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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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Enhancing Nucleus and Cytoplasm Segmentation in Cervical Cytology Using Deep Learning

Enhancing Nucleus and Cytoplasm Segmentation in Cervical Cyt...

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IEEE Punecon

作者： Soham Mhatre Arya Singh Swati Shinde Department of Computer Science and Engineering (AI&ML) Pimpri Chinchwad College of Engineering Pune India Department of Computer Science and Engineering Pimpri Chinchwad College of Engineering Pune India

ISBN: (数字)9798331527822

ISBN: (纸本)9798331527839

Cervical cancer, with over 660,000 cases in 2022, ranks as the fourth most frequent malignancy in women globally, with low- and middle-income nations accounting for approximately 94% of the 350,000 annual deaths. The challenging diagnosis of cervical cancer, often detected at advanced stages, has been addressed by recent advancements in high-throughput quantitative imaging of 2D cell cultures. Image segmentation automates the traditionally manual process of Pap smear cell diagnosis. While conventional approaches are effective, they face limitations in underrepresented cellular regions, noise, and blurriness in microscopy. Semantic segmentation causes inter-class competition, which causes performance issues for popular segmentation algorithms such as U-Net and Mask R-CNN in multi-class contexts. Our novel approach addresses these challenges by treating nucleus and cytoplasm segmentation as separate tasks, significantly enhancing overall segmentation performance. The nucleus, characterized by higher density and darker appearance, presents a more readily learnable target for the U-Net algorithm. While watershed and similar methods partially mitigate the issue of overlapping and occluded cells, challenges persist. A viable approach is to use pre-trained open-source models like Cellpose, which have been trained on thousands of images. Fine tuning these models through transfer learning for cytoplasm segmentation has produced remarkable outcomes. Pairing segmented nuclei and cytoplasm via centroid distance analysis enhances segmentation accuracy and resolves cellular overlaps. With an accuracy of 99.3%, an IoU of 83.91%, a Dice Coefficient of 91.04%, and precision of 89.88% for nucleus segmentation, and 98.81%, 95.62%, and 91.81% for cytoplasm segmentation, our method demonstrated remarkable accuracy across several parameters. These findings show how our method may help with cervical cancer diagnosis and detection.

关键词： Accuracy Semantic segmentation Microscopy Noise Transfer learning Watersheds Manuals Cervical cancer Tuning Optimization

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Navigating Challenges in Creating Effective Chatbots with Natural Language Processing 15th

Navigating Challenges in Creating Effective Chatbots with Na...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Sathwik, Abbaraju Sai Rao, A. Koteswara Saiteja, Bheri Madhavi, K. Reddy Doppalapudi, Asutosh Amudha, P. VIT-AP University Amaravati India Department of Computer Science and Engineering Andhra Loyola Institute of Engineering and Technology Vijayawada India AI&ML School of Computing Mohan Babu University Tirupati India Coimbatore Ayya Avinashilingam Nagar641108 India

ISBN: (纸本)9783031889912

A conversational agent, also referred to as a chatbot, is computer software that can communicate with users in natural language. Because of a number of issues, developing a successful chatbot model is still difficult. First, understanding the nuances and context of human language remains a complex task, especially when dealing with slang, dialects, or ambiguous expressions. Second, maintaining engaging and coherent conversations over extended periods can be a hurdle, as chatbots need to adapt to changing topics and user moods. Third, ensuring ethical and responsible behavior in chatbots, such as avoiding biased or harmful responses, requires continuous monitoring and refinement. Lastly, staying up-to-date with the evolving field of NLP and ai is essential to incorporate the latest advancements and provide users with increasingly sophisticated conversational experiences. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Chatbots

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Drone Detection and Tracking using Deep Convolutional Neural Networks from Real-time CCTV Footage

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IEIE Transactions on Smart Processing and Computing 2024年第4期13卷 313-321页

作者： Allmamun, Md Akter, Fahima Talukdar, Muhammad Borhan Uddin Chakraborty, Sovon Uddin, Jia Department of Computer Science and Engineering European University of Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering University of Liberal Arts Bangladesh Dhaka Bangladesh AI and Big Data Department Woosong University Daejeon Korea Republic of

Drones are flying objects that may be controlled remotely or programmed to do a wide range of tasks, including aerial photography, videography, surveys, crop and animal monitoring, search and rescue missions, package delivery, and military operations. Unrestrained use, however, can pose a significant threat to safety, privacy, and security through eavesdropping, flying close to prohibited locations, interfering with public events, and delivering illicit items. Hence, real-time drone detection and tracking are indispensable and appropriate measures. This study developed real-time drone detection and tracking using the most efficient deep-learning approaches. The models were fine-tuned first to suit the required purpose and yield the desired outcome. The performance of the developed system was better than that of earlier endeavors in terms of accuracy and loss. Of the seven fined-tuned models, the Xception model constantly rendered the maximum accuracy with negligible loss. The model outperformed other state-of-the-art architectures, exhibiting an accuracy and loss of 99.18% and 3.83, respectively. Copyrights © 2024 The Institute of Electronics and Information Engineers.

关键词： Aerial photography

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An Innovative Method for Brain Stroke Prediction based on Parallel RELM Model 4

An Innovative Method for Brain Stroke Prediction based on Pa...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Kavitha, K. Devi, Vanjangi Jyotsna Alam, Mohammad Shabbir Sindhu, V. Gandhewar, Nisarg Kalra, Gourav Gmr Institute of Technology Department of CSE-AI&ML Andhra Pradesh Razam India Aditya Institute of Technology and Management Department of Information Technology Andhra Pradesh Tekkali India College of Engineering and Computer Science Jazan University Department of Computer Science Jizan Saudi Arabia Christ University Department of Computer Science Karnataka Bangalore India Shri Ramdeobaba College of Engineering and Management Department of Aiml Nagpur India Department of Mechanical Engineering Mullana Ambala India

ISBN: (纸本)9798331540364

Strokes occur when blood supply to the brain is suddenly cut off or severely impaired. Stroke victims may experience cell death as a result of oxygen and food shortages. The effectiveness of various predictive data mining algorithms in illness prediction has been the subject of numerous studies. The three stages that make up this suggested method are feature selection, model training, and preprocessing. Missing value management, numeric value conversion, imbalanced dataset handling, and data scaling are all components of data preparation. The chi-square and RFE methods are utilized in feature selection. The former assesses feature correlation, while the latter recursively seeks for ever-smaller feature sets to choose features. The whole time the model was being trained, a Parallel RELM was used. This new method outperforms both ELM and RELM, achieving an average accuracy of 95.84%. © 2024 IEEE.

关键词： Contrastive Learning

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Detection of Unathorized Vechile in BRT Lane

Detection of Unathorized Vechile in BRT Lane

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IEEE Punecon

作者： Pallavi Nikumbh Pankaj More Harsh Vahal Pratham Mali Prathmesh Mohite Department of Computer Science and Engineering (AI&ML) Pimpri Chinchwad College of Engineering Pune India Department of Computer Science and Engineering Pimpri Chinchwad College of Engineering Pune India

ISBN: (数字)9798331527822

ISBN: (纸本)9798331527839

The increasing number of unauthorized vehicles in the Bus Rapid Transit (BRT) lanes creates a significant challenge to urban transportation. Due to this efficiency and quality of safety decreases. Our project aims to develop an automated system that detects unauthorized vehicles that come into BRT lanes using deep learning techniques like YOLOv8. The system employs combination video surveillance, image processing, and deep learning models to accurately identify and track vehicles that violate the BRT regulations. The proposed solution enhances traffic management and reduces the incidence of unauthorized lane usage by integrating the real- time data processing and alert mechanisms. The system employs a dataset from Roboflow, extended with a self-made dataset containing images of various vehicles in BRT lanes. We utilize YOLOv8 for object detection to accurately identify and classify vehicles. Also, Easy-OCR is used to recognize text from license plates for validation and verification against authorized vehicle list. Our preliminary results demonstrate that the system achieves 80% accuracy in detecting unauthorized vehicles. Our solution is urban traffic management by reducing illegal lane usage and ensuring easier public transport operations.

关键词： Deep learning Text recognition Urban areas Transportation Object detection Video surveillance Regulation Safety Object recognition License plate recognition

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The Future of Smart Factories: Real-Time Automation and Big Data Analytics on the Cloud 15

The Future of Smart Factories: Real-Time Automation and Big ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Singh, K. Ranjith Arora, Arpit Goud, Dilip Ambika, Krovvidi S B Sandeep, V. More, Priyanka Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India NNRG Engineering College India ADITYA COLLEGE of ENGINEERING and TECHNOLOGY Department of CSE-AI&ML & DS Surampalem India School of Engineering and Technology Mechaical Engineering Karnataka Bangalore India Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

The evolving future of manufacturing will be all about intelligent factories featuring state-of-the-art technologies. Smart factories combine connected automation and big data in the cloud to streamline production, increase productivity, and lower operational costs. Real-time automation utilizes state-of-the-art sensors, internet of Things (IoT) devices, and artificial intelligence (ai) technology to monitor and control machines or processes in real time independently. This results in faster potential decision-making and bright maintenance works, thus decreasing downtime risk and increasing overall productivity. Big data analytics in the cloud enables real-time collection and analysis of extensive amounts of production processes, machine performance, and supply chain management-related information. The data can then be utilized to discover areas that need improvement, predict maintenance requirements, and even optimize the level of inventories in the sense that it would save costs while at the same time becoming more efficient. Also, real-time automation combined with significant cloud data analytics results in improved communication and collaboration from one production stage to another, hence smooth operations. In other words, it helps create an even more agile and adaptable manufacturing environment where processes can be fine-tuned on the fly to deal with dynamic market shifts. © 2024 IEEE.

关键词： Smart manufacturing

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Context is King: Rethinking Query Expansion in Modern Search Engines

Context is King: Rethinking Query Expansion in Modern Search...

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2025 International Conference on Artificial Intelligence and Data engineering, aiDE 2025

作者： Batri, Krishnan Lakshmi, S. Ramesh, S. Sangeetha, N. Annamalai, S. School of Computer Science and Engineering Bengaluru India Electronics and Communication Engineering Bengaluru India City Engineering College Department of AI&ML Bengaluru India

ISBN: (纸本)9798331527518

In the era of digital information overload, search engines have evolved beyond simple keyword matching to embrace context-aware methodologies. However, the task of accurately interpreting user intent remains a critical challenge, with traditional query expansion techniques often falling short of capturing semantic and contextual nuances. This paper critically examines the evolution of query expansion strategies, highlighting their limitations and exploring state-of-the-art techniques such as machine learning models, knowledge graphs, and transformer-based systems like BERT and GPT. Experimental results compare traditional methods like synonym expansion with modern approaches, demonstrating the superior relevance and diversity of results from context-aware systems. The study underscores the importance of integrating real-time contextual signals and dynamic user preferences to enhance search engine performance, paving the way for more personalized and accurate retrieval systems. © 2025 IEEE.

关键词： Search engines

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Sentiment Analysis in Social Media using Bidirectional Long Short Term Memory with Focus Loss function

Sentiment Analysis in Social Media using Bidirectional Long ...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Sivakumaran, A.R. Shnain, Ammar H. Lande, Jayapal Sindgi, Anupama Sruthi, Patlolla Department of Information Technology Hyderabad India College of Technical Engineering the Islamic University Department of Computers Techniques Engineering Najaf Iraq Sr University Department of Computer Science & Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India Gokaraju Rangaraju Institute of Engineering and Technology Department of Ai&ml Hyderabad India

ISBN: (纸本)9798350375442

Sentiment Analysis (SA), is often known as "Opinion Mining"which seeks to analyze sentiment polarities in opinion-based texts. Several Deep Learning (DL) approaches are developed to effectively solves the complex SA challenges. However, existing methods failed to identify the sentiments correctly due to contextual embedding's and different meaning for same word in a sentence or document. To overcome this, a Bidirectional - Long Short Term Memory with Focal Loss (Bi-LSTM-FL) function for SA in social media. The proposed Bi-LSTM-FL model enhanced the SA process by capturing the context from both past and future words in a sentence which lead to understand complex sentence structures. The twitter dataset is utilized and preprocessed by to enhance the text format for classification. The preprocessed texts are fed to Principal Component Analysis (PCA) for selecting optimal set of features and to reduce dimensionality of features. Finally, with the help of selected features the tweets are categorized as positive, negative or neutral effectively. The experimental results of proposed Bi-LSTM-FL achieved accuracy of 98.2% which is greater when compared to previous method such as Gated Attention Recurrent Network (GARN). © 2024 IEEE.

关键词： Long short-term memory

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Circle Search Algorithm with Convolutional Neural Network for Distributed Denial-of-Service Attack Detection in Software-Defined Networks

Circle Search Algorithm with Convolutional Neural Network fo...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Padmakala, S. Al-Jawahry, Hassan M. Palanivel, R. John, Y.M. Mahaboob Madhavi, Arelli Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Chennai India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ai & Ds Bengaluru India Mahendra College of Engineering Department of Ece Salem India Gokaraju Rangaraju Institute of Engineering and Technology Department of Ai&ml Hyderabad India

ISBN: (纸本)9798350375442

The Distributed Denial-of-Service (DDoS) attack is a crucial cybersecurity threat for Software-Defined Network (SDN). However, DDoS in SDN severely affect network availability which reduces the classifier performance. Hence, Circle Search Algorithm with Convolutional Neural Network (CSA-CNN) is proposed for detecting DDoS anomaly attacks in SDN. The DDoS anomaly dataset such as CIC-DDoS2019 is preprocessed through label encoding which changes categorical data to arithmetical data types through allocating single numerical labels to every class. Then, CSA is applied for feature selection which helps to efficiently detect malicious traffic patterns by exploring local and global search spaces and improves the detection accuracy. The CNN has ability to detect known and unseen attacks due to handling multidimensional data which increase the security of SDN. The CSA-CNN achieved better performance in terms of 99.97%, 99.95%, 99.94% and 99.98% of precision, sensitivity, f1-score and accuracy for CIC-DDoS2019 dataset when compared to Gated Recurrent Unit (GRU). © 2024 IEEE.

关键词： Convolutional neural networks

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