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Cyber-Attacks and Anomaly Detection in Networking Based on Deep Learning—A Survey 1

2nd International Conference on Computational Intelligence in Machine Learning, ICCIML 2022

作者： Swathi, K. Narsimha, G. Department of Computer Science and Engineering CVR College of Engineering Jawaharlal Nehru Technological University Hyderabad India Department of Computer Science and Engineering Jawaharlal Nehru Technological University Hyderabad India

ISBN: (数字)9789819979547

ISBN: (纸本)9789819979530

The rapid development in Internet has brought great convenience in our daily life, as well as cyber criminals focus shifted from real to virtual life as well. These security problems threaten user’s privacy and also increasing prominently because hackers launch attacks by injecting malicious software in to the network and disturbs normal flow of network activities. Traditional Artificial Intelligence specifically Machine Learning Algorithms is no longer effective in detecting new attack patterns and unknown abnormal behavior in the network traffic. The anomaly detection process helps in providing the report of malicious activities and gives better results. To detect abnormal traffic in network, we need a strong robust deep learning abnormal traffic detection framework. In this paper, different machine learning algorithms were studied in detecting the anomalies and cyber-attacks in network and to identify the problem in existing solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Anomaly detection

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A new hybrid MobileNet-MLP model for secured data communication using signature verification and deep learning

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Iran Journal of computer Science 2024年第1期8卷 233-252页

作者： Pandiyaraju, V. Ganapathy, Sannasi Thangaramya, K. Nahar, R. Raunak Kannan, A. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department Computer Science and Engineering Education National Institute of Technical Teachers Training and Research (NITTTR) Bhopal India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Signature verification plays a critical role in various industries, including finance and document authentication. However, traditional verification techniques have limitations, such as a lack of robustness and an inability to effectively capture intricate patterns and relationships within the signature images. This paper introduces an innovative approach that addresses the drawbacks by integrating conventional machine learning methods with deep learning techniques. For this purpose, we design a new hybrid MobileNet model using deep learning to extract features from the signature images. Here, the Multilayer Perceptron (MLP) classifier is also used to carry out the binary classification over the extracted features. The experimental results of the proposed model demonstrate that the amalgamated technique surpasses the conventional methods and underscores the possibilities of merging traditional machine learning and deep learning in signature verification. Moreover, the proposed model overcomes the limitations of the traditional signature verification techniques by leveraging the strengths of both deep learning and traditional machine learning. Finally, it obtains a more robust and efficient solution and proves it by providing high security to the data in terms of accuracy. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural network Feature extraction MobileNet Multilayer perceptron Signature verification

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Enhancing Secure Development in Globally Distributed Software Product Lines: A Machine Learning-Powered Framework for Cyber-Resilient Ecosystems

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computers, Materials & Continua 2024年第6期79卷 5031-5049页

作者： Marya Iqbal Yaser Hafeez Nabil Almashfi Amjad Alsirhani Faeiz Alserhani Sadia Ali Mamoona Humayun Muhammad Jamal University Institute of Information Technology PMAS-Arid Agriculture UniversityRawalpindiPakistan Department of Software Engineering College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Science College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Engineering&Networks College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaAl Jouf72388Saudi Arabia Department of Mathematics Pir Mehr Ali Shah Arid Agriculture UniversityRawalpindiPakistan

Embracing software product lines(SPLs)is pivotal in the dynamic landscape of contemporary software ***,the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability,underscoring the critical importance of robust cybersecurity *** paper advocates for leveraging machine learning(ML)to address variability management issues and fortify the security of *** the context of the broader special issue theme on innovative cybersecurity approaches,our proposed ML-based framework offers an interdisciplinary perspective,blending insights from computing,social sciences,and ***,it employs ML for demand analysis,dynamic feature extraction,and enhanced feature selection in distributed settings,contributing to cyber-resilient *** experiments demonstrate the framework’s superiority,emphasizing its potential to boost productivity and security in *** digital threats evolve,this research catalyzes interdisciplinary collaborations,aligning with the special issue’s goal of breaking down academic barriers to strengthen digital ecosystems against sophisticated attacks while upholding ethics,privacy,and human values.

关键词： Machine Learning variability management cybersecurity digital ecosystems cyber-resilience

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Intelligent Spatial Anomaly Activity Recognition Method Based on Ontology Matching

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computers, Materials & Continua 2025年第6期83卷 4447-4476页

作者： Longgang Zhao Seok-Won Lee The Knowledge-intensive Software Engineering(NiSE)Research Group Department of Artificial IntelligenceAjou UniversitySuwon City16499Republic of Korea Department of Software and Computer Engineering Ajou UniversitySuwon City16499Republic of Korea

This research addresses the performance challenges of ontology-based context-aware and activity recognition techniques in complex environments and abnormal activities,and proposes an optimized ontology framework to improve recognition accuracy and computational *** method in this paper adopts the event sequence segmentation technique,combines location awareness with time interval reasoning,and improves human activity recognition through ontology *** with the existing methods,the framework performs better when dealing with uncertain data and complex scenes,and the experimental results show that its recognition accuracy is improved by 15.6%and processing time is reduced by 22.4%.In addition,it is found that with the increase of context complexity,the traditional ontology inferencemodel has limitations in abnormal behavior recognition,especially in the case of high data redundancy,which tends to lead to a decrease in recognition *** study effectively mitigates this problem by optimizing the ontology matching algorithm and combining parallel computing and deep learning techniques to enhance the activity recognition capability in complex environments.

关键词： Context awareness activity recognition ontological reasoning complex context anomaly detection

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A system-on-chip microwave photonic processor solves dynamic RF interference in real time with picosecond latency

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Light(Science & Applications) 2024年第1期13卷 91-102页

作者： Weipeng Zhang Joshua C.Lederman Thomas Ferreira de Lima Jiawei Zhang Simon Bilodeau Leila Hudson Alexander Tait Bhavin J.Shastri Paul R.Prucnal Department of Electrical and Computer Engineering Princeton UniversityPrinceton08544NJUSA NEC Laboratories America Princeton08540NJUSA Department of Electrical and Computer Engineering Queen’s UniversityKingstonK7L 3N6OntarioCanada Department of Physics Engineering Physics and AstronomyQueen’s UniversityKingstonK7L 3N6OntarioCanada

Radio-frequency interference is a growing concern as wireless technology advances,with potentially life-threatening consequences like interference between radar altimeters and 5 G cellular *** transceivers mix signals with varying ratios over time,posing challenges for conventional digital signal processing(DSP)due to its high *** challenges will worsen as future wireless technologies adopt higher carrier frequencies and data ***,conventional DSPs,already on the brink of their clock frequency limit,are expected to offer only marginal speed *** paper introduces a photonic processor to address dynamic interference through blind source separation(BSS).Our system-on-chip processor employs a fully integrated photonic signal pathway in the analogue domain,enabling rapid demixing of received mixtures and recovering the signal-of-interest in under 15 *** reduction in latency surpasses electronic counterparts by more than three orders of *** complement the photonic processor,electronic peripherals based on field-programmable gate array(FPGA)assess the effectiveness of demixing and continuously update demixing weights at a rate of up to 305 *** compact setup features precise dithering weight control,impedance-controlled circuit board and optical fibre packaging,suitable for handheld and mobile *** experimentally demonstrate the processor’s ability to suppress transmission errors and maintain signal-to-noise ratios in two scenarios,radar altimeters and mobile *** work pioneers the real-time adaptability of integrated silicon photonics,enabling online learning and weight adjustments,and showcasing practical operational applications for photonic processing.

关键词： processor mixing dynamic

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VisiCompanion: Comparative Analysis of Yolov8 and Yolov9 3

VisiCompanion: Comparative Analysis of Yolov8 and Yolov9

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3rd International Conference on Disruptive Technologies, ICDT 2025

作者： Chhabra, Payal Arora, Tanmay Jain, Vibhore Gandhi, Neeraj Bhatnagar, Swapnil Arora, Nitin Ghaziabad India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Patiala India

ISBN: (纸本)9798331519582

There are approximately 21 million visually im-paired individuals who depend on other people for their basic work. This research adds to the current understanding of assistive technology by introducing a method for choosing and optimizing the best object detection models suited for accessi-bility environments. This research enhances the Visicompanion functions and assists visually impaired individuals in doing crucial daily tasks independently by selecting a suitable model. This research compares the analysis of two models i.e. Y oLov8 [1] and YOLOV9 [2] and identifies which algorithm is best for the application. YOLOv8 is known for its rapid inference speed which is beneficial for real-time systems whereas YOLOv9 introduces advanced detection capability that improves accuracy but may increase problems for processing time and computational resource requirements. This research uses evaluation metrics like precision, recall, size of model, and resource utilization across different hardware configurations. The results showed thatYOLOv9 is slightly better than YOLOv8 with mAP reaching 95% and precision 96.8%. © 2025 IEEE.

关键词： Interactive computer systems

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CNN-Driven Voice Guidance: Image Based Medicine Recognition

CNN-Driven Voice Guidance: Image Based Medicine Recognition

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International Conference on Business Intelligence and Data Analytics, BIDA 2024

作者： Surapaneni, Ravi Kishan Aruna Bharathi, Jadam Divya, Durgempudi Department of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Kanuru520007 India

ISBN: (纸本)9789819777167

There are many benefits of using image processing to identify educations. This helps the person to gain knowledge in the field of medicine beyond formal education. By taking pictures of packaging sheets, regardless of educational background, one may quickly identify medications using a straightforward image-based interface. The described method yields fast and precise results while drastically reducing the amount of time spent looking for information. Moreover, the system’s integration of speech output capabilities guarantees that users receive audio-based information regarding the detected medicine, irrespective of their reading level or visual disability. This proposed method uses Neural Networks which takes images as input and process it to find critical features which will help to classify the images to their respective categories. The proposed article has an accuracy of 96.22%. The utilization of image processing in drug identification not only guarantees dependability and precision but also makes a substantial contribution to the promotion of knowledgeable and easily accessible healthcare practices. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Audio output CNN model Image Processing Medicines

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Performance Analysis of Hybrid Metaheuristic Cross-Layer Optimization Algorithm (HMCLOA) in UAV -based IoT Networks 4

Performance Analysis of Hybrid Metaheuristic Cross-Layer Opt...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Rajashekar, A. Chouhan, Dharamendra University Visvesvaraya College of Engineering Bangalore University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331523923

Optimization in UAV based IoT networks is the most important task for the improvement of network performance, resource utilization, and overall efficiency. UAV -based IoT networks consist of decentralized architecture and dynamic topology. Due to this nature, the network faces challenges like routing, resource allocation, energy efficiency, and Quality of Service (QoS). Addressing these challenges is essential for real-time communication in smart city infrastructure, where UAVs provide reliable and efficient data transmission through free-space communication. Hence, to overcome this research issues, proposed a scheme called 'Hybrid Metaheuristic Cross-Layer Optimization Algorithm (HMCLOA)' finds an energy efficient routing path for an efficient data transmission. A comparative analysis of different optimization approaches highlights their merits and suitability for various network requirements. The major challenges in UAV based IoT networks such as throughput, delay energy consumption, and QoS constraints are discussed, and compared the performance with the existing optimization techniques such as metaheuristic algorithms, cross-layered approaches, and game-theoretic strategies. Finally, it is concluded that the proposed Hybrid Metaheuristic Cross-Layer Optimization Algorithm (HMCLOA) optimization technique helps to improve routing efficiency, resource management, energy consumption, and QoS, thereby enhancing the overall performance and reliability of UAV-based IoT networks. © 2025 IEEE.

关键词： Resource allocation

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A comparative study on data mining models for weather forecasting: A case study on Chittagong, Bangladesh

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Natural Hazards Research 2024年第2期4卷 295-303页

作者： Tahsin, Mohammad Sadman Abdullah, Shahriar Karim, Musaddiq Al Ahmed, Minhaz Uddin Tafannum, Faiza Ara, Mst Yeasmin Department of Computer Science and Engineering BRAC University Dhaka Bangladesh Department of Geography University of Alabama Tuscaloosa 35487 AL United States Department of Computer Science and Engineering American International University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering University of Texas at Arlington Texas 76019 TX United States

The primary focus of this study is to analyze and predict the patterns of this essential feature of the natural world. This study analyses and predicts the daily weather patterns of a specific urban area. This article utilizes weather data over 20 years to analyze the climate patterns of Chittagong city. A total of 12 distinct Data Mining models were employed to predict daily weather patterns. The algorithms can be categorized into three distinct types, namely rules-based, tree-based, and function-based. To evaluate the effectiveness of the models, various performance metrics were computed, including precision, recall, accuracy, F-measure, and the area under the receiver operating characteristic curve (ROC area). Based on the results obtained, it can be concluded that among the 12 algorithms evaluated, J48 exhibits the highest level of performance and accuracy. The J48 classifier demonstrated an accuracy of 82.30%, precision of 82.40%, recall of 82.20%, f-measure of 84.20%, and a ROC area of 97.8%. Furthermore, a comprehensive analysis of the confusion matrix for all twelve algorithms was conducted to facilitate further evaluation. © 2023 National Institute of Natural Hazards, Ministry of Emergency Management of China

关键词： Data mining Temperature Weather forecasting Weather prediction

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Optimizing Multilayer Perceptron Classifiers for Predictive Heart Disease Diagnosis: A Hyperparameter Tuning Approach 8

Optimizing Multilayer Perceptron Classifiers for Predictive ...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Vadlamudi, Anvitha Talasila, Smeeth Althaph, Bollapalli Challa, Nagendra Panini Vadlamudi, Gnana Abhinay School of Computer Science and Engineering VIT-AP University Amaravati India Department of Computer Science and Engineering New York United States

ISBN: (纸本)9798331505462

Heart diseases, collectively known as cardiovascular diseases (CVDs), remain one of the foremost health challenges across the globe, taking millions of lives each year. This study evaluates the effectiveness of Multilayer Perceptron (MLP) classifiers in predicting CVDs using the Heart Disease Dataset from Kaggle. We implemented a structured approach using data pre-processing, model training with baseline MLP classifiers, and hyperparameter optimization utilizing Random and Grid Search techniques. The optimized models, particularly the MLP Classifier trained with Grid Search, outperformed the baseline, achieving up to 98.54 % accuracy and 100% precision. However, analysis of learning curves indicated the need for further model refinement to enhance generalization on unseen data. This study underscores the importance of hyperparameter tuning in developing effective MLP classifiers for CVDs prediction and suggests directions for future research, including exploring advanced optimization methods and regularization techniques. © 2024 IEEE.

关键词： Cardiology

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