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Systematic Security Guideline Framework through Intelligently Automated Vulnerability Analysis

computers, Materials & Continua 2024年第3期78卷 3867-3889页

作者： Dahyeon Kim Namgi Kim Junho Ahn Computer Information Technology Korea National University of TransportationChungju27469Korea Department of AI Computer Science and Engineering Kyonggi UniversitySuwon16227Korea

This research aims to propose a practical framework designed for the automatic analysis of a product’s comprehensive functionality and security vulnerabilities,generating applicable guidelines based on real-world *** existing analysis of software security vulnerabilities often focuses on specific features or *** partial and arbitrary analysis of the security vulnerabilities makes it challenging to comprehend the overall security vulnerabilities of the *** key novelty lies in overcoming the constraints of partial *** proposed framework utilizes data from various sources to create a comprehensive functionality profile,facilitating the derivation of real-world security *** guidelines are dynamically generated by associating functional security vulnerabilities with the latest Common Vulnerabilities and Exposure(CVE)and Common Vulnerability Scoring System(CVSS)scores,resulting in automated guidelines tailored to each *** guidelines are not only practical but also applicable in real-world software,allowing for prioritized security *** proposed framework is applied to virtual private network(VPN)software,wherein a validated Level 2 data flow diagram is generated using the Spoofing,Tampering,Repudiation,Information Disclosure,Denial of Service,and Elevation of privilege(STRIDE)technique with references to various papers and examples from related *** analysis resulted in the identification of a total of 121 *** successful implementation and validation demonstrate the framework’s efficacy in generating customized guidelines for entire systems,subsystems,and selected modules.

关键词： Framework automation vulnerability analysis security guidelines

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Context-Aware Summarization for PDF Documents using Large Language Models 4

Context-Aware Summarization for PDF Documents using Large La...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Ramprasad, Akshara Sivakumar, P. School of Computer Science and Engineering Vellore Institute of Technology B. Tech Computer Science with specialization in AI & ML Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350385793

In an era characterized by the overflow of textual information, the demand for effective text summarization techniques has become increasingly evident. This research study presents a novel solution to address this demand: the development of a PDF Summarizer Generative ai Platform for context-aware text and document summarization. The developed platform empowers users to generate informative summaries from lengthy textual documents. It features an interface powered by Gradio for enabling user-friendly accessibility. Equipped with essential components such as Openai's language models for text comprehension, Chroma vector enables document embedding and conversational retrieval chains for contextual understanding, where the platform offers a comprehensive solution. Users can effortlessly upload documents in various formats, including PDFs, and interactively engage with the system to obtain concise summaries tailored to their specific requirements. © 2024 IEEE.

关键词： Chroma Database (ChromaDB) Gradio LangChain Large Language Models (LLM) Openai

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Guest Editorial: New Developments in Explainable and Interpretable Artificial Intelligence

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1427-1428页

作者： Subbalakshmi, K. P. Suba Samek, Wojciech Hu, Xia Ben Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States Fraunhofer HHI Einsteinufer AI Department Berlin10587 Germany ML & Communications Berlin10587 Germany Rice University Computer Science HoustonTX77005 United States

This special issue brings together seven articles that address different aspects of explainable and interpretable artificial intelligence (ai). Over the years, machine learning (ml) and ai models have posted strong performance across several tasks. This has sparked interest in deploying these methods in critical applications like health and finance. However, to be deployable in the field, ml and ai models must be trustworthy. Explainable and interpretable ai are two areas of research that have become increasingly important to ensure trustworthiness and hence deployability of advanced ai and ml methods. Interpretable ai are models that obey some domain-specific constraints so that they are better understandable by humans. In essence, they are not black-box models. On the other hand, explainable ai refers to models and methods that are typically used to explain another black-box model. © 2020 IEEE.

关键词： Artificial intelligence

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Analysis of Various Motion Detection and Facial Recognition Based Home Security Systems 2nd

Analysis of Various Motion Detection and Facial Recognition ...

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2nd International Conference on Deep sciences for Computing and Communications, IconDeepCom 2023

作者： Karthikeyan, P. Nidhi, Shashank Vishnu Kumar, S.R. Karunakaran, V. Velliangiri, S. Department of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan Department of Computer Science and Engineering-AI and ML Jain Deemed to be University Bangalore India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9783031689079

A combination of ai and IoT makes a great home security system. IoT collects data from the surroundings, while ai takes the data and makes predictions accordingly. In this case, these predictions help in creating alerts. CCTV cameras were traditionally used for home security systems, with attendees required to monitor the footage. Because of technological advancements, this process can be completed with the least amount of human intervention. Other work has been done in the field of home security systems that use facial recognition and motion detection. Those systems don’t exhibit good accuracy in detecting multiple human faces. This work focuses on a detailed survey of different facial recognition techniques along with motion detection techniques as per the requirements of a home security system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Motion tracking

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Agricultural repercussions caused by the pandemic period

Agricultural repercussions caused by the pandemic period

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Sheeja Kumari, V. Seema Dev Aksatha, D. Michael, G. Vennira Selvi, G. Manikandan, T. Gururama Senthilvel, P. Department of Computational Intelligence – Institute of AI & ML SIMATS School of Engineering SIMATS University Chennai India Department of Computer Science Sri Krishna Arts and Science College Coimbatore India Department of Computer Science and Engineering SIMATS School of Engineering SIMATS University Chennai India

ISBN: (纸本)9781032669663

COVID-19 has affected India’s agriculture system. However, the latest quarterly GDP predictions post-COVID scenario show the resilience of the Indian agricultural sector, which is expected to be the sole sector to expand 4% for fiscal years. In this context, the major purpose is to assemble a summary of early evidence on COVID-19‘s influence on India’s agriculture industry, including production, marketing, and consumption, and then devise pragmatic measures to recover and thrive after the pandemic. The epidemic has disrupted production and marketing through labour and logistical restrictions, while the negative income shock has restricted access to markets and increased food commodity costs, changing consumption patterns, the poll found. India’s agriculture business was devastated by the outbreak. The crisis has allowed the state to launch many programmes and make important changes. After COVID-19, coordinated effort is needed to break the global stalemate. This review study shows how the COVID-19 epidemic has damaged global. © 2024, CRC Press/Balkema. All rights reserved.

关键词： COVID-19

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A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

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computers, Materials & Continua 2024年第3期78卷 3089-3127页

作者： Navaneetha Krishnan Muthunambu Senthil Prabakaran Balasubramanian Prabhu Kavin Kishore Senthil Siruvangur Kavitha Chinnadurai Jehad Ali Department of Computer Applications Karpagam College of EngineeringCoimbatore641032India Department of Computer Science and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Data Science and Business Systems College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyJeppiaar NagarRajiv Gandhi SalaiChennai600119India Department of AI Convergence Network Ajou UniversitySuwon16499Korea

The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the ***,this development has expanded the potential targets that hackers might *** adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or *** identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious *** research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks(RNN)integrated with Long Short-Term Memory(LSTM)*** proposed model can identify various types of cyberattacks,including conventional and distinctive *** networks,a specific kind of feedforward neural networks,possess an intrinsic memory *** Neural Networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended *** such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual *** are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection *** model utilises Recurrent Neural Networks,specifically exploiting LSTM *** proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.

关键词： Cybersecurity intrusion detection machine learning leveraging long short-term memory(LLSTM) CICIDS2019 dataset innovative cyberattacks

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A novel WO-ANT: whale-ant optimization algorithm for detection of Parkinson’s disease

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1873-1881页

作者： Singh, Neha Sinha, Sapna Singh, Laxman Amity Institute of Information Technology Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering (AI-ML) KIET Group of Institutions U.P Ghaziabad India

Parkinson’s disease (PD) is a neurological condition that impacts the quality of life for millions of people all over the world. A prompt and precise diagnosis of PD is absolutely necessary for the successful treatment and management of the condition. For the detection of PD, numerous methods exist in the literature, including speech-based methods, gait-based methods, and handwriting-based methods. Compared to the other two methods, the speech-based method is regarded as the most effective and competitive. The Whale-Ant Optimization Algorithm (WO-ANT) is a novel swarm optimization strategy that is presented in this paper as a potential method for the detection of PD via feature selection. To help in early detection, the proposed method attempts to enhance classification accuracy of PD detection by selecting an optimum subset of features from a wide feature space. In this paper, authors evaluate the classification efficacy of WO-ANT for PD using a speech-based method. MATLAB is used to implement the proposed method on the PD dataset obtained from ***. The efficacy of the algorithm is evaluated based on the optimal subset of features, accuracy, and computational cost. WO-ANT accurately reduced the number of features from 23 to 4 giving an accuracy of 90.29%. According to the numerical results, the algorithm performed well in terms of accuracy and selection of the optimal number of features. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Ant colony optimization algorithm Feature selection Parkinson’s disease Whale optimization algorithm Whale-ant optimization algorithm

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Climate Geoscience–Based Disaster Management in Healthcare Analysis by Markov Q-Transfer Adversarial with Cloud Computing

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 474-483页

作者： Srinivas, Jhade Rebecca, B. Bhargavi, Kovvuri N Swetha, Velma Reddy Spandana, S. Degala, Divya Priya Department of Computer Science and Engineering-Data Science KG Reddy College of Engineering and Technology Moinabad Telangana Hyderabad India Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Telangana Dundigal 500043 India Department of CSE Aditya University AP Surampalem India Department of Computer Science and Engineering (AI-ML) Malla Reddy Engineering College Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Bowrampet Telangana Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Analysis and reaction to natural disasters have made extensive use of deep learning methods using semantic segmentation networks. These implementations’ foundation is based on convolutional neural networks (CNNs), which are capable of precisely identifying and locating the relevant areas of interest within satellite imagery or other types of remote sensing data. This helps with rescue planning, restoration efforts, and disaster evaluation. Difficulties in the provision of healthcare services, due to natural disasters, are made worse by the existing problems prevalent in most healthcare systems. The aim of this research is to propose a novel technique in climate change–based disaster management with their public healthcare analysis using cloud computing with deep learning techniques. In this research, the cloud network has been used in LiDAR image collection for climate change–based disaster-struck regions and their public healthcare analysis. This collected image has been classified and optimized using a deep Markov Q-transfer adversarial neural network with binary grey whale swarm optimization. Experimental analysis has been carried out for various disaster management–based public healthcare datasets in terms of classification accuracy, specificity, NSE, sensitivity, and F-1 score. The proposed technique attained a classification accuracy of 97%, F-1 score of 94%, specificity of 96%, sensitivity of 95%, and NSE of 57%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial neural network Cloud computing Disaster management LiDAR image Whale swarm optimization

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Steel Surface Defect Detection Using Learnable Memory Vision Transformer

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computers, Materials & Continua 2025年第1期82卷 499-520页

作者： Syed Tasnimul Karim Ayon Farhan Md.Siraj Jia Uddin Department of Computer Science and Engineering BRAC UniversityDhaka1212Bangladesh Department of AI and Big Data Endicott CollegeWoosong UniversityDaejeon34606Republic of Korea

This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as other transformer-based models including Token to Token ViT,ViT withoutmemory,and Parallel *** awidely-used steel surface defect dataset,the research applies data augmentation and t-distributed stochastic neighbor embedding(t-SNE)to enhance feature extraction and *** techniques mitigated overfitting,stabilized training,and improved generalization *** LMViT model achieved a test accuracy of 97.22%,significantly outperforming ResNet18(88.89%)and ResNet50(88.90%),aswell as the Token to TokenViT(88.46%),ViT without memory(87.18),and Parallel ViT(91.03%).Furthermore,LMViT exhibited superior training and validation performance,attaining a validation accuracy of 98.2%compared to 91.0%for ResNet 18,96.0%for ResNet50,and 89.12%,87.51%,and 91.21%for Token to Token ViT,ViT without memory,and Parallel ViT,*** findings highlight the LMViT’s ability to capture long-range dependencies in images,an areawhere CNNs struggle due to their reliance on local receptive fields and hierarchical feature *** additional transformer-based models also demonstrate improved performance in capturing complex features over CNNs,with LMViT excelling particularly at detecting subtle and complex defects,which is critical for maintaining product quality and operational efficiency in industrial *** instance,the LMViT model successfully identified fine scratches and minor surface irregularities that CNNs often *** study not only demonstrates LMViT’s potential for real-world defect detection but also underscores the promise of other transformer-based architectures like Token to Token ViT,ViT without memory,and Parallel ViT in industrial scenarios where complex spatial relationships are *** research m

关键词： Learnable Memory Vision Transformer(LMViT) Convolutional Neural Networks(CNN) metal surface defect detection deep learning,computer vision image classification learnable memory gradient clipping label smoothing t-SNE visualization

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Model-based framework for exploiting sensors of IoT devices using a botnet: a case study with android

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Cyber-Physical Systems 2025年第1期11卷 1-46页

作者： Khaliq, Zubair Khan, Dawood Ashraf Baba, Asif Iqbal Ali, Shahbaz Farooq, Sheikh Umar Department of Computer Science University of Kashmir Jammu and Kashmir Srinagar India AI Thndr Dubai United Arab Emirates Department of Computer Science Tuskegee University TuskegeeAL United States Data Engineering hyke.ai Dubai United Arab Emirates

Botnets have become a severe security threat not only to the Internet but also to the devices connected to it. Factors like the exponential growth of IoT, the COVID-19 pandemic, and the ever-larger number of cybercriminals are incentivising the growth of botnets in this domain. The recent outbreak of botnets like Dark Nexus (derived from Qbot and Mirai), Mukashi, LeetHozer, Hoaxcalls, etc. shows the alarming rate at which this threat is converging. The botnets have attributes that make them an excellent platform for malicious activities in IoT devices used by organisations to safeguard the personal and confidential data of their customers, employees, and business partners. The IoT devices have built-in sensors or actuators that can be exploited to monitor or control the physical environment of the entities connected to them, thereby violating the fundamental concept of privacy-by-design of these devices. In this paper, we design and describe a modular botnet framework for IoT. The framework uses an enhanced centralised architecture associated with a novel ‘Domain Fluxing Technique’. The proposed framework will provide insights into how privacy in IoT devices can be incorporated at design time to check the sensors and actuators in these devices against malicious exploitation, consequently preserving privacy. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Internet of things

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