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UUT: Unveiling Unknown Threats with machine learning for Exploit Detection

UUT: Unveiling Unknown Threats with Machine Learning for Exp...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Aadil Khan Ishu Sharma Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

In cybersecurity, attack detection indicates finding and countering efforts to take advantage of weak spots in computer systems using cutting edge technologies like AI and machine learning. Exploits are software or procedures that exploit vulnerabilities in computer systems, networks, or applications to compromise security. It's a tool or method used by attackers to exploit system flaws. software defects, configuration mistakes, and design weaknesses may be exploited. A successful exploit may provide unauthorized access, data alteration, or malicious code execution. AI quickly looks at how networks work, finds strange behavior, and spots possible security holes before they get worse. This proactive strategy enables firms to react to and decrease cyber hazards promptly, reducing their consequences and improving overall network security. Early cybersecurity vulnerability discovery is essential to minimize harm. Early exploit detection permits quick reaction, reducing cyberattack damage. Early detection helps firms fix vulnerabilities, protect sensitive data, and strengthen defenses, minimizing the chance of breaches and improving cybersecurity resilience. By trying out different machine learning methods for attack identification, this research article gives useful information. According to the results, the decision tree and XGBoost algorithms are the best at finding and reducing risks. Through its review and suggestions of strong attack detection methods, this research study helps to improve cybersecurity practices.

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Deep learning Models for software Defect Classification

Deep Learning Models for Software Defect Classification

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Device Intelligence, Computing and Communication Technologies, (DICCT), international conference on

作者： Kalpana Singh Alankrita Aggarwal Rattan Deep Aneja Rajender Kumar Punit Soni Apex Institute of Technology (CSE) Chandigarh University Mohali India Computer Science and Engineering Chitkara University Rajpura Punjab India

ISBN: (数字)9798350372847

ISBN: (纸本)9798350372854

With the increasing reliance on automatic software-based applications, it is critical to automate software defect classification and assure software dependability. This article proposes an expert-based automatic software flaw classification system. Defect report categorization is the process of categorizing defects into predefined groups. Many machine learning (ML) algorithms have recently been presented to categorize defect in various classes. This research proposes a classification model based on the Long Short Term Memory (LSTM) network and Convolution Neural Network (CNN) that uses these deep learning (DL) models to categorize defect reports by generating new word embedding from the defect reports. In terms of recall, accuracy and precision, the results are compared to pre-trained word embedding using Google's word2vec. According to the experimental results, LSTM beats the other models utilized in the study. On the redmine dataset, LSTM achieves a maximum accuracy of 70%.

关键词： Deep learning Performance evaluation machine learning algorithms Convolution Computational modeling Neural networks software algorithms

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Sentiment Analysis of Covid-19 Twitter Data using Deep learning Algorithm 2

Sentiment Analysis of Covid-19 Twitter Data using Deep Learn...

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2nd international conference on machine learning and Data engineering, ICMLDE 2023

作者： Valarmathi, B. Gupta, N. Srinivasa Karthick, V. Chellatamilan, T. Santhi, K. Chalicheemala, Dhanush Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Manufacturing School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Assistant Systems Engineer Tata Consultancy Services Chennai India Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

A key method used in the study of Natural Language Processing (NLP) is sentiment analysis, or emotion analysis, plays a pivotal role in text analysis. Its primary function is to discern and categorize the underlying emotions within textual content, classifying them as positive, neutral, or negative sentiments. A variety of textual inputs can be used with this process, including entire documents, individual sentences, and more. Understanding the sentiments expressed by individuals is of paramount importance to organizations in today's digital age. Clients and customers now have the means to express their thoughts and emotions with greater ease and immediacy than ever before. This wealth of sentiment data can offer valuable insights that organizations can leverage to enhance their products, services, and overall customer experience. The present study focuses on the sentiment analysis of Twitter data related to the COVID-19 pandemic, employing the Long Short-Term Memory (LSTM) algorithm. To improve the accuracy of sentiment analysis, a pre-processing procedure is introduced, which involves the use of the Neat Text module in Python to clean the tweets. In this research endeavor, a dataset comprising 1,79,107 COVID-19-related tweets is subjected to sentiment analysis using the proposed pre-processing module and LSTM. The results demonstrate an impressive accuracy rate of 96% in accurately determining the sentiments conveyed in these COVID-19 tweets. In contrast, the existing algorithm, based on Artificial Neural Network (ANN), achieved a significantly lower accuracy rate of 76%. This research not only showcases the effectiveness of LSTM and pre-processing in sentiment analysis but also highlights the significance of sentiment analysis in gaining valuable insights from vast amounts of textual data, especially when important events like the COVID-19 pandemic are involved. © 2024 Elsevier B.V.. All rights reserved.

关键词： COVID-19

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software Bug Report Detection Methods Based on machine learning Techniques

Software Bug Report Detection Methods Based on Machine Learn...

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Advancement in Computation & Computer Technologies (InCACCT), international conference on

作者： Ulligaddala Srinivasarao Akhil Chaganti Ajay Mannam Dhanush Kotrikeline Hemanth Katta Department of Computer Science and Engineering GITAM (Deemed to be) University Hyderabad India Department of Computer Science and Engineering and Hyderabad India GITAM (Deemed to be) University Hyderabad India

ISBN: (数字)9798350371314

ISBN: (纸本)9798350371321

software defect prediction, an integral facet of software engineering, proactively identifies and resolves potential flaws before they escalate into disruptive issues during production. Insights gleaned from dedicated research serve as solid support for development teams, pinpointing vulnerable areas within their codebase and optimizing resource allocation. This strategic approach significantly reduces overall expenditure on defect rectification. This paper conducts a comprehensive study, meticulously examining various machine learning algorithms in this domain. It intricately dissects deployment, functionality, and the comparative effectiveness of models like Logistic Regression, K-Nearest Neighbours, Naive-Bayes Classifier, Random Forest Classifier, and Recurrent Neural Networks. This thorough exploration enhances understanding of defect prediction methodologies. It provides a detailed roadmap for optimal strategies, reinforcing software quality measures and mitigating the impact of potential coding irregularities on system integrity and reliability. According to the experimental findings, RNN outperformed the baseline methods in terms of accuracy, F1 score, recall, and precision when identifying software bug reports.

关键词： Logistic regression Recurrent neural networks machine learning algorithms Computer bugs Prediction algorithms Approximation algorithms Solids

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Investigating the Transferability of Evasion Attacks in Network Intrusion Detection Systems Considering Domain-Specific Constraints 1

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25th international conference on Information Security Applications, WISA 2024

作者： Mbow, Mariama Roman, Rodrigo Seal, Ayan Wang, Kevin I-Kai Mohanty, Sraban Kumar Sakurai, Kouichi Department of Information Science and Technology Kyushu University Fukuoka Japan Lab University of Malaga Málaga Spain PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur482005 India Department of Electrical Computer and Software Engineering The University of Auckland Auckland1010 New Zealand

ISBN: (数字)9789819616244

ISBN: (纸本)9789819616237

Recently, adversarial attacks against machine learning-based network intrusion detection systems (NIDS) have gained significant attention in cybersecurity. This study investigates the transferability of these adversarial attacks in the context of NIDS. In addition, most existing studies in adversarial learning against NIDS adopted attack strategies designed originally for image classification without considering network traffic characteristics. However, this is impractical and will fail in the real world as network traffic features are constrained, and ignoring the functional behavior of the network traffic features leads to invalid network traffic flow or produces adversarial samples that do not retain their original functionality (malicious or benign). To address these issues, we propose a constrained momentum iterative fast gradient sign method (C-MIFGSM) to generate adversarial network flows that can successfully evade an ML-based IDS through transfer-based attacks while preserving the functional behavior of the network traffic. Our approach was validated using several target NIDS models built with the NSLKDD benchmark dataset. Experimental results demonstrate that even without knowledge of the target model and under feature constraints, it is possible to generate adversarial network traffic flows that achieve a high evasion attack success rate against NIDS built with deep learning and classical ML models. For example, the attack degraded the detection rate of the DoS traffic drops from 92.35% to 20.27% for the MLP model with an evasion increase rate of 78.04%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Intelligent Data Mining of Hyper Spectral Images for Feature Extraction

Intelligent Data Mining of Hyper Spectral Images for Feature...

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Artificial Intelligence and machine learning Applications Theme: Healthcare and Internet of Things (AIMLA), international conference on

作者： Jyoti Seth Varalakshmi S Sneha Kashyap Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Management School of Mangement - UG JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science & IT ARKA JAIN University Jamshedpur Jharkhand India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

This technical abstract describes the software of wise information mining strategies for the analysis of hyper-spectral imaging for the purpose of extracting underlying features. Specifically, the usage of supervised and unsupervised device studying algorithms is explored, which include clustering, choice trees, help vector machines and artificial neural networks. The techniques are in comparison on the way to optimize consequences for diverse consequences, including endmember extraction and target detection. A singular technique was additionally proposed for enhancing class accuracy and lowering fake detections through the use of winger correction. The paper outcomes reveal the capability of smart information mining for hyper-spectral picture evaluation in quite a number of applications.

关键词： software algorithms Object detection Medical services machine learning Feature extraction Vectors software

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Currency Pair Trading Simulator Using ML Models

Currency Pair Trading Simulator Using ML Models

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Sustainable Computing and Smart Systems (ICSCSS), international conference on

作者： Snehal Bhamodre Pushpi Rani Department of Computer Engineering G H Raisoni College of Engineering and Management Pune India GRCEM Pune India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

The Currency Pair Trading Simulator is a software project designed to offer traders a realistic and risk-free environment for refining their currency pair trading strategies. Leveraging advanced technologies like SQLite, MongoDB, and pandas, the simulator utilizes historical price data, order execution algorithms, and machine learning models to create an accurate trading experience. Traders can experiment with different approaches, analyze historical data, and make data-driven decisions without risking real capital. Additionally, machine learning algorithms like XGBoost Regression help predict price movements and assess the effectiveness of trading strategies. This simulator offers several key advantages. It allows traders to practice and improve their trading strategies without the fear of financial losses. Traders can also evaluate the accuracy of prediction models and make necessary adjustments to their strategies. Furthermore, this tool is valuable for both novice and experienced traders, as well as financial and educational institutions, enabling them to enhance trading skills, conduct research, and improve risk management practices.

关键词： machine learning algorithms Accuracy Computational modeling software algorithms Refining Predictive models software

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Flood Prediction Map Using QGIS and ML-Case Study for Barpeta District Assam

Flood Prediction Map Using QGIS and ML-Case Study for Barpet...

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Emerging Trends in engineering and Medical Sciences (ICETEMS), international conference on

作者： Lalit Damahe Sagar Lachure Kush Thakare Vaishnav Dekate Vedant Shivde Sanskruti Gedam Department of CSE YeshwantraoChavan College of Engineering Nagpur India Department of IT VeermataJijabai Technological Institute Mumbai India

ISBN: (数字)9798331529352

ISBN: (纸本)9798331529390

Across the world, floods pose a serious threat to populations, hence precise forecasting techniques required for efficient relief. The objective of this research is to forecast floods in the Barpeta district of Assam, India, by combining machine learning approaches with Geographic Information Systems (GIS). Through the integration of many geographic datasets, such as river networks, administrative boundaries, and Digital Elevation Models (DEMs), in conjunction with rainfall data and a 2022 flood hazard map, the study creates prediction models to identify flood-prone regions and assess flood risk levels. Utilizing the XGBoost and GBM algorithm, the study assesses model performance and visualizes results using GIS software. The results offer significant perspectives to decision-makers and interested parties, supporting the development of plans to lessen the effects of flooding in areas that are susceptible to flooding. Through a data-driven approach, this research highlight the potential of integrating GIS and machine learning techniques to address complex environmental challenges and promote resilience in flood-prone areas.

关键词： Deep learning Accuracy Satellites software algorithms software Rivers Floods Forecasting Geographic information systems Resilience

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machine learning Driven Load Balancing in software Defined Networks: Recent Progress and Emerging Challenges

Machine Learning Driven Load Balancing in Software Defined N...

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Advancements and Key Challenges in Green Energy and Computing (AKGEC), international conference on

作者： Sonam Sharma Rajendra Prasad Mahapatra Manoj Kapil Dambarudhar Seth Department of Computer Science Engineering SRM Institute of Science and Technology Delhi-NCR Campus Ghaziabad India Faculty of Engineering and Technology Swami Vivekananda Subharti University NH-58 Delhi-Haridwar Bypass Road India

ISBN: (数字)9798350390179

ISBN: (纸本)9798350390186

Load balancing has arisen as an important challenge in case of software Defined Network because of the dynamic patterns in the traffic and expanding demands of network. The centralized framework of SDN delivers unique opportunity. It enhances the performance of network and usage of resources by distributing the load of network dynamically across multiple devices and paths. This paper provides a concise overview of SDN and examines the influence of load balancing within this context. It includes a comprehensive study to analyse the algorithms of load balancing used by researchers for different scenarios and to identify the persistent challenges. Furthermore, a methodology has been proposed to highlight the impact of machine learning techniques to amplify the load balancing decision which can be useful to develop more intelligent and adaptive architectures of SDN. For future research, the study aims to deploy scalable and robust SDN solutions in next-generation network infrastructures.

关键词： Heuristic algorithms Scalability software algorithms machine learning Computer architecture Traffic control Load management Resource management software defined networking Next generation networking

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Improving Diagnostics for Brain Cancer: An Application of Deep learning

Improving Diagnostics for Brain Cancer: An Application of De...

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Artificial Intelligence and machine learning Applications Theme: Healthcare and Internet of Things (AIMLA), international conference on

作者： Neeraj Sharma Umakanth S Alok Kumar Moharana Department of Electrical Engineering Vivekananda Global University Jaipur India Department of Management School of Mangement - UG JAIN (Deemed to be University) Bangalore Karnataka India Department of Pharmacy ARKA JAIN University Jamshedpur Jharkhand India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

The software of deep mastering to improve diagnostics for mind cancer has the potential to master healthcare and scientific imaging. This painting provides the improvement and evaluation of a deep getting-to-know-based system for the type of brain cancer Magnetic Resonance Imaging (MRI) scans of actively treated patients. The version combines deep convolution neural networks (CNN) and switches learning for you to identify and classify brain tumours appropriately. The gadget was tested on a large dataset of 204 MRI scans and executed with a normal accuracy of 91.67%, a sensitivity of ninety 60.95%, and a specificity of 87%. The results exhibit the effectiveness of gaining knowledge for the diagnosis of most brain cancers and its potential position in improving medical outcomes.

关键词： Deep learning Sensitivity Magnetic resonance imaging Brain cancer Medical services Sensitivity and specificity software

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