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International Conference on Computing and Machine Learning, CML 2024

作者： Alhindi, Ahmad Computer Science and Artificial Intelligence Department College of Computer and Information Systems Makkah Saudi Arabia

ISBN: (纸本)9789819778386

Facing the growing menace of distributed denial of service (DDoS) attacks, there’s a critical demand for enhanced detection mechanisms. This necessitates a shift from traditional methodologies to more advanced, dynamic detection techniques. The significance of advanced computational methods in pinpointing DDoS threats is increasingly recognized. This paper delves into the crucial role these techniques play in detecting DDoS activities, with a focus on the adaptability of network-based algorithms and the proficiency of advanced models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and autoencoders in identifying complex attack patterns that traditional methods may overlook. Our findings reveal that while advanced computational strategies offer considerable benefits over conventional methods in terms of detection accuracy and adaptability to evolving threats, they also present unique challenges, including higher computational demands and the need for continuous model training to keep pace with new attack vectors. The comparative analysis underscores the operational advantages and potential limitations of these approaches, providing a nuanced understanding of their application in real-world scenarios. This investigation aims to present a current view of advancements in digital security techniques and to serve as a basis for future research and the development of innovative, practical solutions. It suggests that enhancing the resilience of digital infrastructures against DDoS attacks will require not only technological innovation but also a comprehensive strategy that includes ongoing research, collaboration among stakeholders, and the adoption of adaptive, scalable detection systems capable of responding to the dynamic nature of cyber threats. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Optimizing Quality-of-Service (QoS) using Semantic Sensing and Digital-Twin in Pro-dynamic Internet of Vehicles (IoV) 100

Optimizing Quality-of-Service (QoS) using Semantic Sensing a...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Rawlley, Oshin Gupta, Shashank Bhattacharyya, Tanisha Singh, Shikhar Bits Pilani Department of Computer Science & Information Systems Rajasthan India

ISBN: (纸本)9798331517786

The emerging autonomous driving industry expects real-time information to be communicated in less amount of time. Most of the extant research works on deterministic or stochastic channels, which are deemed unrealistic for pro-dynamic Internet of Vehicles (IoV) communications. Semantic communication provides a novel concept of serving high-mobility vehicles with faster vehicular communications by using digital twin (DT) technology. However, the low-latency demand, intermittent connectivity, and signal attenuation in the IoV canyon pose big challenges. To facilitate the efficient functioning of Intelligent Transport systems (ITS) applications, we integrate DT, which is a co-simulation of software such as CARLA, SUMO, python, etc., to improve the semantic communication and quality of service (QoS) of the IoV scenario. Further, we have formulated a vehicular sensing and computation model that incorporates system cost and DT migration cost as their key metrics to evaluate the QoS of the system. We have proposed a pro-dynamic algorithm based on digital-twin deep reinforcement learning (DT-DRL) to decode the QoS maximization problem. Numerical results reveal the superiority of our method by decreasing the cost of the system and improving latency, maintaining the semantic real-time communication in IoV. © 2024 IEEE.

关键词： Stochastic systems

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TinyML for the Detection of Plant Diseases in Resource-Constrained Areas within West Africa 9

TinyML for the Detection of Plant Diseases in Resource-Const...

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9th IEEE International Conference on Adaptive science and Technology, ICAST 2024

作者： Ibegbu, Chinwe Korsah, G. Ayorkor Ashesi University Computer Science and Information Systems Department Berekuso Ghana

ISBN: (纸本)9798350385403

Most of West Africa's rural farmers do not have access to smartphones, a stable internet connection or stable electricity - additionally, they often have low English proficiency and technological competency. Subsistence farmers in the Akuapim-South Municipal Assembly of Ghana face the same reality but have the added problem of limited access to agricultural extension agents who provide plant disease identification services. While many technological solutions to plant disease identification exist, they are cloud-based and, thus, require resources not accessible to a rural farmer in this population. This study investigates the use of Tiny Machine Learning (TinyML), a subset of machine learning that is focused on deployment to microcontrollers and hence facilitates low latency, low power, low cost, and high privacy computing. While this technology has been proven comparable to cloud-based Machine Learning, there is a lack of research regarding its applicability to both the area of plant disease identification and the West African context. To fill this gap, this study develops a TinyML-based system and compares it to an existing cloud-based plant disease identification mobile app. The results of this study suggest TinyML has promising performance although it falls short of the performance of the cloud-based solution in this study. A significant advantage of the TinyML system is on-device inference without the need for internet connectivity. Going forward, a combination of cloud-based model training and on-device inference is suggested as the most suitable ML setup to address this problem for this population. This study also identifies some areas for improvement and further research, such as the creation of a context-specific dataset, expanded experiment scope, and the use of alternative TinyML development platforms. © 2024 IEEE.

关键词： Cloud platforms

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Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

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computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq Saad Arif Zohaib Mushtaq Muhammad Irfan Ghulam Abbas Arshad Ali Alanoud Al Mazroa School of Computer Information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering The University of LahoreLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

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Advanced Leaf Classification using Multi-Layer Perceptron for Smart Crop Management

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IEEE Access 2025年 13卷 105579-105589页

作者： Mumtaz, Sara Alshehri, Mohammed AlQahtani, Yahya Alshahrani, Abdulmonem Alabdullah, Bayan Alhasson, Haifa F. Liu, Hui Air University Department of Computer Science Islamabad44000 Pakistan King Khalid University Department of Computer Science Abha61421 Saudi Arabia King Khalid University Department of Informatics and Computer Systems Abha61421 Saudi Arabia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems P.O. Box 84428 Riyadh11671 Saudi Arabia Qassim University College of Computer Department of Information Technology Buraydah52571 Saudi Arabia University of Bremen Cognitive Systems Lab Bremen28359 Germany

Accurate leaf classification plays a vital role in ecological studies and precision agriculture, yet the diversity in leaf morphology and environmental influences presents a significant challenge. To improve accuracy, this study suggests a revolutionary methodology that combines sophisticated preprocessing, segmentation, feature extraction, optimization, and classification algorithms. To improve image quality, preprocessing is first done with bilateral filtering and Contrast Limited Adaptive Histogram Equalization (CLAHE). Then, to ensure accurate border identification, Conditional Random Fields (CRF) are used for segmentation. To extract features, complex texture and shape details are captured using Local Ternary Patterns (LTP), KAZE, and Histogram of Oriented Gradients (HOG). The optimization method, Stochastic Gradient Descent (SGD), refines the feature space for better categorization. Finally, a Multi-Layer Perceptron (MLP) classifier is used, and it produces remarkable results on three different datasets: accuracy of 92.63%, 88.3%, and 94.5%, respectively. This strong architecture shows great promise for disease monitoring and plant identification, providing a flexible and scalable solution for a range of agricultural applications. © 2013 IEEE.

关键词： Feature Selection

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Concept Drift Analysis and Malware Attack Detection System Using Secure Adaptive Windowing

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computers, Materials & Continua 2023年第5期75卷 3743-3759页

作者： Emad Alsuwat Suhare Solaiman Hatim Alsuwat Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif26571Saudi Arabia Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah24382Saudi Arabia

Concept drift is a main security issue that has to be resolved since it presents a significant barrier to the deployment of machine learning(ML)*** to attackers’(and/or benign equivalents’)dynamic behavior changes,testing data distribution frequently diverges from original training data over time,resulting in substantial model *** to their dispersed and dynamic nature,distributed denial-of-service attacks pose a danger to cybersecurity,resulting in attacks with serious consequences for users and *** paper proposes a novel design for concept drift analysis and detection of malware attacks like distributed Denial of Service(DDOS)in the *** goal of this architecture combination is to accurately represent data and create an effective cyber security prediction *** intrusion detection system and concept drift of the network has been analyzed using secure adaptive windowing with website data authentication protocol(SAW_WDA).The network has been analyzed by authentication protocol to avoid malware *** data of network users will be collected and classified using multilayer perceptron gradient decision tree(MLPGDT)*** on the classification output,the decision for the detection of attackers and authorized users will be *** experimental results show output based on intrusion detection and concept drift analysis systems in terms of throughput,end-end delay,network security,network concept drift,and results based on classification with regard to accuracy,memory,and precision and F-1 score.

关键词： Concept drift machine learning DDOS cyber security SAW_WDA MLPGDT

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Hand Gesture Classification for the Deaf and Mute Using the DenseNet169 Model 9

Hand Gesture Classification for the Deaf and Mute Using the ...

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9th International Conference on Communication and Electronics systems, ICCES 2024

作者： Dabwan, Basel A. Gazzan, Mazen Ismil, Omar A. Farah, Enaam A. Almula, Soad M. Ali, Yahya A. AlBaydha University Department of Information System AlBaydha Yemen College of Computer Science and Information Systems Najran University Department of Information System Saudi Arabia Najran University Department of Information System Saudi Arabia Najran University Department of Computer Science Saudi Arabia College of Computer Science and Information Systems Najran University Department of Computer Science Saudi Arabia Collage of Computer Science and Information Systems Najran University Department of Information System Saudi Arabia

ISBN: (纸本)9798350377972

Sign language is a crucial means of communication for people who are deaf or hard of hearing, relying on specific signs, hand shapes, gestures, and facial expressions to express ideas and information. Since many deaf individuals face challenges with verbal communication, reading, and writing, developing a Sign Language Recognition (SLR) system is crucial for enhancing their ability to interact with hearing individuals. This paper presents a solution using the DenseNet169 model to recognize American Sign Language (ASL) gestures. We utilized a dataset representing the ASL alphabet, consisting of 24 classes (excluding the letters J and Z, which involve movement), with a The dataset contains 27,455 samples for training and 7,172 samples for testing. It was divided such that 80% of the data was used for training and 20% for testing. The proposed model has achieved impressive results, with a validation accuracy of 98%, highlighting its potential to significantly enhance communication for the hearing-impaired community. This work contributes to the advancement of human-computer interaction by providing an effective tool for recognizing sign language gestures and facilitating communication. © 2024 IEEE.

关键词： Human computer interaction

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A Survey on Symbolic Knowledge Distillation of Large Language Models

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 5928-5948页

作者： Acharya, Kamal Velasquez, Alvaro Song, Houbing Herbert Department of Information Systems BaltimoreMD21250 United States University of Colorado Department of Computer Science BoulderCO80309 United States

This survey article delves into the emerging and critical area of symbolic knowledge distillation in large language models (LLMs). As LLMs such as generative pretrained transformer-3 (GPT-3) and bidirectional encoder representations from transformers (BERT) continue to expand in scale and complexity, the challenge of effectively harnessing their extensive knowledge becomes paramount. This survey concentrates on the process of distilling the intricate, often implicit knowledge contained within these models into a more symbolic, explicit form. This transformation is crucial for enhancing the interpretability, efficiency, and applicability of LLMs. We categorize the existing research based on methodologies and applications, focusing on how symbolic knowledge distillation can be used to improve the transparency and functionality of smaller, more efficient artificial intelligence (AI) models. The survey discusses the core challenges, including maintaining the depth of knowledge in a comprehensible format, and explores the various approaches and techniques that have been developed in this field. We identify gaps in current research and potential opportunities for future advancements. This survey aims to provide a comprehensive overview of symbolic knowledge distillation in LLMs, spotlighting its significance in the progression toward more accessible and efficient AI systems. © 2024 IEEE.

关键词： Distillation

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Suicide risk assessment using word-level model with dictionary-based risky posts selection

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Multimedia Tools and Applications 2024年第7期83卷 21435-21454页

作者： Tsai, Yun Sheng Chen, Arbee L. P. Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan

Suicide is a serious issue around the world and is a leading cause of death in US. In the past 20 years, the suicide rate has seen a significant increase of 35%. With the rapid development of information technology, more and more people begin to use social media to share their inner feelings. It enables social media data to be widely used for research on suicide risk assessment. However, not all social media posts are suicide related. Previous research addressed this problem with post-level attention mechanism. However, post-level attention mechanism may not find relevant suicide posts. This problem becomes more serious in the feature-based post embeddings since each post is converted into a single vector to serve as the input of the model, resulting in the loss of word-level information during training. In this paper, we addressed this problem by introducing a novel word-level model including a post-selectin layer as a solution. Firstly, we utilize a suicide keyword dictionary to identify risky posts that may be missed by the post-level attention mechanism. We then convert the words in the risky posts into word embeddings and use self-attention to generate the post embeddings for the risky posts. Finally, we pass the post embeddings to a multilayer perceptron to classify the suicide risk. We also demonstrate that the FScore used in previous studies can be reduced to a function of accuracy, which does not reflect the model performance in predicting imbalanced datasets. Therefore, we additionally adopt macro F1 score as the evaluation function. Experiment results show that our model not only outperforms previous studies in FScore performance, but also achieves macro F1 Score a nearly 4% improvement compared to previous studies. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Embeddings

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AI-powered dining: text information extraction and machine learning for personalized menu recommendations and food allergy management

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International Journal of information Technology (Singapore) 2025年第4期17卷 2107-2115页

作者： Brahimi, Samiha Department of Computer Information Systems College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam 31441 Saudi Arabia

Individuals with food allergies face limitations in social events and restaurant dining. Artificial intelligence solutions should be offered to this category. In this paper, a recommender system is proposed for the benefit of people with food allergies. The system aims to identify convenient options for the user in a restaurant/hotel menu. The system collects user’s allergy information and the restaurant menu, it extracts dishes names using a machine learning model. Then it conducts search about the recipes of these dishes and identify allergen-free ones. The system has been implemented as a mobile application involving a Naïve Bayes classification model and a web search API. The performance of the classifier was significant (accuracy 87%). Yet, an enhancement approach was introduced to increase the accuracy to 90%. In addition, an expert-driven test has been conducted and 98.5% of the system allergen identification was accurate in comparison with the original recipes used by restaurants’ chefs. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： information retrieval Machine learning Menu recommendation Recommender systems

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