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A New Cybersecurity Approach Enhanced by xAI-Derived Rules to Improve Network Intrusion Detection and SIEM

computers, Materials & Continua 2025年第5期83卷 1607-1621页

作者： Federica Uccello Marek Pawlicki Salvatore D'Antonio RafałKozik MichałChoras Centro Direzionale Department of EngineeringUniversity of Naples‘Parthenope’Isola C4Napoli80133Italy Department of Computer and Information Science Software and SystemsLinköping UniversityLinköping58183Sweden ITTI Sp.z o.o. Pozna´n61-612Poland Faculty of Telecommunications Computer Science and Electrical EngineeringBydgoszcz University of Science and TechnologyBydgoszcz85-796Poland

The growing sophistication of cyberthreats,among others the Distributed Denial of Service attacks,has exposed limitations in traditional rule-based Security Information and Event Management *** machine learning–based intrusion detection systems can capture complex network behaviours,their“black-box”nature often limits trust and actionable insight for security *** study introduces a novel approach that integrates Explainable Artificial Intelligence—xAI—with the Random Forest classifier to derive human-interpretable rules,thereby enhancing the detection of Distributed Denial of Service(DDoS)*** proposed framework combines traditional static rule formulation with advanced xAI techniques—SHapley Additive exPlanations and Scoped Rules-to extract decision criteria from a fully trained *** methodology was validated on two benchmark datasets,CICIDS2017 and *** rules were evaluated against conventional Security Information and Event Management systems rules with metrics such as precision,recall,accuracy,balanced accuracy,and Matthews Correlation *** results demonstrate that xAI-derived rules consistently outperform traditional static ***,the most refined xAI-generated rule achieved near-perfect performance with significantly improved detection of DDoS traffic while maintaining high accuracy in classifying benign traffic across both datasets.

关键词： Cybersecurity explainable artificial intelligence intrusion detection system rule-based SIEM distributed denial of service

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A Robust Automated Framework for Classification of CT Covid-19 Images Using MSI-ResNet

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computer systems science & Engineering 2023年第6期45卷 3215-3229页

作者： Aghila Rajagopal Sultan Ahmad Sudan Jha Ramachandran Alagarsamy Abdullah Alharbi Bader Alouffi Department of Computer Science and Business Systems Sethu Institute of TechnologyKariapattiVirudhunagar626115TamilnaduIndia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj11942Saudi Arabia Department of Computer Science and Engineering School of EngineeringKathmandu UniversityBanepaKathmanduNepal Department of CSE University College of EngineeringPanrutiTamilnaduIndia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Nowadays,the COVID-19 virus disease is spreading *** are some testing tools and kits available for diagnosing the virus,but it is in a lim-ited *** diagnose the presence of disease from radiological images,auto-mated COVID-19 diagnosis techniques are *** enhancement of AI(Artificial Intelligence)has been focused in previous research,which uses X-ray images for detecting *** most common symptoms of COVID-19 are fever,dry cough and sore *** symptoms may lead to an increase in the rigorous type of pneumonia with a severe *** medical imaging is not suggested recently in Canada for critical COVID-19 diagnosis,computer-aided systems are implemented for the early identification of COVID-19,which aids in noticing the disease progression and thus decreases the death ***,a deep learning-based automated method for the extraction of features and classi-fication is enhanced for the detection of COVID-19 from the images of computer tomography(CT).The suggested method functions on the basis of three main pro-cesses:data preprocessing,the extraction of features and *** approach integrates the union of deep features with the help of Inception 14 and VGG-16 *** last,a classifier of Multi-scale Improved ResNet(MSI-ResNet)is developed to detect and classify the CT images into unique labels of *** the support of available open-source COVID-CT datasets that consists of 760 CT pictures,the investigational validation of the suggested method is *** experimental results reveal that the proposed approach offers greater performance with high specificity,accuracy and sensitivity.

关键词： Covid-19 CT images multi-scale improved ResNet AI inception 14 and VGG-16 models

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Towards Artificial Intelligence-Driven Marketing: An Adoption Framework for Lesotho 8

Towards Artificial Intelligence-Driven Marketing: An Adoptio...

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8th Conference on Information Communication Technology and Society, ICTAS 2024

作者： Batani, John Mothabeng, Matau Rosina Mbunge, Elliot Botho University Faculty of Engineering & Technology Maseru100 Lesotho Botho University Faculty of Business & Accounting Maseru100 Lesotho Department of Computer Science Kwaluseni Manzini Swaziland

ISBN: (纸本)9798350314892

Artificial intelligence (AI) is revolutionising almost every industry and sphere of life. AI has been utilise to meet customers' expectations in business sectors by customising products and services. AI-driven marketing promises to improve customer service and satisfaction by analysing customergenerated data to create value, enhance resource allocation and develop effective and cost-effective marketing strategies through big data analytics. However, adopting AI-driven digital marketing in some resource-constrained settings is still nascent, and there is a dearth of studies to enhance AI-driven marketing. Thus, this study proposed a framework to enhance AI-driven marketing adoption by addressing the barriers to the adoption of AI-driven marketing and customer satisfaction in Lesotho. The study used a qualitative research design to (i) explore the challenges faced by businesses in Lesotho in adopting AI technologies for digital marketing and customer satisfaction, (ii) investigate the barriers to AI-driven marketing and customer satisfaction by Lesotho's businesses, and (iii) propose a framework for AI-driven marketing adoption by businesses in Lesotho. Findings revealed that lack of awareness, legislation, resistance and financial constraints were the main barriers to AI-driven marketing adoption. The proposed framework suggests interventions to deal with these impediments. © 2024 IEEE.

关键词： Customer satisfaction

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LieVis: A Visual Interactive Dashboard for Lie Detection Using Machine Learning and Deep Learning Techniques 26

LieVis: A Visual Interactive Dashboard for Lie Detection Usi...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Prome, Shanjita Akter Rafiqul Islam, Md Asirvatham, David Hossain Sakib, Md. Kowsar Ari Ragavan, Neethiahnanthan Australian Institute of Higher Education Business Information Systems Australia Taylors University School of Computer Science Malaysia Taylors University Faculty of Social Sciences & Leisure Management Malaysia

ISBN: (纸本)9798350359015

Lying poses a significant concern in our everyday lives, impacting social interactions. The skill of detecting lies or deceitful statements is of immense value, mainly due to the elusive nature of the underlying patterns associated with lying. Numerous studies have extensively explored lie detection, employing various advanced techniques. Researchers have devoted their attention to devising more effective and efficient solutions for lie detection. However, there remain ongoing challenges, particularly in achieving higher accuracy levels. There has been relatively limited focus on developing visually interpretable and interactive systems for lie detection. Therefore, we leveraged artificial intelligence (AI), incorporating machine learning and deep learning techniques to augment the existing system. Our experiment shows that deep learning models, specifically BERT, achieve the highest accuracy, whereas machine learning models such as Random Forest (RF), and K-Nearest Neighbors (KNN) exhibit superior accuracy across both sets of features. To enhance result exploration, we introduced a dedicated visual interactive system/dashboard called LieVis, meticulously designed to facilitate comprehensive exploration of the obtained results. The evaluation of LieVis showcases its superiority over existing systems. The source code and the live dashboard with result visualization are available on our GitHub repository https://***/KHSakib/LieVis/tree/master. © 2023 IEEE.

关键词： Deep Learning Lie Detection Machine Learning Visual Dashboard

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AI-based Models to Predict the Heart Rate Using PPG and Accelerometer Signals During Physical Exercise

AI-based Models to Predict the Heart Rate Using PPG and Acce...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Ribeiro, Lucas Oliveira, Helder P. Hu, Xiao Pereira, Tania University of Porto Inesc Tec Inesc Tec - Institute for Systems and Computer Engineering Technology and Science Feup - Faculty of Engineering Porto Portugal University of Porto Inesc Tec - Institute for Systems and Computer Engineering Technology and Science Fcup - Faculty of Science Porto Portugal Emory University Nell Hodgson Woodruff School of Nursing Department of Biomedical Informatics School of Medicine Department of Computer Science College of Arts and Sciences Atlanta United States Technology and Science Inesc Tec - Institute for Systems and Computer Engineering Porto Portugal

ISBN: (纸本)9798350337488

PPG signal is a valuable resource for continuous heart rate monitoring;however, this signal suffers from artifact movements, which is particularly relevant during physical exercise and makes this biomedical signal difficult to use for heart rate detection during those activities. The purpose of this study was to develop learning models to determine heart rate using data from wearables (PPG and acceleration signals) and dealing with noise during physical exercise. Learning models based on CNNs and LSTMs were developed to predict the heart rate. The PPG signal was combined with data from accelerometers trying to overcome the noise movement on the PPG signal. Two datasets were used on this work: the 2015 IEEE Signal Processing Cup (SPC) dataset was used for training and testing, and another dataset was used for validation of the learning model (PPG-DaLiA dataset). The predictions obtained by the learning model represented a mean average error of 7.033±5.376 bpm for the SCP dataset, while a mean average error of 9.520±8.443 bpm for the validation set. The use of acceleration data increases the performance of the learning models on the prediction of the heart rate, showing the benefits of using this source of data to overcome the noise movement problem on the PPG signal. The combination of PPG signal with acceleration data could allow the learning models to use more information regarding the motion artifacts that affect the PPG and improve performance on the physiological event detections, which will largely spread the use of wearables on the healthcare applications for continuous monitor the physiological state allowing early and accurate detection of pathological events. © 2023 IEEE.

关键词： Convolutional Neural Network Heart Rate Estimation Long Short Term Memory Motion Artifacts Photoplethysmography

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Reversible Data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年第13期12卷 23933-23945页

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： Cryptography Encryption Security Data mining Adaptive coding Correlation Cloud computing Polynomials Image coding Streaming media

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AraDS: Arabic Datasets for Text Mining Approaches

AraDS: Arabic Datasets for Text Mining Approaches

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Yafooz, Wael M.S. Alsaeedi, Abdullah Emara, Abdel-Hamid M. College of Computer Science and Engineering Taibah University Computer Science Department Medina Saudi Arabia Al-Azhar University Faculty of Engineering Department of Computers and Systems Engineering Cairo11884 Egypt

ISBN: (纸本)9798350347050

With the digital transformation, life started depending on the digital world. Hence, there is a massive amount of unstructured textual data produced and accumulated faster. Such data used in many applications such sentiment analysis, topic modeling, summarization, classifications, and clustering. However, researcher's wastes time and effort in collecting data and constructing a dataset to examine and evaluate their models or algorithms. In the Universal Language of English, there are many benchmark datasets. The Arabic language lacks datasets in many domains. This paper introduces an Arabic dataset called AraDS. AraDS consist of three Arabic datasets namely;Arabic Dataset on Herbal Treatments for Diabetes (ADHTD), Arabic Multi-Classification Dataset (AMCD), and the Arabic Sentiment Dataset-Khat (ASDK). These datasets were collected from social media platforms such as YouTube. It contains user-generated comments and video metadata. AraDS publicly available dataset. The assessment of the annotation process has been carried out and evaluated using four methods. They are Cohen's kappa, three Arabic native speaker annotators, accuracy, and F-measure. AMCD and ASDK datasets are balanced, whereas ADHTD is an imbalanced dataset. Two datasets are sided with binary classes and the third one is a multi-class dataset. These datasets can be beneficial for many data, text mining, and sentiment analysis researchers to apply methods and algorithms. © 2023 IEEE.

关键词： Sentiment analysis

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Evaluation and Comparison of Emotionally Evocative Image Augmentation Methods 28th

Evaluation and Comparison of Emotionally Evocative Image Aug...

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28th International Conference on Knowledge Based and Intelligent information and Engineering systems, KES 2024

作者： Ignatowicz, Jan Kutt, Krzysztof Nalepa, Grzegorz J. Jagiellonian Human-Centered AI Lab Mark Kac Center for Complex Systems Research Institute of Applied Computer Science Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Lojasiewicza 11 Kraków30-348 Poland

Experiments in affective computing are based on stimulus datasets that, in the process of standardization, receive metadata describing which emotions each stimulus evokes. In this paper, we explore an approach to creating stimulus datasets for affective computing using generative adversarial networks (GANs). Traditional dataset preparation methods are costly and time consuming, prompting our investigation of alternatives. We conducted experiments with various GAN architectures, including Deep Convolutional GAN, Conditional GAN, Auxiliary Classifier GAN, Progressive Augmentation GAN, and Wasserstein GAN, alongside data augmentation and transfer learning techniques. Our findings highlight promising advances in the generation of emotionally evocative synthetic images, suggesting significant potential for future research and improvements in this domain. © 2024 The Authors.

关键词： Generative adversarial networks

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Hybrid Algorithm for Secure Routing in MANET Using Cryptographic Applications

Hybrid Algorithm for Secure Routing in MANET Using Cryptogra...

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2024 IEEE International Conference on Electronic systems and Intelligent Computing, ICESIC 2024

作者： Muruganandam, S. Gnanavel, S. Narayana, K.E. Jaeyalakshmi, M. Antony Kumar, K. Panimalar Engineering College Department of Computer Science and Business Systems Chennai India SRM Institute of Science and Technology Kattankulathur Campus Faculty of Engineering and Technology Department of Computing Technologies Chengalpattu India Rajalakshmi Engineering College Department of Information Technology Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

In Mobile sensor Networks, Each and every wireless node dynamically changes its location due to its mobility nature. The routing information is maintained by the master node to deliver a data packet from source to destination. The attackers easily hack this wireless network. The performance of the complete networks is dropped by packets transmission attacks. Attacker node drops the packet. An enhanced protection technique in response to packets transmission misbehaviour attacks is provided to increase the energy efficiency of mobile nodes. This paper proposed the Cryptographic formed Malicious Node Detection and Secure Routing framework (CMNDSR). This model incorporates three various strategies specially algorithm for control of access, to protects initial illegal node in the network. The suspected identification and prevention techniques based on hop count value. Secure routing method with a private key generated at random like one time password (OTP). The proposed methods are power efficient, by operating a minimum number of nodes in challenging environment. The misbehaviour nodes are promptly identified and isolated using this proposed approach. The Cryptographic based Secure routing algorithm is applied to provide secure transmission in wireless networking environment. The outcomes of the simulation display how effectively the recommended approach can identify malicious nodes in a MANET. In comparison with previous methods, the energy consumption is 10% lower and the suspicious node detection rate is increased by 95%. © 2024 IEEE.

关键词： Encryption algorithms

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An Enhanced Diagnostic System using Deep Learning for Early Prediction of Pancreatic Cancer 10

An Enhanced Diagnostic System using Deep Learning for Early ...

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10th International Conference on Electrical Energy systems, ICEES 2024

作者： Bhargavi, Kumbham Prasad, M Lakshmi Arun, G. Shaker Reddy, Pundru Chandra Yuvalatha, S. Triveni, M Naga Telangana Hyderabad India Computer Science and Engineering Institute of Aeronautical Engineering Telangana Hyderabad India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Computer Science and Engineering Chennai India SR University School of Computer Science and Artificial Intelligence Telangana Warangal India Anna University Bannari Amman Institute of Technology Computer Science and Business Systems Tamil Nadu India Telangana Hyderabad India

ISBN: (纸本)9798350353778

With a dismal 5-year survival-rate of approximately 7%, pancreatic-cancer(PC) remains one of the world's most lethal tumors. In order to improve patient survival rates, early detection of PC is essential. computerized-tomography(CT), MRI combined with MRCP, or a biopsy is necessary for the diagnosis of PC. The steps involved in the suggested CAD design process are as follows: picture preprocessing, segmentation, feature-extraction & classificatiion. Color conversion and the isotropic diffusion filter method are utilized for preprocessing. The next step is the segmentation processes' usage of the suggested Fuzzy K-NN Equality code. A classification tool that makes use of Deep Learning is feature extraction. Using the characteristics gathered from the pancreatic sample, tumor cells are categorized. The image classification criterion includes both the train values and the testing datasets. To identify pancreatic cancer, an algorithm called DCNN_DBN is employed, which combines Deep-Convolutional Neural-Networks with Deep-Belief-Networks. The results of the experiments show that the present CAD system has great promise and is safe for the automated diagnosis of benign & malignant tumors, with an accuracy rate of 99.8 percent. The use of this classifier greatly reduces the computational complexity. More anomalies in pancreatic cancer cells could be detected with an improved version of the proposed method. © 2024 IEEE.

关键词： Image segmentation

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