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Leveraging XGBoost based GBM for Proactive Detection of Man-in-the-Middle Cyber Attacks

Leveraging XGBoost based GBM for Proactive Detection of Man-...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Manjaragi, Shivanand V. Asrani, Deepak Kumar, Arvind Sandesh, R. Maindola, Meenakshi Sai, Aarush Department of Computer Science and Engineering Hirasugar Institute of Technology Karnataka Nidasoshi591236 India Department of Computer Science and Engineering MIET Uttar Pradesh Greater Noida India SCA MRIIRS Haryana Faridabad India Department of Computer Science and Engineering ATME College of Engineering Karnataka Mysuru India Department of Computer Science Engineering Graphic Era Deemed to be University Uttarakhand Dehradun India Department of CSBS Gyan Ganga Institute of Technology and Sciences Madhya Pradesh Jabalpur India

ISBN: (纸本)9798350374933

Man-in-the-Middle (MitM) cyber-attacks provide a substantial risk to network security, since they allow hostile individuals to intercept and alter communications between two parties. Conventional detection approaches frequently fail to recognize these complex threats, requiring the creation of more resilient and precise solutions. This study introduces a model called XGBoost-based Gradient Boosting Machine (GBM) to identify MitM attacks in advance. The system employs extensive datasets from Kaggle, namely CICIDS 2017 and UNSW-NB15, for the purpose of training and evaluating the model. By carefully preparing the data, selecting relevant features, and doing thorough cross-validation with hyperparameter optimization, the suggested model attains exceptional performance metrics. The assessment findings indicate an accuracy of 0.98, precision of 0.97, recall of 0.96, F1-Score of 0.965, and a remarkable ROC-AUC score of 0.99. Our model regularly outperforms existing technologies like as Random Forest and Traditional GBM in identifying MitM attacks, achieving higher performance metrics across the board. This research demonstrates the effectiveness of the XGBoost-based GBM model in improving cybersecurity defenses and suggests that it might serve as a standard for future advancements in cyber-attack detection systems. The suggested system's exceptional accuracy and resilience make it an invaluable tool for proactively detecting and reducing MitM attacks. © 2024 IEEE.

关键词： Cyber attacks

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Artificial Neural Network Based Selection Method for Antenna Parameters in Microwave Application

Artificial Neural Network Based Selection Method for Antenna...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Prabha, R. Senthil, G.A. Priyadharshini, J. Subashini, V. Rajesh Kanna, R. Divya, B. Sri Sai Ram Institute of Technology Department of Electronics and Communication Engineering Chennai India Agni College of Technology Department of Information Technology Chennai India Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Ramapuram Campus Chennai India Sri Sai Ram Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350394580

The usage of machine learning in the field of microwave engineering has increased. This project proposes a design optimized model in order to achieve parameter optimization of 2 × 2 square patch antenna using Artificial Neural Network (ANN) Technique. An artificial neural network was trained to get the optimum value of the antenna parameters in less time. The input of the model is length, width of the square patch and range of antenna parameter value, the output is S-parameters, VSWR. This was made a perfect choice for communication systems engineers due to its low weight, low cost. The formulations are used to calculate the neural network cost function. The proposed neural network can tune and get the best value for the parameter. Then with the help of a machine learning algorithm, geometrical parameters of the patch antenna were designed and optimized. The substrate called fr4 is used. Hence a 2 × 2 square patch antenna element was optimized, designed, and developed. © 2023 IEEE.

关键词： Antenna Artificial Neural Network (ANN) Deep Neural Network Rectified Linear Unit (ReLU) S-Parameters

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An Investigation into Round Robin and Random Algorithms for the Purpose of Load Balancing on Web Servers in SDN Environments

An Investigation into Round Robin and Random Algorithms for ...

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Kadam, Sanjay Ingle, D.R. Jadhav, Tushar Tanaji Damal, Hitesh Lakshmidhar Kadam, Ashitosh Bramhadeo Kale, Aditya Nagesh Information Technology Bharati Vidyapeeth College of Engineering Navi Mumbai India Computer Science Bharati Vidyapeeth College of Engineering Navi Mumbai India

ISBN: (纸本)9798350333602

The Future of computer Networks will be Software Defined Networks (SDN) as it is efficient and has the capabilities to replace the Traditional Networks due to the increasing number of nodes connected to the internet, it is becoming more difficult to adapt to the latest technology in order to maintain its scalability. This is why it is important that the research community is able to test the various aspects of Software-Defined Networking (SDN) to ensure that it can meet the requirements of the industry. Besides being able to provide a solution that can overcome the traditional network limitations, it also allows the researchers to develop a more robust and resilient network. One of the most common controllers used for implementing SDN features is the Ryu Controller. This research aims to provide a comprehensive analysis of the performance of this component in terms of load balancing, through a simulation tool known as Mininet, the researchers were able to perform various tests on the system. The framework known as the Ryu is a software-defined networking framework that is built on components. It allows developers to create easy-to-use control and management applications for network devices. It supports various protocols such as OpenFlow and OVSDB, Ryu fully supports OpenFlow versions 1.0, 1.2, 1.3, 1.4, 1.5, and Nicira Extensions. © 2023 IEEE.

关键词： Software defined networking

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Innovative Practices for Reliable Tuberculosis Detection with Deep Learning

Innovative Practices for Reliable Tuberculosis Detection wit...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Manju, K. Dinesh, P.M. Jeyageetha, K. Lakshmi, G. Prasanna Thangavel, Kuntavai Alagarsamy, Manjunathan Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India Ramco Institute of Technology Department of Computer Science and Engineering Tamil Nadu Rajapalayam626117 India Sandip University School of Computer Science and Engineering Maharashtra Nashik India Adhiparasakthi Engineering College Melmaruvathur Department of Electrical and Electronics Engineering Tamil Nadu Chengalpattu603319 India K.Ramakrishnan College of Technology Department of Electronics and Communication Engineering Tamil Nadu Trichy India

ISBN: (纸本)9798350390049

A bacterial infection is the primary cause of the chronic lung disease tuberculosis, among the ten most common causes of death. Because it may be fatal, TB must be accurately and promptly detected. The identification and treatment of the problem caused by TB require the accurate detection of the disease using image pre-processing, Deep learning, picture segmentation, and data augmentation categorization using X-ray pictures of the chest. Before being employed for transfer learning, the initial weights of nine distinct deep CNN's (ResNet18, ResNet50, ResNet101, Chex Net, InceptionV3, Vgg19, DenseNet201, Squeeze Net, and Mobile Net) were trained, validated, and tested for TB and non-TB case detection. © 2024 IEEE.

关键词： Image segmentation

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Low-Speed Collision Prevention Warning for Intelligent Vehicles based on Digital Twin and Deep Learning 2

Low-Speed Collision Prevention Warning for Intelligent Vehic...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Wang, Han Niu, Wenlai Liu, Yahui Tan, Ye Xiu, Chunlian Chongqing University Of Technology College Of Computer Science And Engineering Chongqing China Chongqing University Of Technology School Of Vehicle Engineering Chongqing China Chongqing University Of Technology School Of Foreign Languages Chongqing China

ISBN: (纸本)9798350381665

The low-speed collision prevention warning system for intelligent vehicles is a critical safety technology aimed at reducing low-speed road collision accidents. This study introduces an intelligent vehicle low-speed collision prevention warning method based on digital twin technology and deep learning algorithms. Firstly, a digital twin model was constructed to simulate the vehicle and its surrounding environment, providing a better understanding of the road situation. Subsequently, convolutional neural networks (CNNs) were employed for object detection, identifying other vehicles and obstacles from video frames captured by the vehicle's front camera. On this basis, a spatial attention mechanism was introduced to reduce the impact of irrelevant targets on collision prediction, thereby improving the system's accuracy. Through motion tracking, collision prediction, and warning decision-making, the system can detect potential collision hazards and provide timely warnings to the driver, helping to reduce the occurrence of low-speed collision accidents. The experimental results indicate that the method has achieved good performance in various road scenarios, showing potential for playing a crucial role in the safety of intelligent vehicles and road safety. This study provides new insights and approaches for low-speed collision prevention technology in the field of intelligent vehicles. © 2024 IEEE.

关键词： Deep learning

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Mountain Gazelle Optimizer-Tuned PID Controller for Frequency Stabilization of PV-Reheat Thermal Power System 8

Mountain Gazelle Optimizer-Tuned PID Controller for Frequenc...

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8th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2024

作者： Izci, Davut Ekinci, Serdar Abualigah, Laith Ghandour, Raymond Salman, Mohammad Batman University Department of Computer Engineering Batman Turkey Al al-Bayt University Computer Science Department Mafraq Jordan College of Engineering and Technology American University of the Middle East Kuwait

ISBN: (纸本)9798350354423

Due to its vital function in controlling power generation and demand, the load frequency control (LFC) system is the focus of this study. As uncertainty around renewable energy sources and shifting demand patterns grow, so does the requirement for accuracy in the LFC system's implementation. This research investigates the dynamics of LFC in a hybrid power system that operates in two domains and consists of a photovoltaic (PV) system and a reheat thermal system. To enhance system performance, a proportional-integral-derivative (PID) controller is implemented. Furthermore, the work describes how the controller settings were tuned using a unique optimization method known as the mountain gazelle optimizer (MGO). This method has not been investigated in LFC designs. The objective function of the optimization method is an integral of time-multiplied absolute error (ITAE). The MGO-tuned PID controller's performance is contrasted with established techniques from recent literature. The results show that the proposed strategy is superior to the present methodologies, as evidenced by the improvement in damping durations and decrease in overshoot values for both system frequency and tie-line power variations. © 2024 IEEE.

关键词： Three term control systems

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Printed MIMO Antenna for C and X band Applications 14

Printed MIMO Antenna for C and X band Applications

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Rajalakshmi, J. Ranjani, S. Siva Selvi, S. Arun Mozhi Muthuraja, T. Sethu Institute of Technology Department of Electronics & Communication Engineering Kariapatti Virudhunagar India Sethu Institute of Technology Department of Computer Science and Engineering Kariapatti Virudhunagar India Holy Cross Engineering College Department of Computer Science and Engineering Thoothukudi India

ISBN: (纸本)9798350335095

In this paper, MIMO (Multiple-Input Multiple-Output) antenna is introduced. The proposed MIMO antenna is to achieve a multiband response and high efficiency. This antenna is fabricated on FR4 substrate with the thickness of 0.8 mm. The size of the antenna is 30*30mm2. The field distribution of proposed antenna is observed. The fabricated antenna results is tested and compared. The proposed antenna is suitable candidate for C and X band applications. © 2023 IEEE.

关键词： Microwave antennas

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Automating ML Models Using MLOPS 2

Automating ML Models Using MLOPS

引用

2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Sirisha, N. Kiran, Ajmeera Arshad, Mohammad Mounika, M. Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad Dundigal India Deccan College of Engineering and Technology Department of It India B v Raju Institute of Technology Department of Ai & Ds India

ISBN: (纸本)9798350370188

Machine learning engineering relies on MLOps, a collection of best practises for commercializing models. As fresh data is added, machine learning models can become less effective. ML models can't use all data. To do this, we must reimagine the model by changing the front and back ends. Our project regularly tracks and trains models to overcome this constraint. MLOps (Machine Learning Operations) can help. ML pipeline automation integrates the trained model into the cloud, which can take a dataset as input and output. To quickly update the cloud ML algorithm in response to fresh data, we don't need to change the model. This guarantees the chosen model is valid and can be used with fewer flaws, improving performance. Our main objective is to build a reliable automated pipeline and install, maintain, and optimise machine learning models and systems securely and efficiently. © 2024 IEEE.

关键词： Machine learning

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Instantaneous Trajectory Prediction via Latent Bidirectional Cooperative Diffusion

Instantaneous Trajectory Prediction via Latent Bidirectional...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kun Ma Qilong Han Jingzheng Yao Changmao Wu Chunrui Na College of Computer Science And Technology Harbin Engineering University Harbin China Yantai Research Institute Harbin Engineering University Yantai China Chinese Academy of Sciences Institute of Software Beijing China College of Shipbuilding Engineering Harbin Engineering University Harbin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In real-world scenarios, extreme cases where pedestrians suddenly emerge from blind spots or occlusions, leaving only a minimal amount of observable trajectory points, occur frequently. This presents a significant challenge for autonomous driving and robotic navigation, where pedestrian safety and timely response are critical considerations. To address this challenge, we propose a framework for instantaneous trajectory prediction using Latent bidirectional Cooperative Diffusion (LCD). It designs a complementary mechanism that constructs a coupled bidirectional cooperative diffusion model. LCD simultaneously and progressively generates unobserved past trajectories and future trajectories, feeding each other as conditions into the cross-attention module for mutual guidance. This framework employs CVAE as its encoder to map the observed multi-model trajectories into a high-dimensional latent space to enhance complex representations. Experiments conducted on the ETH/UCY and SDD datasets demonstrate the superiority of our framework.

关键词： Pedestrians Navigation Signal processing Diffusion models Liquid crystal displays Trajectory Safety Speech processing Robots Autonomous vehicles

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Investigating the Utility of Data Mining for Automated Credit Scoring 15

Investigating the Utility of Data Mining for Automated Credi...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Yadav, Dhyan Garima Soni, Rudransh Anandhasilambarasan, D. Sasirekha, R. Prema, S. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Karnataka Bangalore India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore641021 India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Department of Artificial Intelligence & Data Science Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798350370249

Data mining is a powerful device for automatic credit scoring. This paper will look into the application of statistics mining for automated credit score scoring by discussing its advantages and downsides. The form may even look at cutting-edge research in the location and discuss the demanding situations that information mining techniques pose while used for automatic credit score scoring. Similarly, the paper will provide pointers on how statistics mining techniques can improve the effectiveness of computerized credit score scoring. Subsequently, a precis of the paper's conclusions and implications for future studies could be supplied. © 2024 IEEE.

关键词： Conclusions Implications Improve Information Scoring

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