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An Exploration of machine learning Approaches in the Field of Cybersecurity

An Exploration of Machine Learning Approaches in the Field o...

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international conference on. Cryptology and Network Security with machine learning, ICCNSML 2023

作者： Khare, Brajesh Kumar Khan, Imran Department of Computer Science and Engineering Harcourt Butler Technical University Uttar Pradesh Kanpur India

ISBN: (纸本)9789819706402

The extensive and growing utilization of the Internet and mobile apps has resulted in the enlargement of the online realm, rendering it more vulnerable to extended and automated cyber assaults. In response to this heightened vulnerability, cybersecurity techniques have been developed to strengthen security measures and improve the ability to detect and respond to cyberattacks. Due to the intelligence of cybercriminals in evading traditional security systems, the previously employed security measures have become inadequate. Conventional security systems struggle to effectively detect new and ever-changing security attacks that are previously unseen or have varying forms. ML methods are making substantial contributions to different aspects of cybersecurity, playing a pivotal role in numerous applications within the discipline. While ML systems have been successful so far, there are considerable obstacles in ensuring their trustworthiness. This paper’s main objective is to offer a thorough examination of the obstacles ML techniques encounter in safeguarding cyberspace from attacks. This is accomplished by examining the existing body of literature concerning ML techniques utilized in the field of cybersecurity. These techniques encompass areas such as intrusion detection, spam detection, and malware detection within computer and mobile networks. The document also provides succinct elucidations of each specific machine learning approach, indispensable machine learning tools, ML involvement in cybersecurity, and current state of ML for cybersecurity. Finally, the paper examines the barriers and challenges, as well as the anticipated path for the future of ML in the context of cybersecurity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： machine learning

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Malware feature selection and adversarial sample generation method based on reinforcement learning 4

Malware feature selection and adversarial sample generation ...

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4th international conference on Telecommunications, Optics, and Computer Science, TOCS 2023

作者： Li, Xiaolong Yan, Zhenhua Zhang, Shuang Li, Xinghua Wang, Feng Power Science Research Institute of State Grid Ningxia Electric Power Co. LTD Ningxia Yinchuan China

ISBN: (数字)9781510679849

ISBN: (纸本)9781510679832

Amidst the intricate battleground where attackers and defenders are perpetually locked in a sophisticated digital cat-and-mouse game, the task of discerning malware within an adversarial crafted environment manifests escalating complexity. The current research delineates the utilization of an enhanced reinforcement learning technique, orchestrated to engender adversarial malware specimens, thereby strategically navigating through machine learning detectors. This endeavor not only bolsters the robustness of malware identification systems but also adeptly navigates the perpetually evolving machinations of malware creators. Within this research, an environmental model is meticulously constructed, emulating detection engines and feature extractors, with malware samples assimilated as input. By integrating an autonomously generated reward function, we ascertain the model’s agility and the concomitant generation of manifold adversarial malevolent samples. The empirical evaluations underscore that, in contrast to conventional machine learning approaches, our methodology exudes superior flexibility and efficacy, furnishing a more formidable challenge to malware detection mechanisms. © 2024 SPIE.

关键词： Reinforcement learning

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Fraud Detection in Credit Card Using Competitive Swarm Optimization with Support Vector machine 2

Fraud Detection in Credit Card Using Competitive Swarm Optim...

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2nd international conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Gajakosh, Ashwini Reddy, R. Archana Mundher Adnan, Myasar Rajalaxmi, G. Pramodhini, R. Department of Csit Bengaluru India School of Computer Science and Engineering Sr University Telangana Warangal India The Islamic University Najaf Iraq MallaReddy Engineering College for Women Dept of Eee Telangana India Nitte Meenakshi Institute of Technology Dept of Ece Bengaluru India

ISBN: (纸本)9798350382952

The characteristics of frauds are unstable, and they require unsupervised learning since they are always changing the way they behave. Scammers can conduct online transactions by using the most recent technologies available to them. Fraudsters assume the habits of their victims, and their schemes rapidly gain traction. Although some criminals use online channels first before moving on to other techniques, fraud detection systems must apply unsupervised learning to identify online payments. Particularly, focus on fraud scenarios that cannot be detected with previous records or supervised learning. This work aims to create a support vector machine model (SVM) to detect anomalies from regular pattern produced by competitive swarm optimisation (CSO). Therefore, CSO-SVM method is implemented based on unsupervised learning utilizes for effectively detect credit card fraud. Implemented CSO-SVM method obtains high performance when compared to existing methods including Competitive swarm optimization and Deep convolutional neural network (CSO-DCNN), dilated CNN (DCNN), and coarse K-neural network (KNN), and result achieved value of 99.88% accuracy. © 2024 IEEE.

关键词： Unsupervised learning

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Applying machine learning Techniques to Time Series Analysis for Early Detection of Thymoma

Applying Machine Learning Techniques to Time Series Analysis...

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2024 international conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Nachappa, M.N. Goswami, Sachin Rastogi, Shreshta Bandhu Department of Computer Science and Information Technology Bangalore India Sanskriti University Department of Management Uttar Pradesh Mathura India College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

Thymoma is an aggressive and frequently deadly most cancers. Early detection of thymoma can enhance prognosis and survival. Latest advances in device learning techniques have enabled the development of correct and efficient answers for detecting and monitoring illnesses. In this examination, we check out the capacity of gadget learning methods for early detection of thymoma using a time collection analysis technique. We evaluated the overall performance of different algorithms, consisting of deep getting-to-know fashions, Recurrent Neural Networks, and other machine learning strategies, including guide Vector machines. Furthermore, we compared the outcomes with those of conventional methods to assess the proposed technique's accuracy and velocity. Consequences showed that a system gaining knowledge of techniques provided higher accuracy and speed than conventional techniques. Our work provides a novel technique in thymoma detection, and this approach can be helpful for clinics inside the early diagnosis of thymoma. The utility of machine mastering strategies to time collection analysis for early detection of thymoma is an essential research place. Thymoma is a cancer of the thymus gland responsible for regulating the immune device. Time collection analytics allow for the identity of dynamic styles in the records, which in turn can offer beneficial insight to help become aware of and are expecting the early onset of thymoma. as an instance, machine-gaining knowledge of algorithms can be used to discover changes in cell conduct over time or to hit upon patterns within the temporal development of the ailment. Moreover, machine mastering algorithms can be used to develop predictive fashions and use the identified patterns to offer early detection of thymoma. Those predictive models allow clinicians to identify high-threat people and better manipulate their treatments. Therefore, the software of device learning techniques for time collection analysis is a practical resou

关键词： Recurrent neural networks

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Autism Insight: Harnessing PySpark for Early Spectrum Detection 1

Autism Insight: Harnessing PySpark for Early Spectrum Detect...

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1st international conference on Software, systems and Information Technology, SSITCON 2024

作者： Syam Dev, R.S. Sumanthraj, P. Nishanth, V. Dinesh Kumar, S. Jashwanth, S. Prajwal, T.S. New Horizon College of Engineering Department of Aiml Bengaluru India

ISBN: (纸本)9798350352931

Autism Spectrum Disorder (ASD) early detection is crucial for effective intervention, but traditional diagnostic methods struggle with complex, voluminous data. This study explores PySpark's potential in ASD prediction, leveraging its distributed computing capabilities for efficient large-scale data processing. We implemented and compared multiple machine learning models-Logistic Regression, Random Forest, Decision Tree, and Support Vector machine (SVM)-using PySpark's MLlib. The models were evaluated on a dataset of 600 entries with 16 behavioral features. Results showed that SVM outperformed other models, achieving 96.5% accuracy and 9 7. 6 %F1 score. Random Forest followed with 9 0. 8 %accuracy and 93.8 %F1 score. Decision Tree and Logistic Regression achieved 84.4 %and 73 %accuracy, respectively. This research demonstrates PySpark's ability to enhance both speed and accuracy in ASD prediction, particularly with extensive datasets. The findings contribute to advancing big data analytics in healthcare, offering potential improvements in early ASD detection and intervention strategies. © 2024 IEEE.

关键词： Adversarial machine learning

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Railway track vertical irregularities classification based on vehicle response using machine learning method 3

Railway track vertical irregularities classification based o...

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3rd international Symposium on advances and Innovation in Mechanical engineering, ISAIME 2022

作者： Wikaranadhi, P. Alfian, S.D. Handoko, Y.A. Palar, P.S. Mechanical Design Research Group Institut Teknologi Bandung Bandung40132 Indonesia Flight Physics Research Group Institut Teknologi Bandung Bandung40132 Indonesia

The common method of railway track irregularities assessment currently used, based on track geometry measurement data, is expensive to run, disrupts regular train services, and shows poor correlation with the actual vehicle response when running on a given track section. Vehicle response-based track irregularities assessment methods aim to overcome the shortcomings of geometry measurement-based methods. One of the possible methods is to establish the correlation between track geometry and vehicle response statistically using machine learning. Classification algorithms have been used in previous research for categorizing track sections into discrete classes. In this paper, in addition to discrete classification, the probability for which a track section is categorized as a certain class is presented. Testing results of the trained machine learning classification model showed high accuracy, precision, and recall. The results indicated that this method is suitable for classification of the vertical track irregularities. Further testing was performed with shifted classification threshold. The finding showed that the classification model's precision and recall could be adjusted by shifting the classification threshold. The result of this study highlights the potential of machine learning classification usage for track irregularities assessment. © Published under licence by IOP Publishing Ltd.

关键词： machine learning

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Building a Model with AutoML in machine Faults Detection 23th

Building a Model with AutoML in Machine Faults Detection

引用

23rd international conference on Intelligent systems Design and Applications, ISDA 2023

作者： Duymaz, Şeyma Department of Industrial Engineering Faculty of Engineering and Natural Sciences Istanbul Rumeli University Istanbul Turkey

ISBN: (纸本)9783031647758

Technological advancements have provided numerous benefits for production activities. The primary aim of companies is to minimize costs and avoid unforeseen expenses. machine failures may result in excessive and unexpected costs for firms. As a result, it is of paramount importance to anticipate and prevent machine breakdowns in advance. Predictive maintenance evaluations can be valuable in recognizing equipment and machine failures. The objective of this study is to utilize automated machine learning (AutoML) in order to achieve optimal outcomes via the adjustment of parameters on machine learning (ML) algorithms aimed at detecting machine faults. The dataset comprises 77,004 rows and 13 features extracted from Google data center operators. Five different classes of machine operating status are identified, namely: evict, fail, finish, kill and lost. Among these, fail, finish and evict classes are deemed failure states. AutoML can be accessed through various libraries. This study specifically uses the Auto-Keras, Auto-Sklearn and Lazy predict libraries, all of which are open-source Python libraries. In this study, hyperparameter optimization was conducted on the random forest (RF) algorithm, which was the third best algorithm obtained through the Lazy Predict library. The code was written using Anaconda. It is important to note that while the results of these libraries are valuable, manual modifications may be necessary for optimal outcomes. Notably, the Decision Tree Classifier algorithm achieved an accuracy of 97%, which was the highest among the evaluated options. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： machine learning

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INTEGRATIVE machine learning APPROACHES FOR ENHANCED CLASSIFICATION OF GENOMIC SEQUENCES: A NEXT-GENERATION SEQUENCING PERSPECTIVE 45

INTEGRATIVE MACHINE LEARNING APPROACHES FOR ENHANCED CLASSIF...

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2024 international Annual conference of the American Society for engineering Management and 45th Annual Meeting: engineering Management Riding the Waves of Smart systems, ASEM 2024

作者： Alla, Sujatha Bheesetty, Nagesh Komaragiri, Sai Gireesh Chidipudi, Prasanthi Mohanty, Joshit Chintala, Sathish Kumar Thomas, Jubin Vummadi, Jayapal Volikatla, Hemanth Kamuni, Navin Old Dominion University United States

ISBN: (纸本)9798985333480

The advent of Next-Generation Sequencing (NGS) techniques has revolutionized genomic research by enabling the rapid sequencing of DNA and RNA. This data can be used for various applications, including genome sequencing, transcriptome profiling, metagenomics, and epigenetics studies. For this study, DNA classifier dataset was extracted from UCI repository of machine learning databases. This vast amount of genomic data necessitates the development of sophisticated machine learning (ML) models for effective classification and analysis. This study presents a comprehensive comparison of various ML models, including Support Vector machines (SVM), Random Forests (RF), and Neural Networks (NNs), approaches, in classifying genomic data. We evaluate these models based on accuracy, computational efficiency, and their ability to handle high-dimensional data. The comparison reveals distinct strengths and limitations of each model, with NNs and DL approaches showing superior performance in handling complex patterns in genomic sequences but requiring significant computational resources. Conversely, SVM and RF offer a balance between accuracy and computational demand, making them suitable for applications with limited computational resources. A notable research gap identified is the challenge of integrating diverse genomic data types (e.g., single nucleotide polymorphisms, copy number variations, and gene expression data) in a unified classification model. This gap underscores the need for developing advanced ML models that can effectively leverage the heterogeneous nature of genomic data to improve classification outcomes. This study paves the way for future research aimed at bridging this gap, which is critical for advancing personalized medicine and understanding complex genetic disorders. Copyright © American Society for engineering Management, 2024.

关键词： Gene encoding

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Stock Price Forecasting by Time Series Analysis

Stock Price Forecasting by Time Series Analysis

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2024 international conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Surekha, Reshma Gupta, Divya Jha, Adarsh Kumar Bhargavi, Maridu Sai, Poorna Department of Computer Science and Engineering Vignan's Foundation for Science Technology and Research Guntur India

ISBN: (纸本)9798331543617

The technological advancement and mountains of financial data now available, more than ever it is crucial for investors, financial institutions, and decision-makers to make an informed choice by obtaining accurate stock price forecasting. Traditional techniques of forecasting often fail in today's complex market dynamics. This paper reveals a number of techniques of machine learning that can be applied to the field of stock price forecasting, de-emphasizing building up the accuracy and relia- bility of such predictions.[2] The main key algorithms presented are ElasticNet for feature selection and XGBoost, CatBoost, and an ensemble Voting Regressor. Its ability to analyze series data and identify strong, non-trivial patterns from the time series of stock price data made this model the best choice. Results show a performance that indicates just how well the ensemble Voting Regressor performed over the rest, with accuracy 99.6% far beyond any model from the bunch, promising much sharper resolution in financial forecasting. XGBoost and CatBoost also had very comparable accuracy that assured the productive use of machine learning in applying it for financial analytics. The research is not only a glorification of benefits from application of developed models of machine learning for predicting stock prices but also introduces points for further refinement of methods of prediction[4]. © 2024 IEEE.

关键词： Prediction models

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Automated Convict Identification Through machine learning-Based Facial Recognition

Automated Convict Identification Through Machine Learning-Ba...

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2024 IEEE international conference on Computing, Applications and systems, COMPAS 2024

作者： Zubary, Nadim N. Khondoker, Shanzida Ferdows, Rubayea Ahmed, Fuad IUBAT-International University of Business Agriculture and Technology Dept of Computer Science and Engineering Bangladesh Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798331529765

In a world riddled with criminal incidents, the swift identification of wrongdoers remains an ongoing challenge. Criminals have become adept at erasing biological and fingerprint traces from crime scenes, rendering traditional investigative methods ineffective. Modern facial identification technologies offer a rapid solution to this problem. With the widespread use of CCTV cameras, driven by security technology advancements, a unique opportunity arises. Our research seeks to automatically detect and identify convicts from video footage, providing a powerful tool for law enforcement. This technology extends to identifying suspects, criminals, missing persons, and more, saving valuable time and effort. Our study focuses on developing a machine learning-based criminal face detection system to streamline law enforcement by automating criminal identification. Employing a mixed methods approach involving data collection and focus group discussions, we gain insights from field practitioners. Our survey reveals a significant challenge in identifying culprits, with nearly 60% of cases analyzed yielding unidentified perpetrators. We propose a novel machine learning based automated method, which integrates with a comprehensive database containing individuals' information and diverse images. Once identified, the system conducts real-time database comparisons, providing immediate results when a match is found. We explore techniques for criminal identification from photographs and introduce innovative ideas. Our research comprehensively covers various models, offering a promising solution to the challenges of locating and identifying convicts in modern law enforcement. © 2024 IEEE.

关键词： Crime

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