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Malware Attacks Detection in ioT Using Recurrent Neural Network(RNN)

intelligent Automation & Soft Computing 2024年第2期39卷 135-155页

作者： Abeer Abdullah Alsadhan Abdullah A.Al-Atawi Hanen karamti Abid Jameel islam Zada Tan N.Nguyen Computer Science Department Imam Abdulrahman Bin Faisal UniversityDamam32232Saudi Arabia Department of Computer Science Applied CollegeUniversity of TabukTabuk47512Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences and Information Technology Hazara UniversityMansehraPakistan Faculty of Computing Department of Software EngineeringInternational Islamic UniversityIslamabadPakistan Department of Architectural Engineering Sejong UniversitySeoul05006Korea

ioT(internet of Things)devices are being used more and more in a variety of businesses and for a variety of tasks,such as environmental data collection in both civilian and military *** are a desirable attack target for malware intended to infect specific ioT devices due to their growing use in a variety of applications and their increasing computational and processing *** this study,we investigate the possibility of detecting ioT malware using recurrent neural networks(RNNs).RNNis used in the proposed method to investigate the execution operation codes of ARM-based internet of Things apps(OpCodes).To train our algorithms,we employ a dataset of ioT applications that includes 281 malicious and 270 benign pieces of *** trained model is then put to the test using 100 brand-new ioT malware samples across three separate LSTM *** exposure was not previously conducted on these *** newly crafted malware samples with 2-layer neurons had the highest accuracy(98.18%)in the 10-fold cross validation experiment.A comparison of the LSTMtechnique to other machine learning classifiers shows that it yields the best results.

关键词： Malware malicious code code obfuscation ioT machine learning deep learning

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Secure digital databases using watermarking based on english-character attributes

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Recent Advances in computer science and Communications 2020年第3期13卷 477-481页

作者： Khatatneh, Khalaf Odeh, Ashraf Mashaleh, Ashraf Hamadeen, Hind Department of Computer Science Al Balqa Applied University As-Salt Jordan Department of Science and Technology Computer Quality Society Jordan Computer Center Al Balqa Applied University As-Salt Jordan Department of Software Engineering Al-Balqa Applied University As-Salt Jordan

introduction: The single space and the double space (DS). in this procedure, an image is used to watermark a digital database, where the image bytes are divided into binary strings that block the text attributes of the selected database, we proposed an algorithm to defend against four common database attacks. Objective: Perform the watermark is Embedding and makes extraction of the watermark. We also describe the principal of the Embedding and extraction the watermark. Methods: The procedure to extract the watermark does not require knowledge of the original database that does not carry the same watermark. This feature is extremely important because it allows the discovery of a watermark in a copy of the original database, regardless of the subsequent updates to the asset. The extraction procedure is a direct reflection of the procedure used to embed the watermark is six steps. Results: Using new algorithm ability to develop a database watermark that would make it difficult for an attacker to remove or change the watermark without discovering the value of the object. To be judged effective, the database algorithm had to be able to create a strong enough watermark that could sustain the security of the database in the face of the following four types of attack: deletion of a sub-dataset, addition of a sub-dataset. Conclusion: The performance of the proposed algorithm was assessed in respect of its ability to defend the database against four common attacks for all tuples selection. © 2020 Bentham science Publishers.

关键词： Database systems

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3D Head Pose Estimation through Facial Features and Deep Convolutional Neural Networks

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computers, Materials & Continua 2021年第2期66卷 1757-1770页

作者： Khalil Khan Jehad Ali Kashif Ahmad Asma Gul Ghulam Sarwar Sahib Khan Qui Thanh Hoai Ta Tae-Sun Chung Muhammad Attique Department of Computer Science and Software Engineering Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur-KPKPakistan Department of Computer Engineering and Department of AI Convergence Network Ajou UniversitySuwon16499South Korea Hamad Bin Khalifa University DohaQatar Department of Statistics Shaheed Benazir Bhutto Women UniversityPeshawarPakistan Department of Software Engineering University of Azad Jammu and KashmirPakistan Department of Electronics and Telecommunications Politecnico di TorinoTorino10129Italy Institute of Research and Development Duy Tan UniversityDanang550000Vietnam Department of Computer Engineering Ajou UniversitySuwon16499Korea Department of Software Sejong UniversitySeoul05006South Korea

Face image analysis is one among several important cues in computer *** the last five decades,methods for face analysis have received immense attention due to large scale applications in various face analysis *** parsing strongly benefits various human face image analysis tasks inducing face pose *** this paper we propose a 3D head pose estimation framework developed through a prior end to end deep face parsing *** have developed an end to end face parts segmentation framework through deep convolutional neural networks(DCNNs).For training a deep face parts parsing model,we label face images for seven different classes,including eyes,brows,nose,hair,mouth,skin,and *** extract features from gray scale images by using *** train a classifier using the extracted *** use the probabilistic classification method to produce gray scale images in the form of probability maps for each dense semantic *** use a next stage of DCNNs and extract features from grayscale images created as probability maps during the segmentation *** assess the performance of our newly proposed model on four standard head pose datasets,including Pointing’04,Annotated Facial Landmarks in the Wild(AFLW),Boston University(BU),and iCT-3DHP,obtaining superior results as compared to previous results.

关键词： Face image analysis face parsing face pose estimation

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Robust Feature engineering Techniques for Designing Motor imagery-Based Bci Systems

SSRN

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SSRN 2025年

作者： Gardezi Ahmed, Syed Saim Khan, Mahnoor Bukhari, Syeda Muneeba Khan, Rizwan Ahmed Jawed, Soyiba Department of Biomedical Engineering Faculty of Engineering Salim Habib University Karachi Pakistan Department of Computer Software Engineering College of Electrical and Mechanical Engineering NUST Islamabad Pakistan Department of Physics Faculty of Applied Sciences Riphah International University Islamabad Pakistan Department of Biomedical Engineering Faculty of Engineering NED University Karachi Pakistan Department of Computer Science Faculty of Information Technology Salim Habib University Karachi Pakistan

A multitude of individuals across the globe grapple with motor disabilities. Neural prosthetics utilizing Brain-computer interface (BCi) technology exhibit promise for improving motor rehabilitation outcomes. The intricate nature of EEG data poses a significant hurdle for current BCi systems. Recently, a qualitative repository of EEG signals tied to both upper and lower limb execution of motor and motor imagery tasks has been unveiled. Despite this, the productivity of the Machine Learning (ML) Models that were trained on this dataset was alarmingly deficient, and the evaluation framework seemed insufficient. To enhance outcomes, robust feature engineering (signal processing) methodologies are implemented. A collection of time domain, frequency domain, and wavelet-derived features was obtained from 16-channel EEG signals, and the Maximum Relevance Minimum Redundancy (MRMR) approach was employed to identify the four most significant features. For classification K Nearest Neighbors, Support Vector Machine, Decision Tree, and Naive Bayes models were implemented with these selected features, evaluating their effectiveness through metrics such as testing accuracy, precision, recall, and F1 Score. By leveraging SVM with a Gaussian Kernel, a remarkable maximum testing accuracy of 92.50% for motor activities and 95.48% for imagery activities is achieved. These results are notably more dependable and gratifying compared to the previous study, where the peak accuracy was recorded at 74.36%. This research work provide an in-depth analysis of the Mi Limb EEG dataset and it will help in designing and developing simple, cost-effective and reliable BCi systems for neuro-rehabilitation. © 2025, The Authors. All rights reserved.

关键词： image enhancement

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Deep Learning Approach for COViD-19 identification 18

Deep Learning Approach for COVID-19 Identification

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18th international computer Conference on Wavelet Active Media Technology and information Processing, iCCWAMTiP 2021

作者： Ul Haq, Amin Li, Jian Ping Khan, Riaz Ullah Mawuli, Cobbinah Bernard Agbley, Bless Lord Y. Raj, Mordecai F. Zhou, Wang Khan, Jalaluddin Haq, Abdul Saboor, Abdus Habib, Faiza Khan, Zafar University of Electronic Science and Tech. of China School of Computer Science and Engineering Chengdu China 313000 China Xihua University School of Computer and Software Engineering Chengdu Sichuan China University of Electronic Science and Tech. of China School of Computer Science and Engineering Chengdu China Gomal University Department of Computer Science D.I. Khan29220 Pakistan Hazzra University Department of Computer Science and IT Manshera KPK 21120 Pakistan Northern University Faculty of Engineering and I.T Nowshera Pakistan

ISBN: (纸本)9781665413640

Accurate diagnostic system is significantly important for timely COViD-19 identification. Diagnosing COViD-19 from chest x-ray images employing the CNN model is recommended for accurate recognition of COViD-19. The existing diagnosis techniques of COViD-19 still lack high accuracy. To handle this problem in this work, we have proposed accurate detection method for COViD-19. in the proposed method, a CNN is incorporated for the diagnosis of COViD-19 using chest x-ray images data. The experimental results illustrate that our technique is good for COViD-19 accurate diagnosis and can be easily implemented in health care systems. © 2021 iEEE.

关键词： COViD-19

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MURDOC: Transforming Pixels into Perception for Camouflage Detection

MURDOC: Transforming Pixels into Perception for Camouflage D...

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Conference on Digital Avionics Systems (DASC)

作者： Debra Hogue Zak Kastl Joseph Karch Don Nguyen Lacey Schley Justin Lewis Shane Connelly Chris Weaver School of Computer Science at the University of Oklahoma. US Air Force's 76th Software Engineering Group. Oklahoma Aerospace and Defense Innovation Institute at the University of Oklahoma. Department of Psychology and Center for Applied Social Research the University of Oklahoma School of Computer Science at the University of Oklahoma

ISBN: (数字)9798350349610

ISBN: (纸本)9798350349627

The MURDOC project introduces an application to enhance trustworthiness and explainability in computer vision models, focusing on camouflage detection. it aims to address the need for transparent and interpretable Ai systems in sensitive domains such as security and defense. MURDOC integrates advanced eXplainable Ai techniques including off-ramps for collecting interpretable insights, attention mechanisms for high-lighting relevant features, and a user-centric image pre-processing tool within the visualization interface. This paper offers an assessment of MURDOC's potential impact on trustworthiness and explainability in camouflaged object detection tasks. it discusses and assesses the potential effectiveness of off-ramps in gathering XAi output at various model stages, such as feature maps, attention mechanisms, and activation maps. Additionally, the paper investigates how user-driven image pre-processing mechanisms may enhance the trustworthiness of the model's predictions and decisions, allowing users to modify the input and observe prediction changes. As a work in progress, the development of MURDOC shows promise in bridging the gap for transparency and interpretability in camouflage detection. The paper also discusses the challenges, future directions, and potential applications of MURDOC's capabilities in diverse domains requiring transparent and trustworthy Ai systems. The source code for the version of MURDOC in this paper is available on GitHub at: https://***/Department-70/DASC_2024.

关键词： Visualization Attention mechanisms Uncertainty Source coding Buildings Security Artificial intelligence Graphical user interfaces Testing software development management

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A Two-stage Anomaly Detection System for Fog Computing Services

A Two-stage Anomaly Detection System for Fog Computing Servi...

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iEEE international Conference on Emerging & Sustainable Technologies for Power & iCT in a Developing Society (NiGERCON)

作者： Sunday Oyinlola Ogundoyin ismaila Adeniyi Kamil isaac Adewale Ojedokun John Oluwaseun Babalola Vincent Omollo Nyangaresi Electrical and Electronic Engineering Programme Bowen University Iwo Nigeria Department of Electrical and Electronic Engineering University of Ibadan Ibadan Nigeria Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and Technology Bando Kenya Department of Applied Electronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Fog computing enhances traditional cloud computing by bringing resources closer to edge devices, enabling faster response times and supporting mission-critical applications. However, its distributed nature makes it vulnerable to cyber-attacks like distributed denial-of-service (DDoS), resulting in service disruption and performance degradation. To mitigate this attack, a two-stage anomaly detection system (ADS) is proposed in this paper. The first stage uses AdaBoost (AB) ensemble machine learning to classify events as obstructive or non-obstructive. Obstructive events are offloaded to the cloud where they are classified into normal or specific attack categories using Long Short-term Memory Encoder-Decoder (LSTM-ED). The results demonstrate that the AB-based model achieves exceptional performance, with nearly 100% in accuracy, precision, recall, and a minimal mean squared error (MSE) of 8.34 × 10 −5 . The LSTM-ED model also performs well, with accuracy and MSE at 99.50% and 0.02686, respectively.

关键词： Performance evaluation Cloud computing Accuracy Computational modeling Mission critical systems Machine learning Time factors Long short term memory Anomaly detection Edge computing

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An Anomaly Detection System Based on Clustering and Fuzzy Set Theory in VANETs 8th

An Anomaly Detection System Based on Clustering and Fuzzy Se...

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8th international Workshop on Soft Computing Applications, SOFA 2018

作者： Kuchaki Rafsanjani, Marjan Fatemidokht, Hamideh Balas, Valentina Emilia Batth, Ranbir Singh Department of Computer Science Faculty of Mathematics and Computer Shahid Bahonar University of Kerman Kerman Iran Department of Applied Mathematics Young Research Society Shahid Bahonar University of Kerman Kerman Iran Department of Automatics and Applied Software Faculty of Engineering Aurel Vlaicu University of Arad Arad Romania School of Computer Science and Engineering Lovely Professional University Jalandhar India

ISBN: (纸本)9783030521899

in vehicular ad hoc network (VANET), which is considered as a type of mobile ad hoc network (MANET), communication occurs between neighbor vehicles and also roadside equipment and vehicles. Due to the characteristics of VANET, there are chances of a number possible attack in this network. in this paper, an anomaly detection system that uses clustering and fuzzy set theory to defend the Denial of Service (DoS) attack is presented. Results of simulation represent that the proposed algorithm arrives to the high detection rate. © 2021, Springer Nature Switzerland AG.

关键词： Fuzzy set theory

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SDCM: An Efficient Camellia Oleifera Fruits Detection Algorithm in Dense Occlusion Scenes

SDCM: An Efficient Camellia Oleifera Fruits Detection Algori...

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Computing and Big Data (iCCBD), international Conference on

作者： Youyang Zhang Qiuxia Chen Wenhua Cui Jinfeng Yang School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China Institute of Applied Artificial Intelligence of the Guang-dong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic Shenzhen China

ISBN: (数字)9798350317008

ISBN: (纸本)9798350317015

To realize intelligent management in the Camellia oleifera fruit industry and foster the efficient growth of its supply chain, the accurate identification and detection of Camellia oleifera fruits are essential. However, in natural environments, these fruits exhibit challenges such as clustering, mutual occlusion, and obstruction by leaves. This paper proposes a Camellia oleifera fruits detection algorithm based on an improved YOLOv7 deep convolutional neural network, which is referred to as the SDCM network model, and add the function of counting the number of fruits. We introduce an enhancement to the backbone layer of the YOLOv7 model by incorporating a module called SDCM (Shortcut and Dilation Convolution based Module). This module enriches multiscale edge information, enabling the network to extract features more effectively, thus improving detection accuracy. The experimental results show that the detection accuracy of the SDCM network model is significantly improved compared with the original YOLOv7 model, and the detection performance of the model in this paper is superior compared with other classical detection models. By enhancing the detection accuracy of the model and adding the function of fruit counting, the model in this paper can provide more reliable and accurate detection performance and the function of predicting fruit yield for the development of the Camellia oleifera fruit industry, improve the production efficiency, and reduce the artificial resources.

关键词： industries Accuracy image edge detection Supply chains Production Predictive models Feature extraction

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Playing the video games during COViD-19 pandemic and its effects on player's well-being

Playing the video games during COVID-19 pandemic and its eff...

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Business Analytics for Technology and Security (iCBATS), international Conference on

作者： Samar Billi Noaman Amer ibrahim Liaqat Ali M. W. iqbal Asma Ashraf Usama Haseeb Salman Muneer Rasah Almajed K. Hamid Faculty of Management Canadian University Dubai Dubai UAE College of Computer and Information Technology American University in the Emirates Dubai United Arab Emirates College of Engineering and Technology University of Science and Technology of Fujairah Fujairah UAE Department of Software Engineering Superior University Lahore Pakistan Department of Software Engineering Superior University Lahore Pakistan Department of Computer Science Superior University Lahore Pakistan School of Computer Science NCBA&E Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan College of Computer and Information Technology American University in the Dubai United Arab Emirates

COViD-19 affected our lives intensly. That state of affairs made humans helpless. They had been depressed and experienced loneliness. At that time many human beings were determined to play video games just to loosen up their minds. Many games changed into an additional source of revenue wherein during COViD-19 they were playing while earning money. With these advantages, there were also some poor effects was accrued. A number of players remained playing video games post COViD-19. The carried out survey is based on the social video games results on players’ well-being and additionally on the effects of gamers’ health and their sensible lifestyles. We are going to investigate the behavior of gamers engaged with video games during the COViD-19 lockdown and the video games affects on their well-being, the time they served in playing video games, and the consequential effect on their behavior and social and mental well-being. The results provide a start line for empirically grounded discussions on video games at some stage in the pandemic, their use, and potential outcomes. Different agegroups of players have been investigated. Most players are between 18 and 30 years. A number of the gamers during lock down played a few hours but most of players were males who spent most of their day playing video games. However, now the ratio of playing video games is reduced as examined with past circumstances. Roughly we can say that the condition as a whole is better, the reason why players enforced video games in their post COViD-19 practical life 1 .

关键词： COViD-19 Video games Pandemics Games Machine learning Behavioral sciences Security

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