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An Enhancing Network Security: A Stacked Ensemble Intrusion detection system for Effective Threat Mitigation

An Enhancing Network Security: A Stacked Ensemble Intrusion ...

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international conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： Nagagopiraju Vullam D Roja NagaMalleswara Rao Sai Srinivas Vellela Lakshma Reddy Vuyyuru K Kiran Kumar Dept. of CSE Chalapathi Institute of Engineering and Technology Guntur AP India Dept. of CSE-Data Science Chalapathi Institute of Technology Guntur AP India Dept. of CSE Chalapathi Institute of Technology Guntur AP India Dept. of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram AP India

Modern cyber security relies heavily on intrusion detection in network traffic to quickly identify and mitigate security threats. Conventional intrusion detection systems frequently depend on lone, stand-alone models that could find it difficult to change with the ways that network attacks are evolving. In order to overcome this difficulty, this research study provides an ensemble-based strategy that improves threat detection effectiveness and accuracy by utilizing the strength of several intrusion detection models. Increasing security measures against data breaches and network invasions is essential due to the ever-growing usage of the Internet and networks. As intrusions are frequently hidden within of valid network packets, firewalls have a difficult time identifying and stopping them. Furthermore, the majority of network monitoring systems and algorithms find it increasingly difficult to handle the sheer volume of network traffic. Various intrusion detection strategies have been proposed in response to these issues, with machine learning techniques emerging as a possible route for handling these situations. This paper introduces an Intrusion detection system (IDS) that leverages stacking ensemble learning. The core ensemble comprises three fundamental machine learning models: k-nearest-neighbours, Decision Tree, and Random Forest. To enhance classification performance, the proposed system combines a total of seven machine learning algorithms with preprocessing methods. Stacking ensembles improve system performance greatly by combining the results of these basic models with a meta-model represented by the Logistic Regression algorithm. The UNSW-NB15 dataset is used to assess the IDS’s efficacy. With an astounding 96.16% accuracy rate in the training phase and an even greater 97.95% accuracy rate in the testing phase, the suggested IDS performs remarkably well. Additionally, the precision scores are remarkable, with training scores of 97.78% and testing scores of

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Refined Modeling and Simulation Method of Raw Signal Returns for Space-based Radar

Refined Modeling and Simulation Method of Raw Signal Returns...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Zhikang Wang Xingjuan Xi Guisheng Liao Lan Lan Hui Chen National Laboratory of Radar Signal Processing Xidian University Xi’an China Jiangsu Cyber Space Science and Technology Co. Ltd Nanjing China School of Information Science and Engineering Southeast University Nanjing China

Radar echo refinement simulation is an important means to provide data for space-based radar design demonstration. Most of the current simulation methods ignore the real surface topography and complex geomorphic distribution, and the target characteristics are not sufficiently described, resulting in a shortage of refined radar echo data, which is difficult to meet the demonstration requirements of space-based radar early warning detection system. This paper proposes a more refined simulation method for space-based radar echo, mainly from the background clutter and target echo two aspects of fine modeling: Firstly, based on the real ground battlefield environment fluctuation model and complex geomorphic distribution attributes constructed by GIS (Geographic Information system) data, a refined space-time two-dimensional clutter simulation model of space-based radar is constructed which simultaneously includes the characteristics of terrain fluctuation scattering distribution, terrain occlusion and diffraction, earth-sea clutter distribution and earth rotation. Secondly, based on the efficient modeling technology of ray-tracing high-frequency electromagnetic scattering, the target characteristics in the detection scene are finely modeled, which can fully consider the changes of target characteristics under different conditions. Finally, the rationality of space-time two-dimensional echo signal is verified and analyzed by simulation cases. The echo generated by this method is closer to the real situation, and provide support for the research of space-based radar system design and clutter suppression algorithm.

关键词： Fluctuations Diffraction Spaceborne radar Radar clutter Radar detection Radar Radar scattering

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Examining the Impact of Data Mining on Decision-Making Processes

Examining the Impact of Data Mining on Decision-Making Proce...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Vaishali Singh Pratik Pandey Prateek Aggarwal Reena R Suvarna R. Bhagwat Sunil MP Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Electronics and Communication Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India

Statistics mining has emerged as a crucial device for agencies in the past two decades that significantly impact selection-making methods. It can be used to gain insights from massive datasets and might assist with an employer's marketing approach, operational performance, customer segmentation, etc. This paper will explore the impact information mining has on decision-making approaches, especially regarding coming across new styles and relationships. It will discuss the blessings of using records mining and how it may help decorate choice-making. It will also look at various methods used to extract statistics from massive datasets and how they facilitate the selection-making method. Finally, it will discuss a number of the constraints of information mining and the ability of answers to overcome them. Standard, this paper aims to pick out the effect of information mining on the choice-making system and how it may be leveraged as a practical device.

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Comparison of Isolation Forest and One Class SVM in Anomaly detection of Gas Pipeline Operation

Comparison of Isolation Forest and One Class SVM in Anomaly ...

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Intelligent Cybernetics technology & Applications (ICICyTA), international conference on

作者： Kelvyn Lukito Aditya Firman Ihsan Hasmawati School of Computing Telkom University Bandung Indonesia

Detecting data anomalies in the operational process of oil and gas pipelines is very important to reduce the risk of disasters, which can adversely affect human safety, the environment and financial aspects. Failure to do so can lead to catastrophic results. The problem is also supported by several catastrophic events that have occurred in several areas of oil and gas production facilities in several regions. To solve this problem, it is necessary to implement a suitable monitoring system that aims to prevent potential losses caused by leaks or over-pressurization of natural gas pipelines. Among the many machine learning algorithms available for anomaly detection such as Feed Forward Neural Network, Linear Regression, KNN, Random Forest, and Support Vector Machine and unsupervised machine learning models such as Principal Component Analysis (PCA) and Hierarchical clustering, One-Class SVM and Isolation Forest are the most prominent. However, these algorithms have their own advantages and disadvantages regarding their performance. This study aims to compare the performance of machine learning algorithms in classifying and detecting data anomalies in offshore natural gas pipeline operational datasets. The assessment is based on ROC-AUC Curve, Confusion Matrix, Sensitivity, and Specificity. The findings indicate that the Isolation Forest model outperforms the One-Class SVM, with a ROC-AUC value of 90%, compared to the One-Class SVM's value of only 61%. Furthermore, the Isolation Forest exhibits a Sensitivity value of 98%, in contrast to the One-Class SVM's 41%, and a Specificity of 81%, compared to the One-Class SVM's 80%.

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Facial Emotional Recognition Using Faster Regional Convolutional Neural Network with VGG16 Feature Extraction Model

Facial Emotional Recognition Using Faster Regional Convoluti...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Nagendar Yamsani Muhammed Basim Jabar Myasar Mundher Adnan A. H. A. Hussein Subhra Chakraborty School of Computer Science and Artificial Intelligence SR University Warangal Telangana India National University of Science and Technology Dhi Qar Iraq The Islamic university Najaf Iraq College of Pharmacy Ahl Al Bayt University Karbala Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

Facial Emotion Recognition (FER) poses a significant challenge when training deep learning (DL) models like Artificial Neural Network (ANN) and machine learning (ML). The primary issue in FER is the detection of the face, the creation of bounding boxes, and the classification of emotional expressions. This paper proposes the use of Faster Regional Convolutional Neural Network (RCNN) for facial recognition and employs a median filter to filter the image dataset. The model leverages the Facial Emotional Recognition (FER 2013) dataset for training and predicting accurate classifications. The Median Filter is used to eliminate noise in the image, such as Salt and Pepper noise, and data augmentation is utilized to generate synthetic data for addressing minor imbalances in classes. Subsequently, the VGG 16 architecture is implemented for precise feature extraction to reduce complexity in the classification process. This paper, achieved accuracy, precision, recall, specificity, and f1-score values of approximately 78.22%, 75.40%, 80.20%, 85.90%, and 71.40%, respectively, when compared to other classification methods like Convolutional Neural Network, ResNet 50, Divide-And-Conquer-Based learning, and Resnet 18.

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A New Design for Protecting Cyber-Attacks and Harmful Threats in IoT Communication Network with Efficient Deep Learning-Based detection system

A New Design for Protecting Cyber-Attacks and Harmful Threat...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： S. Dhinakaran Vipul Bansal Kumud Pant Sanjeev Kumar Joshi Umme Habiba Maginmani Ch. Raghava Prasad Department of Computer Science Rathinam College of Arts and Science Coimbatore Tamil Nadu India Information Technology Kurukshetra University Haryana Department of Biotechnology Graphic Era Deemed to be University Dehradun Uttarakhand India Uttaranchal Institute of Technology Uttaranchal University Department Of Computer Science & Engineering Secab Institute Of Engineering and Technology Vijayapur Karnataka Department of Electronics and Communication Engineering KoneruLakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

Fast progress in both smart devices with limited resources and state-of-the-art communication methods have helped establish the Internet of Things (IoT) as the gold standard for low-power lossy networks. However, because to the low computing, storage, and networking capabilities of the endpoint devices, these networks are susceptible to cyber assaults. IoT network security solutions are often rendered ineffective since many attacks are produced by slightly modifying previously known assaults. With their superior performance, deep learning algorithms have seen rapid adoption in security applications in recent years. In this paper, we introduce the IoT-IDCS-CNN, a brand new, unsupervised deep learning-based method for detecting and categorising cyberattacks in IoT communication networks. The system uses high-performance computing on dependable Nvidia GPUs, and parallel processing on fast Intel CPUs. It is structured into three parts: a traffic classification module, a feature learning module, and a refinement module for engineering features. Each part is developed independently and then tested and combined to create the complete system.

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Comparison of Various Classifiers for Efficient Plant Diseases detection Using Image Processing Techniques

Comparison of Various Classifiers for Efficient Plant Diseas...

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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： Rinku Garg Ashish Sharma Atul Babbar Vishal Goyal Khlood Waheeb Anita Gehlot University Institute of Computing Chandigarh University Mohali India Department of Electrical engineering Chandigarh University Mohali India Department of Mechanical Engineering SGT University Gurugram India Department of ECE GLA University Mathura India Medical technical college Al-Farahidi University Iraq Department of ECE Uttaranchal Institute of Technology Uttaranchal University Dehradun India

Processing images of the fruit, the leaves, or the entire plant, image processing enables the early diagnosis of fruit illnesses. In order to stop the spread of illnesses and reduce crop losses, early diagnosis is essential. Image processing techniques can identify subtle changes in color, texture, and shape that are often indicative of disease symptoms. This level of precision and accuracy is challenging to achieve through human visual inspection alone. Image processing provides an objective and consistent analysis of disease symptoms. Human judgment can be influenced b y various factors, whereas i mage processing algorithms follow predefined criteria, reducing s ubjectivity, To create predictive models, image processing may be combined with other technologies like machine learning and artificial intelligence (AI). Based on past data and environmental factors, these models can predict disease outbreaks. In this work we are discussing various existing image processing techniques that will predict the diseases in fruit efficiently and accurately.

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End to End Scalable Image Coding for Machines

End to End Scalable Image Coding for Machines

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Intelligent Communications and Computing (ICC), international conference on

作者： Zijun Wu Hanming Wang Huifen Wang Yuan Zhang Big Data and Artificial Intelligence China Telecom Shanghai China

With the rapid advancements in image data acquisition technology, there has been a significant improvement in the resolution of captured images. This progress enables clear display of intricate details. However, to support downstream tasks such as object detection and semantic segmentation, image encoding in machine vision requires compression and transmission of source images for machine analysis. Additionally, it involves allocating transmitted bitstreams to different task networks and controlling bit rates. In this article, we propose a deep progressive image codec framework specifically designed for machine vision tasks. Our approach consists of several key steps. Firstly, we convert the image into a potential tensor representation. Then, utilizing a depth-constrained image encoder, we encode each element of the tensor into a marching plane. Subsequently, we decode the marching planes into images of varying quality based on the decoding points' priority. To enhance the framework's suitability for machine vision tasks, we perform joint training with downstream task networks. This collaborative training ensures that the codec aligns effectively with the requirements of these tasks. By integrating the feedback from the downstream networks, we optimize the codec's performance in relation to machine vision objectives. Experimental results demonstrate that the proposed framework not only effectively guarantees the accuracy of downstream tasks but also enables substantial savings in transmission bit rates. Additionally, the framework meets the demands of reconstructing images of varying quality without requiring further training.

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Dentistry in the Age of Automation: The Benefits and Challenges of AI and Robotics

Dentistry in the Age of Automation: The Benefits and Challen...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Joel Alanya-Beltran Jesús Padilla-Caballero S. K. Shah Srinivas O. Parveen Kumar Tomar V. Mareeskannan Information and Communication Technology Universidad Tecnológica del Perú Lima Peru South America Postgraduate Department Universidad César Vallejo Ate Peru South America Uttaranchal Institute of Technology Uttaranchal University Dehradun Uttarakhand India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of Electronics & Communication Kurukshetra University Haryana India

The integration of artificial intelligence (AI) and robotics into healthcare has been gaining momentum over the years. However, there is a gap in the understanding and awareness of dental practitioners regarding the use of AI and robotics in dental practice and oral healthcare. This study aimed to evaluate the awareness and understanding of dental practitioners regarding the application of AI and robotics in oral care management. The study was conducted using a self-directed questionnaire on 350 dental professionals aged between 20 to 55 years. The study found that the majority of respondents (59.1%) believed that AI would significantly enhance both dental and healthcare. Additionally, 54.9% of respondents predicted that AI would eventually replace dentists and medical professionals. A significant percentage of respondents (63.6%) agreed that AI could be utilized for 3-D implant placement and scheduling. The findings of this study have several implications for dental practice and education. The study highlights the need for dental practitioners to keep up with technological advancements in AI and robotics to provide the best care for their patients. The findings suggest that policymakers should consider the integration of AI and robotics into the healthcare system to improve oral health outcomes. Further research is needed to evaluate the effectiveness of AI and robotics in dental practice and oral healthcare.

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Continuous Glucose Monitoring: A Non-Invasive Approach for Improved Daily Healthcare

Continuous Glucose Monitoring: A Non-Invasive Approach for I...

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Electronic and Electrical Engineering and Intelligent system (ICE3IS), international conference on

作者： Karisma Trinanda Putra Indar Surahmat Anna Nur Nazilah Chamim Muhammad Zhafran Ramadhan Darma Wicaksana Raden Ajeng Dhea Namyra Alissa Department of Electrical Engineering Center of Artificial Intelligence and Robotics Studies Universitas Muhammadiyah Yogyakarta Bantul Indonesia Department of Electrical Engineering Universitas Muhammadiyah Yogyakarta Bantul Indonesia

Diabetes mellitus is a prevalent health condition affecting approximately one in eight individuals worldwide. Insufficient management of this disease can lead to various medical complications, including vision loss and limb amputation. Continuous glucose monitoring (CGM) is crucial for effective diabetes management. However, current one-time glucose monitoring methods still rely on invasive techniques, causing pain and lacking continuous monitoring capability. This study aims to develop a non-invasive technology for measuring blood glucose levels that can be conveniently monitored through a smartphone using wireless communication. The device utilizes an ESP32 microprocessor and a PPG MAX 30102 sensor capable of recording pulse waves, a biomarker, at the fingertips. In this preliminary study, non-invasive data from 22 participants were used and divided into three classes according to conventional invasive measurements. Artificial intelligence (AI) methods, specifically Convolutional Neural Networks (CNN) and Artificial Neural Networks (ANN) models, were utilized to achieve convergence between the conventional invasive measurements and the proposed approach. The trained model demonstrated the highest accuracy of 0.944, with a minimum model loss of 0.177. In the validation phase, the proposed approach proved its reliability by achieving an average accuracy of 0.750. The non-invasive approach has the potential to provide a convenient and pain-free alternative to traditional monitoring methods, significantly enhancing diabetes management and improving the overall quality of life for those affected by this condition.

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