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Convolutional Neural Network-Based Anomaly Detection for Cyber Security: A Survey

Convolutional Neural Network-Based Anomaly Detection for Cyb...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Neha Agarwal Amandeep Kaur Department of Computer Science Engineering Vivekananda Global University Jaipur India Department of Master of Computer Application Chandigarh School of Business Jhanjeri

There are many security risks as computer and communication technologies continue to advance. To reduce these worries, several different kinds Numerous processes with cyber-security enabled have been developed. Anomaly detection is one of these methods. Finding anomalies involves employing a variety of approaches and procedures to identify various patterns that do not adhere to predetermined characteristics of the entire body of data. Deep learning techniques have recently been accepted as an adequate option due to their capacity to take data properties directly from the data. Convolution neural network (CNN) is employed primarily because it can handle input with many dimensions. This study offers a thorough analysis of the use of CNN as a primary tool for anomaly identification. The majority of the known remedies have been compiled and categorized in the literature. Additionally, this research provides a bound together cross framework that mimics complete anomaly detection methods that were previously offered examinations, as per the information source. In-depth assessment from a brought together cross system upgrades this paper’s top to bottom conversation of how CNN is utilized in the writing’s response to the irregularity identification issue. Finally, this article makes a number of recommendations for future study areas that the audience may find useful in their own further activities in this area.

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Classification of Garbage For Robtic System Using Deep Learning Techniques 6

Classification of Garbage For Robtic System Using Deep Learn...

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6th International Conference on Intelligent Computing and Control Systems, ICICCS 2022

作者： Poojary, Harshita Dooja Shinde, Nachiket Mahesh Singh, Akash Kumar Kothuru, Vivek Kshirsagar, Saurabh Bhaskarrao Agarwal, Harsh Ramrao Adik Institute of Technology Department of Computer Engineering Mumbai India Bharati Vidyapeeth Institute of Management Kolhapur Institution Department of Computer Application Mumbai India Galgotias University Department of Computer Science and Engineering Greater Noida India Niit University Department of Computer Science Engineering Rajasthan India Savitribai Phule Pune University Department of Mechanical Engineering Pune India Chaitanya Bharati Institute of Technology Department of Chemical Engineering Hyderabad India

ISBN: (纸本)9781665410359

Humans are living in the world where garbage can be seen everywhere. Especially in countries like India garbage is a major problem which leads to Air, Water and Soil pollution. There are many ways to reduce garbage pollution like Disposing waste properly, Recycling, Reusing etc. But you know what garbage can be turned into a useful asset for example Waste to Energy plants which burn solid waste and produce Electricity. This kind of thing is only possible if the waste is separated from one another. Lacking of proper garbage classification system leads to excessive waste of garbage which causes harmful effect to our environment. In that case it is highly required to collaborate with technologies like Convolutional Neural Network (Artificial Intelligent Machine which learns directly from data) which help us to classify waste. InceptionV3 is one of the garbage recognizers which use CNN and Data Augmentation to recognise garbage and sort them. According to sources its accuracy is 99.3 percent. Countries like China, Columbia and Indonesia is also working toward garbage recognizer machine which is fruitful for the environment as well as for the peoples living here. There are many techniques through which can be used in the robotic systems to segregate the waste accordingly using the machine learning and deep learning techniques. The main aim of the paper is to compare the deep learning techniques and prove which is the best technique that can be used for a particular system. © 2022 IEEE.

关键词： Deep learning

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Segmentation of Liver Tumors in CT Scans Using Active Contour Models and Machine Learning

Segmentation of Liver Tumors in CT Scans Using Active Contou...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Sharma, Rajita Alabdeli, Haider Baghel, Abhishek Singh Jayashankari, J. Maheswari, G. Dhanashree, S. Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Hindustan Institute of Technology and Science Department of Information Technology Chennai India Saveetha Institute of Medical and Technical Sciences Department of Ai & Ml Tamilnadu Chennai India

ISBN: (纸本)9798350390759

Liver tumor segmentation in CT scans plays a crucial role in cancer diagnosis and treatment planning. This paper proposes a novel methodology that integrates Active Contour Models (ACMs) with Convolutional Neural Networks (CNNs) for accurate and efficient segmentation of liver tumors. The approach leverages the feature learning capabilities of deep learning and the precise boundary detection of ACMs to achieve state-of-the-art segmentation results. Evaluation on the LiTS dataset demonstrates superior performance in terms of Dice Similarity Coefficient (DSC), Precision, Recall, and computation time. The proposed methodology offers a promising solution for improving clinical decision-making in liver cancer treatment. © 2024 IEEE.

关键词： Convolutional neural networks

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Prediction of Cardiac Arrest using Generalized Soft Computing Techniques

Prediction of Cardiac Arrest using Generalized Soft Computin...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Radhanath Hota Sachikant Dash Sujogya Mishra Subhalaxmi Das S Lenka G Pradhan Department of Computer Science Engineering GIET University Gunupur Odisha India Department of Computer Science Engineering Gandhi Institute for Education and Technology Gunupur Odisha India Department of Mathematics Odisha University of Technology and Research Bhubaneswar India Dept. of Computer Science and Engineering Odisha University of Technology and Research Bhubaneswar India Department of Computer Science and Application Utkal University Bhubaneswar India

Cardiac-related diseases always lead to fatal consequences around the globe. This research includes vague data analysis of the data related to cardiac anomalies. This study proposes a method to develop a dimension reduction model using vague data related to cardiac diseases using rough set theory (RST). The overall research is divided into two different sections. The first section of this study proposes analyses of the raw cardiac-related data using RST, and the second section calculates the performance accuracy using Principal Component Analysis (PCA) applied to the universal data set; the performance measures also include a combination of two Universal data sets. The above claims are verified using statistical tests

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A Multi-Objective Decision Assistance System for Selecting Security Controls Based On Simulation

A Multi-Objective Decision Assistance System for Selecting S...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Ritu Shree Komal Sandhu Department of Computer Science Engineering Vivekananda Global University Jaipur India Department of Master of Computer Application Chandigarh School of Business Jhanjeri

In this work, we present the results of our continuing investigations into simulation-based risk analysis for information security and multi-objective investment optimization for security controls. We provide a methodological approach that takes into account the organization's features, information architecture, assets that must be safeguarded, unique threat sources encountered, and decision-makers' risk preferences. This framework includes dynamic attack graph generation techniques, ontological modelling of security knowledge, probabilistic reenactment of assaults by objective driven danger specialists, Identification of compelling arrangement of data security controls utilizing meta-heuristics, and intuitive choice help. These components make it possible for cutting-edge techniques to generate attack graphs and infer potential attack routes based on the motivations, goals, skills, and available entry points of attackers, Then, using this inferred information, simulate assaults based on the infrastructure a model of an organisation. The strategy aids decision-makers weigh financial and non-financial factors related to risks, costs, and benefits when assessing possible security control initiatives. In order to assess the strategy through application case studies, we are now constructing a prototype implementation of the framework.

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SQAT-LD: SPeech Quality Assessment Transformer Utilizing Listener Dependent Modeling for Zero-Shot Out-of-Domain MOS Prediction

SQAT-LD: SPeech Quality Assessment Transformer Utilizing Lis...

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IEEE Workshop on Automatic Speech Recognition and Understanding

作者： Kailai Shen Diqun Yan Li Dong Ying Ren Xiaoxun Wu Jing Hu Faculty of Electrical Engineering and Computer Science Ningbo University Zhejiang Key Laboratory of Mobile Network Application Technology

In this paper, we propose the speech quality assessment transformer utilizing listener dependent modeling (SQAT-LD) mean opinion score (MOS) prediction system, which was submitted to the 2023 VoiceMOS Challenge. The system is based on a combination of self-supervised learning (SSL) models and listener-dependent modeling. Due to this challenge’s emphasis on real-world and challenging zero-shot out-of-domain MOS prediction in three different voice evaluation scenarios, we specifically designed a two-branch module to predict scores and weights for each frame, aiming to achieve better generalization. In the challenge, our system achieved fourth place in Track 1a, second place in Track 1b and first place in Track 2. Additionally, we conducted an ablation study to investigate the effectiveness of our proposed method.

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Comparison of Tumor Segmentation Techniques from Medical Images 7

Comparison of Tumor Segmentation Techniques from Medical Ima...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Gururaj, Bharathi Nair, Prajith Prakash Harish, L. Shanmugasundaram, V. Alaskar, Kamal Maranan, Ramya Ece Acs College of Engineering Karnataka India Cmr Institute of Technology Information Science and Engineering Karnataka India Sona College of Technology Department of Electrical and Electronics Engineering Chennai India Institute of Management Department of Computer Application Maharashtra Kolhapur India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798350341485

A Brain Tumor (BT) is one of the leading causes of mortality worldwide. Because of this, early diagnosis is crucial. BT localization and segmentation from magnetic resonance imaging (MRI) is a challenging but crucial task with many medical analytic applications. Many methods have been presented for tumor segmentation. However, they each have their issues, such as reliance on manual intervention from experts and uncertainty over which feature extractor to use. To overcome these obstacles, a novel, hybrid strategy for tumor region segmentation based on a convolutional neural network is suggested. Kaggle's Brain MRI data was used for training and testing the proposed approach. First, a pre-processing strategy is used to operate on a selected portion of the image instead of the entire image to obtain a robust and adaptable BT segmentation system. Reduced processing time and no overfitting issues are the results of this strategy. Second, the Res-SegNet hybrid model was employed, now the model works only with a smaller portion of the MRI Accuracy, loss, Intersection of Union (IoU), and Dice coefficient are some of the metrics that were used to gauge how well the proposed method performed. When applied to tumor segmentation, the model achieved an accuracy of 0.99, an IoU of 0.745, a Dice of 0.831, and a loss of 0.176. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Vector angle-based and ISDE+ indicator selection for many-objective evolutionary optimization 3

Vector angle-based and ISDE+ indicator selection for many-ob...

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3rd International Conference on Electronic Information engineering, Big Data, and computer Technology, EIBDCT 2024

作者： Liu, Peng Chen, Hanning Liu, Keyi Wang, Guopeng Song, Lizhe Xia, Yelin Jia, Kun School of Computer Science and Technology Tiangong University Tianjin300387 China College of Artificial Intelligence Tianjin University of Science and Technology Tianjin300457 China School of Mechanical and Electrical Engineering Xinjiang Institute of Engineering Xinjiang Wulumuqi830023 China The Open University of China Beijing100039 China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education Beijing100039 China Jingwei Textile Machinery Beijing00176 China

ISBN: (纸本)9781510680449

Many-objective optimization problems are challenging for the majority of extant multi-objective evolutionary algorithms to solve because they are unable to strike a balance between diversity and convergence in the high-dimensional objective space. Therefore, for many-objective evolutionary optimization, this paper proposes a vector angle-based and ISDE+ indicator selection strategies. Using these two strategies, the worst individual are repeatedly removed from the population through environmental selection. More specially, the first one is aimed at finding any two individuals that have the smallest vector angle, which indicates that they are looking in the same research direction. The latter considers the individuals with poor convergence and diversity, which are eliminated according to the value of ISDE+ indicator one by one. Experimental results revel that the proposed algorithm obtains competitive advantages on various benchmark problems with up to 10 objectives, when the proposed algorithm is compared with several state-of-the-art many-objective algorithms. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Vectors

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Optimizing Cloud Security with CNN and XGBoost Models for Intrusion Detection Systems

Optimizing Cloud Security with CNN and XGBoost Models for In...

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

作者： Amit Karbhari Mogal V. Anitha Preeti Nitin Bhatt Kriti Srivasatava S. Rukmani Devi Anvesh Perada Department of Computer Science and Application MVP Samaj's CMCS College Nashik India Department of Computer Science and Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Trichy India Department of Electronics & Communication Engineering IIMT College of Engineering Greater Noida India Department of Computer Science and Engineering (Data Science) Dwarkadas J Sanghvi College of Engineering Vile Parle India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai India Department of Electrical and Computer Engineering Drexel University Philadelphia United States of America

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Cloud computing and Internet growth have simplified certain formerly difficult activities. This advancement has also revealed many security weaknesses. Due to cyberattacks, organizations need Intrusion Detection Systems (IDS) to protect their data and networks. Preprocessing, feature selection, and model training comprise the suggested strategy. Preprocessing includes data cleansing, standardization, and labeling. Information gain, chi-square, and PSO are used to choose features. CNN-XGBoost trains the model. The CNN-XGBoost model outperforms solo CNN and XGBoost with an average accuracy of 92.08%. This accuracy proves the hybrid method's cloud computing breach detection usefulness. The study underlines the importance of enhanced IDS models in cyber risk reduction. CNN's feature extraction and XGBoost's classification power provide a powerful, efficient, and reliable cloud intrusion detection model. Modern organizations benefit from this network security method.

关键词： Training Cloud computing Accuracy Computational modeling Standards organizations Intrusion detection Standardization Feature extraction Reliability Integrated circuit modeling

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Utilizing Mixture Methods for Classifier in NLP: An Essential Consideration

Utilizing Mixture Methods for Classifier in NLP: An Essentia...

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Artificial Intelligence and Smart Communication (AISC), International Conference on

作者： Ms. Kavita Harveer Singh Department of Master of Computer Application Chandigarh School of Business Department of Computer Science Engineering Sanskriti University Mathura Uttar Pradesh India

The primary purpose of natural language program is to use it for desktop programming tool. This is very effective, and it can transform common biological conversation into a device which can be easily usable form. The robot may receive inputs in the form of data, voice, or visual. Currently, the machine earning is growing and finding usage in a variety of fields. The machine learning tool is developed by leading companies are having very big effect on the mobile and communication industry. Previous generations' daily tasks will be made easier by chat bots that are educated for all kind of issues which need to be discussed and or taken guidance. This well-known platform was mostly constructed using machine learning algorithms (ML) methods. In order to obtain the best outcomes and also the accuracy rate, the study intends to offer a principle component analysis gives the answer to a text categorization issue.

关键词： Visualization Machine learning algorithms Service robots Text categorization Natural languages Transforms Oral communication

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