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SACNN-IDS: A self-attention convolutional neural network for intrusion detection in industrial internet of things

CAAI Transactions on Intelligence Technology 2024年第6期9卷 1398-1411页

作者： Mimonah Al Qathrady Safi Ullah Mohammed S.Alshehri Jawad Ahmad Sultan Almakdi Samar M.Alqhtani Muazzam A.Khan Baraq Ghaleb Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Department of Computer Science Quaid-i-Azam UniversityIslamabadPakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghUK ICESCO Chair Big Data Analytics and Edge Computing Quaid-i-Azam UniversityIslamabadPakistan

Industrial Internet of Things(IIoT)is a pervasive network of interlinked smart devices that provide a variety of intelligent computing services in industrial *** IIoT nodes operate confidential data(such as medical,transportation,military,etc.)which are reachable targets for hostile intruders due to their openness and varied *** Detection Systems(IDS)based on Machine Learning(ML)and Deep Learning(DL)techniques have got significant ***,existing ML and DL-based IDS still face a number of obstacles that must be *** instance,the existing DL approaches necessitate a substantial quantity of data for effective performance,which is not feasible to run on low-power and low-memory *** and fewer data potentially lead to low performance on existing *** paper proposes a self-attention convolutional neural network(SACNN)architecture for the detection of malicious activity in IIoT networks and an appropriate feature extraction method to extract the most significant *** proposed architecture has a self-attention layer to calculate the input attention and convolutional neural network(CNN)layers to process the assigned attention features for *** performance evaluation of the proposed SACNN architecture has been done with the Edge-IIoTset and X-IIoTID *** datasets encompassed the behaviours of contemporary IIoT communication protocols,the operations of state-of-the-art devices,various attack types,and diverse attack scenarios.

关键词： convolutional neural network deep learning industrial internet of things intrusion detection self-attention

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ScanSavant: Malware Detection for Android Applications with Explainable AI

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第19期18卷 171-181页

作者： Navaneethan, S. Udhaya Kumar, S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India

Mobile devices face SQL injection, malware, and web-based threats. Current solutions lack real-time detection. This paper introduces an Android app with advanced algorithms for real-time threat scanning. During testing, our application detected 94% of SQL injection attempts, outperforming the 86% average detection rate in similar studies. For malware analysis, it achieved a 97% detection accuracy on a dataset of infected files, higher than the industry standard of 93%. Additionally, our app can detect 85 malware variants and assign 15 attributes (***.8, ***, ***, ***, ***, ***, ***-2017-0143, ***, ***, Backdoor. DarkComet, ***, ***, ***, ***32. Necurs, and ***) to some malwares, providing detailed analysis for better threat management. The application effectively scans both EXE and APK files, ensuring com-prehensive protection. When assessing website links, the application identified security risks with 96% accuracy, demonstrating its capability in managing web-based threats. This app detects SQL injections, analyses malware, and assesses website security, bolstering cyber defence with user-friendly features and top-notch threat mitigation. © 2024, International Federation of Engineering Education Societies (IFEES). All rights reserved.

关键词： Android malware

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Automated detection of sugarcane crop lines from UAV images using deep learning

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Information Processing in Agriculture 2024年第3期11卷 385-396页

作者： Joao Batista Ribeiro Renato Rodrigues da Silva Jocival Dantas Dias Mauricio Cunha Escarpinati Andre Ricardo Backes School of Computer Science-Federal University of Uberlandia UberlandiaMGBrazil Department of Computing-Federal University of Sao Carlos Sao CarlosSPBrazil

UAVs(Unmanned Aerial Vehicles)have become increasingly popular in the agricultural sector,promoting and enabling the application of aerial image monitoring in both the scientific and business *** captured by UAVs are fundamental for precision farming *** enable us do a better crop planning,input estimates,early identification and correction of sowing failures,more efficient irrigation systems,among other *** all these activities deal with low or medium altitude images,automated identification of crop lines plays a crucial role improving these *** address the problem of detecting and segmenting crop *** use a Convolutional Neural Network to segment the images,labeling their regions in crop lines or unplanted *** also evaluated three traditional semantic networks:U-Net,LinkNet,and *** compared each network in four segmentation datasets provided by an *** also assessed whether the network’s output requires a post-processing step to improve the *** demonstrate the efficiency and feasibility of these networks in the proposed task.

关键词： Crop line Aerial Images CNN UAV Precision agriculture

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Parameter-efficient fine-tuning of pre-trained code models for just-in-time defect prediction

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Neural computing and Applications 2024年第27期36卷 16911-16940页

作者： Abu Talib, Manar Bou Nassif, Ali Azzeh, Mohammad Alesh, Yaser Afadar, Yaman Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering College of Computing and Informatics Sharjah United Arab Emirates King Hussein School for Computing Sciences Princess Sumaya University for Technology Amman Jordan

Software engineering workflows use version control systems to track changes and handle merge cases from multiple contributors. This has introduced challenges to testing because it is impractical to test whole codebases to ensure each change is defect-free, and it is not enough to test changed files alone. Just-in-time software defect prediction (JIT-SDP) systems have been proposed to solve this by predicting the likelihood that a code change is defective. Numerous techniques have been studied to build such JIT software defect prediction models, but the power of pre-trained code transformer language models in this task has been underexplored. These models have achieved human-level performance in code understanding and software engineering tasks. Inspired by that, we modeled the problem of change defect prediction as a text classification task utilizing these pre-trained models. We have investigated this idea on a recently published dataset, ApacheJIT, consisting of 44k commits. We concatenated the changed lines in each commit as one string and augmented it with the commit message and static code metrics. Parameter-efficient fine-tuning was performed for 4 chosen pre-trained models, JavaBERT, CodeBERT, CodeT5, and CodeReviewer, with either partially frozen layers or low-rank adaptation (LoRA). Additionally, experiments with the Local, Sparse, and Global (LSG) attention variants were conducted to handle long commits efficiently, which reduces memory consumption. As far as the authors are aware, this is the first investigation into the abilities of pre-trained code models to detect defective changes in the ApacheJIT dataset. Our results show that proper fine-tuning improves the defect prediction performance of the chosen models in the F1 scores. CodeBERT and CodeReviewer achieved a 10% and 12% increase in the F1 score over the best baseline models, JITGNN and JITLine, when commit messages and code metrics are included. Our approach sheds more light on the abilities of l

关键词： Forecasting

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The effect of interactive tutorial information and purpose built virtual chemistry laboratory on students’ performance

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Multimedia Tools and Applications 2025年第18期84卷 18873-18892页

作者： Ali, Numan Ullah, Sehat Department of Computer Science & IT University of Malakand Chakdara18800 Pakistan Department of Computer Games Development Faculty of Computing & Artificial Intelligence Air University Islamabad Islamabad44230 Pakistan

Different Virtual Chemistry Laboratories (VCLs) have been developed where users can perform their experimental work to increase their learning for hands-on chemistry experiments. The existing VCLs are playing a vital role in the enhancement of users’ performance, however, mental/cognitive load on users is a key issue in the existing VCLs. The issue of mental/cognitive load arises due to the complexity of the environment by displaying a number of chemicals, glass wares, and other lab equipments in the VCLs. In this paper, we present a new concept of performing experiments in a Purpose Built Virtual Chemistry Laboratory (PBVCL) with interactive tutorial information (ITI) to minimize mental/cognitive load on users and enhance their performance and theoretical knowledge about the experiment. PBVCL displays only those chemicals and glass wares which are necessary in the current experiment while hiding other unnecessary equipments. During evaluations, fourty-eight (48) students perform the experiments in two different groups using PBVCL and general VCL. Evaluations reveal that the proposed PBVCL provides less mental load and users can easily enhance their performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Glass

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Contemporary Study for Detection of COVID-19 Using Machine Learning with Explainable AI

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computers, Materials & Continua 2024年第7期80卷 1075-1104页

作者： Saad Akbar Humera Azam Sulaiman Sulmi Almutairi Omar Alqahtani Habib Shah Aliya Aleryani Department of Computer Science University of KarachiKarachi75270Pakistan Department of Computing Faculty of Engineering Science and TechnologyHamdard UniversityKarachi75540Pakistan Department of Health Informatics College of Applied Medical SciencesQassim UniversityQassim51452Saudi Arabia Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia

The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic *** this article,a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored dataset obtained from a private hospital for detecting COVID-19,pneumonia,and normal conditions in chest X-ray images(CXIs)is proposed coupled with Explainable Artificial Intelligence(XAI).Our study leverages less preprocessing with pre-trained cutting-edge models like InceptionV3,VGG16,and VGG19 that excel in the task of feature *** methodology is further enhanced by the inclusion of the t-SNE(t-Distributed Stochastic Neighbor Embedding)technique for visualizing the extracted image features and Contrast Limited Adaptive Histogram Equalization(CLAHE)to improve images before extraction of ***,an AttentionMechanism is utilized,which helps clarify how the modelmakes decisions,which builds trust in artificial intelligence(AI)*** evaluate the effectiveness of the proposed approach,both benchmark datasets and a private dataset obtained with permissions from Jinnah PostgraduateMedical Center(JPMC)in Karachi,Pakistan,are *** 12 experiments,VGG19 showcased remarkable performance in the hybrid dataset approach,achieving 100%accuracy in COVID-19 *** classification and 97%in distinguishing normal ***,across all classes,the approach achieved 98%accuracy,demonstrating its efficiency in detecting COVID-19 and differentiating it fromother chest disorders(Pneumonia and healthy)while also providing insights into the decision-making process of the models.

关键词： COVID-19 detection deep neural networks support vector machine CXIs InceptionV3 VGG16 VGG19 t-SNE embedding CLAHE attention mechanism XAI

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A Comprehensive Study of Resource Provisioning and Optimization in Edge computing

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computers, Materials and Continua 2025年第3期83卷 5037-5070页

作者： Bhaskaran, Sreebha Muthuraman, Supriya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru560035 India

Efficient resource provisioning, allocation, and computation offloading are critical to realizing low-latency, scalable, and energy-efficient applications in cloud, fog, and edge computing. Despite its importance, integrating Software Defined Networks (SDN) for enhancing resource orchestration, task scheduling, and traffic management remains a relatively underexplored area with significant innovation potential. This paper provides a comprehensive review of existing mechanisms, categorizing resource provisioning approaches into static, dynamic, and user-centric models, while examining applications across domains such as IoT, healthcare, and autonomous systems. The survey highlights challenges such as scalability, interoperability, and security in managing dynamic and heterogeneous infrastructures. This exclusive research evaluates how SDN enables adaptive policy-based handling of distributed resources through advanced orchestration processes. Furthermore, proposes future directions, including AI-driven optimization techniques and hybrid orchestration models. By addressing these emerging opportunities, this work serves as a foundational reference for advancing resource management strategies in next-generation cloud, fog, and edge computing ecosystems. This survey concludes that SDN-enabled computing environments find essential guidance in addressing upcoming management opportunities. Copyright © 2025 The Authors.

关键词： Computation offloading

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Machine Learning-Based Routing Protocol in Flying Ad Hoc Networks: A Review

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computers, Materials & Continua 2025年第2期82卷 1615-1643页

作者： Priyanka Manjit Kaur Deepak Prashar Leo Mrsic Arfat Ahmad Khan Department of Computer Science and Engineering(CSE) Lovely Professional UniversityPhagwara144411India Department for Program Engineering Algebra UniversityGradiscanska 24Zagreb10000Croatia Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

“Flying Ad Hoc Networks(FANETs)”,which use“Unmanned Aerial Vehicles(UAVs)”,are developing as a critical mechanism for numerous applications,such as military operations and civilian *** dynamic nature of FANETs,with high mobility,quick node migration,and frequent topology changes,presents substantial hurdles for routing protocol *** the preceding few years,researchers have found that machine learning gives productive solutions in routing while preserving the nature of FANET,which is topology change and high *** paper reviews current research on routing protocols and Machine Learning(ML)approaches applied to FANETs,emphasizing developments between 2021 and *** research uses the PRISMA approach to sift through the literature,filtering results from the SCOPUS database to find 82 relevant *** research study uses machine learning-based routing algorithms to beat the issues of high mobility,dynamic topologies,and intermittent connection in *** compared with conventional routing,it gives an energy-efficient and fast decision-making solution in a real-time environment,with greater fault tolerance *** protocols aim to increase routing efficiency,flexibility,and network stability using ML’s predictive and adaptive *** comprehensive review seeks to integrate existing information,offer novel integration approaches,and recommend future research topics for improving routing efficiency and flexibility in ***,the study highlights emerging trends in ML integration,discusses challenges faced during the review,and discusses overcoming these hurdles in future research.

关键词： FANET protocol machine learning

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Semi-supervised learning based hybrid beamforming under time-varying propagation environments

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Digital Communications and Networks 2024年第4期10卷 1168-1177页

作者： Yin Long Hang Ding Simon Murphy School of Computer Science and Technology Southwest University of Science and TechnologyMianyang621000China Department of Computing Science University of AlbertaEdmontonT6G 2R3Canada

Hybrid precoding is considered as a promising low-cost technique for millimeter wave(mm-wave)massive Multi-Input Multi-Output(MIMO)*** this work,referring to the time-varying propagation circumstances,with semi-supervised Incremental Learning(IL),we propose an online hybrid beamforming ***,given the constraint of constant modulus on analog beamformer and combiner,we propose a new broadnetwork-based structure for the design model of hybrid *** with the existing network structure,the proposed network structure can achieve better transmission performance and lower ***,to enhance the efficiency of IL further,by combining the semi-supervised graph with IL,we propose a hybrid beamforming scheme based on chunk-by-chunk semi-supervised learning,where only few transmissions are required to calculate the label and all other unlabelled transmissions would also be put into a training data *** the existing single-by-single approach where transmissions during the model update are not taken into the consideration of model update,all transmissions,even the ones during the model update,would make contributions to model update in the proposed *** the model update,the amount of unlabelled transmissions is very large and they also carry some information,the prediction performance can be enhanced to some extent by these unlabelled channel *** results demonstrate the spectral efficiency of the proposed method outperforms that of the existing single-by-single ***,we prove the general complexity of the proposed method is lower than that of the existing approach and give the condition under which its absolute complexity outperforms that of the existing approach.

关键词： Hybrid beamforming Time-varying environments Broad network Semi-supervised learning Online learning

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Privacy-preserving human activity sensing:A survey

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High-Confidence computing 2024年第1期4卷 108-117页

作者： Yanni Yang Pengfei Hu Jiaxing Shen Haiming Cheng Zhenlin An Xiulong Liu School of Computer Science and Technology Shandong UniversityQingdao 266237China Department of Computing and Decision Sciences Lingnan UniversityHong KongChina Department of Computing The Hong Kong Polytechnic UniversityHong KongChina Department of Computer Science Princeton UniversityNJ 08544USA College of Intelligence and Computing Tianjin UniversityTianjin 300350China

With the prevalence of various sensors and smart devices in people’s daily lives,numerous types of information are being *** using such information provides critical and convenient services,we are gradually exposing every piece of our behavior and *** are aware of the privacy risks and have been working on preserving privacy while sensing human *** survey reviews existing studies on privacy-preserving human activity *** first introduce the sensors and captured private information related to human *** then propose a taxonomy to structure the methods for preserving private information from two aspects:individual and collaborative activity *** each of the two aspects,the methods are classified into three levels:signal,algorithm,and ***,we discuss the open challenges and provide future directions.

关键词： Human activity sensing Privacy-preserving sensing Activity sensing algorithms Human sensors Privacy protection

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