campus Department of Electronics & Communication Engineering Kandlakoya India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

The development and widespread adoption of numerous wireless technologies are opening the door to novel forms of remote monitoring. Because of their adaptability, performance, low cost, and mobility, sensing devices are gaining in popularity. Due to their portability and flexibility, Wireless Sensor Networks (WSN) can be a viable alternative to hardwired infrastructure in geographically dispersed settings. There has been a rise in their popularity due to their capacity to detect and report changes in a wide range of environmental and physical variables, including temperature, sound, vibration, pressure, and many more. The primary goal of this study is to identify intra- and inter-cluster communication protocols that are suitable in terms of energy efficiency, scalability, and dependability. At this stage, the specified region is partitioned into clusters, and the leader nodes of those clusters are chosen according to predetermined criteria. Once a cluster has formed, periodic and aperiodic data can be sensed and communicated among members using appropriate MAC protocols. In order to take advantage of the best features of CSMA/CA and variable TDMA, these protocols need a third, hybrid way of data sensing. As a result, the proposed protocols require less power to run while still providing excellent reliability and scalability for a static network's periodic and event-based data transmission. © 2023 IEEE.

关键词： Wireless sensor networks

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