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Explainable Classification Model for Android Malware Analysis Using API and Permission-Based Features

Computers, Materials & Continua 2023年第9期76卷 3167-3188页

作者： Nida Aslam Irfan Ullah Khan Salma Abdulrahman Bader Aisha Alansari Lama Abdullah Alaqeel Razan Mohammed Khormy Zahra Abdultawab AlKubaish Tariq Hussain saudi aramco cybersecurity chair Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Computer Engineering Department College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia School of Computer Science and Technology Zhejiang Gongshang UniversityHangzhouChina

One of the most widely used smartphone operating systems,Android,is vulnerable to cutting-edge malware that employs sophisticated logic.Such malware attacks could lead to the execution of unauthorized acts on the victims’devices,stealing personal information and causing hardware damage.In previous studies,machine learning(ML)has shown its efficacy in detecting malware events and classifying their types.However,attackers are continuously developing more sophisticated methods to bypass detection.Therefore,up-to-date datasets must be utilized to implement proactive models for detecting malware events in Android mobile devices.Therefore,this study employed ML algorithms to classify Android applications into malware or goodware using permission and application programming interface(API)-based features from a recent dataset.To overcome the dataset imbalance issue,RandomOverSampler,synthetic minority oversampling with tomek links(SMOTETomek),and RandomUnderSampler were applied to the Dataset in different experiments.The results indicated that the extra tree(ET)classifier achieved the highest accuracy of 99.53%within an elapsed time of 0.0198 s in the experiment that utilized the RandomOverSampler technique.Furthermore,the explainable Artificial Intelligence(EAI)technique has been applied to add transparency to the high-performance ET classifier.The global explanation using the Shapely values indicated that the top three features contributing to the goodware class are:Ljava/net/URL;->openConnection,Landroid/location/LocationManager;->getLastKgoodwarewnLocation,and Vibrate.On the other hand,the top three features contributing to themalware class are Receive_Boot_Completed,Get_Tasks,and Kill_Background_Processes.It is believed that the proposedmodel can contribute to proactively detectingmalware events in Android devices to reduce the number of victims and increase users’trust.

关键词： Android malware machine learning malware detection explainable artificial intelligence cyber security

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Optimal Deep Learning Model Enabled Secure UAV Classification for Industry 4.0

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Computers, Materials & Continua 2023年第3期74卷 5349-5367页

作者： Khalid A.Alissa Mohammed Maray Areej A.Malibari Sana Alazwari Hamed Alqahtani Mohamed K.Nour Marwa Obbaya Mohamed A.Shamseldin Mesfer Al Duhayyim saudi aramco cybersecurity chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Mechanical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Emerging technologies such as edge computing,Internet of Things(IoT),5G networks,big data,Artificial Intelligence(AI),and Unmanned Aerial Vehicles(UAVs)empower,Industry 4.0,with a progressive production methodology that shows attention to the interaction between machine and human beings.In the literature,various authors have focused on resolving security problems in UAV communication to provide safety for vital applications.The current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification(CSODL-SUAVC)model for Industry 4.0 environment.The suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image classification.Primarily,the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation(ML-DWT),CSO-related Optimal Pixel Selection(CSO-OPS),and signcryption-based encryption.The proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover images.The secret images,encrypted by signcryption technique,are embedded into cover images.Besides,the image classification process includes three components namely,Super-Resolution using Convolution Neural Network(SRCNN),Adam optimizer,and softmax classifier.The integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV communication.The proposed CSODLSUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct aspects.The simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.

关键词： Unmanned Aerial Vehicles Artificial Intelligence emerging technologies Deep Learning Industry 4.0 image steganography

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Optimal Deep Learning Driven Intrusion Detection in SDN-Enabled IoT Environment

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Computers, Materials & Continua 2023年第3期74卷 6587-6604页

作者： Mohammed Maray Haya Mesfer Alshahrani Khalid A.Alissa Najm Alotaibi Abdulbaset Gaddah AliMeree Mahmoud Othman Manar Ahmed Hamza Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia saudi aramco cybersecurity chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Warwick UniversityCovetryUK Department of Computer Science Faculty of Computers and Information TechnologyFuture University in Egypt New Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

In recent years,wireless networks are widely used in different domains.This phenomenon has increased the number of Internet of Things(IoT)devices and their applications.Though IoT has numerous advantages,the commonly-used IoT devices are exposed to cyber-attacks periodically.This scenario necessitates real-time automated detection and the mitigation of different types of attacks in high-traffic networks.The Software-Defined Networking(SDN)technique and the Machine Learning(ML)-based intrusion detection technique are effective tools that can quickly respond to different types of attacks in the IoT networks.The Intrusion Detection System(IDS)models can be employed to secure the SDN-enabled IoT environment in this scenario.The current study devises a Harmony Search algorithmbased Feature Selection with Optimal Convolutional Autoencoder(HSAFSOCAE)for intrusion detection in the SDN-enabled IoT environment.The presented HSAFS-OCAE method follows a three-stage process in which the Harmony Search Algorithm-based FS(HSAFS)technique is exploited at first for feature selection.Next,the CAE method is leveraged to recognize and classify intrusions in the SDN-enabled IoT environment.Finally,the Artificial Fish SwarmAlgorithm(AFSA)is used to fine-tune the hyperparameters.This process improves the outcomes of the intrusion detection process executed by the CAE algorithm and shows the work’s novelty.The proposed HSAFSOCAE technique was experimentally validated under different aspects,and the comparative analysis results established the supremacy of the proposed model.

关键词： Internet of things SDN controller feature selection hyperparameter tuning autoencoder

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Proposed Biometric Security System Based on Deep Learning and Chaos Algorithms

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Computers, Materials & Continua 2023年第2期74卷 3515-3537页

作者： Iman Almomani Walid El-Shafai Aala AlKhayer Albandari Alsumayt Sumayh S.Aljameel Khalid Alissa Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Computer Science Department King Abdullah II School of Information TechnologyThe University of Jordan11942Jordan Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Applied CollegeImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Computer Science Department College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia saudi aramco cybersecurity chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

Nowadays,there is tremendous growth in biometric authentication and cybersecurity applications.Thus,the efficient way of storing and securing personal biometric patterns is mandatory in most governmental and private sectors.Therefore,designing and implementing robust security algorithms for users’biometrics is still a hot research area to be investigated.This work presents a powerful biometric security system(BSS)to protect different biometric modalities such as faces,iris,and fingerprints.The proposed BSSmodel is based on hybridizing auto-encoder(AE)network and a chaos-based ciphering algorithm to cipher the details of the stored biometric patterns and ensures their secrecy.The employed AE network is unsupervised deep learning(DL)structure used in the proposed BSS model to extract main biometric features.These obtained features are utilized to generate two random chaos matrices.The first random chaos matrix is used to permute the pixels of biometric images.In contrast,the second random matrix is used to further cipher and confuse the resulting permuted biometric pixels using a two-dimensional(2D)chaotic logisticmap(CLM)algorithm.To assess the efficiency of the proposed BSS,(1)different standardized color and grayscale images of the examined fingerprint,faces,and iris biometrics were used(2)comprehensive security and recognition evaluation metrics were measured.The assessment results have proven the authentication and robustness superiority of the proposed BSSmodel compared to other existing BSSmodels.For example,the proposed BSS succeeds in getting a high area under the receiver operating characteristic(AROC)value that reached 99.97%and low rates of 0.00137,0.00148,and 3516 CMC,2023,vol.74,no.20.00157 for equal error rate(EER),false reject rate(FRR),and a false accept rate(FAR),respectively.

关键词： Biometric security deep learning AE network 2D CLM cybersecurity and authentication applications feature extraction unsupervised learning

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Regional variation of pediatric inflammatory bowel disease in saudi Arabia: Results from a multicenter study

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World Journal of Gastroenterology 2020年第4期26卷 416-423页

作者： Mohammad I El Mouzan Mohammed H AlEdreesi Mohammed Y Hasosah Abdulrahman A Al-Hussaini Ahmad A Al Sarkhy Asaad A Assiri Department of Pediatrics Gastroenterology UnitKing Saud UniversityRiyadh11461Saudi Arabia Specialty Pediatrics Division Pediatric GastroenterologyJohns Hopkins Aramco HealthcareDhahran 31311Saudi Arabia Department of Pediatrics Gastroenterology UnitKing Saud bin Abdulaziz University for Health SciencesJeddah 21423Saudi Arabia Children Hospital Gastroenterology DivisionKing Fahad Medical CityRiyadh 11525Saudi Arabia Supervisor of Prince Abdullah Bin Khalid Celiac Disease Research chair Gastroenterology UnitKing Saud UniversityRiyadh 11461Saudi Arabia

BACKGROUND Incidence and severity variations of inflammatory bowel disease(IBD)have been reported from Western populations between continents and regions of the same countries.However,no data were available from other countries.AIM To investigate the regional differences in the IBD profiles of pediatric patients from the Kingdom of saudi Arabia.METHODS Data from a national multicenter IBD study were used.The incidence,time trend,and clinical presentation of Crohn’s disease(CD)and ulcerative colitis(UC)in the Central region(CR),Western region(WR),and Eastern region(ER)were analyzed and compared.Statistical analysis included Poisson regression analysis for incidence variation and Chi-square test for demographic and clinical parameters.A Psaudi Arabia.Prospective studies are needed to explain such variations.

关键词： Crohn’s disease Ulcerative colitis Epidemiology Children Regional variation

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Machine Learning Based cybersecurity Threat Detection for Secure IoT Assisted Cloud Environment

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Computer Systems Science & Engineering 2023年第10期47卷 855-871页

作者： Z.Faizal Khan Saeed M.Alshahrani Abdulrahman Alghamdi Someah Alangari Nouf Ibrahim Altamami Khalid A.Alissa Sana Alazwari Mesfer Al Duhayyim Fahd N.Al-Wesabi Department of Computer Engineering College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Computer Science College of Science and Humanities DawadmiShaqra UniversityShaqraSaudi Arabia Department of Mathematics College of EducationShaqra UniversityShaqraSaudi Arabia saudi aramco cybersecurity chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAlaflaj16828Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia

The Internet of Things(IoT)is determine enormous economic openings for industries and allow stimulating innovation which obtain between domains in childcare for eldercare,in health service to energy,and in developed to transport.cybersecurity develops a difficult problem in IoT platform whereas the presence of cyber-attack requires that solved.The progress of automatic devices for cyber-attack classifier and detection employing Artificial Intelligence(AI)andMachine Learning(ML)devices are crucial fact to realize security in IoT platform.It can be required for minimizing the issues of security based on IoT devices efficiently.Thus,this research proposal establishes novel mayfly optimized with Regularized Extreme Learning Machine technique called as MFO-RELM model for cybersecurity Threat classification and detection fromthe cloud and IoT environments.The proposed MFORELM model provides the effective detection of cybersecurity threat which occur in the cloud and IoT platforms.To accomplish this,the MFO-RELM technique pre-processed the actual cloud and IoT data as to meaningful format.Besides,the proposed models will receive the pre-processing data and carry out the classifier method.For boosting the efficiency of the proposed models,theMFOtechnique was utilized to it.The experiential outcome of the proposed technique was tested utilizing the standard CICIDS 2017 dataset,and the outcomes are examined under distinct aspects.

关键词： Mayfly optimization machine learning artificial intelligence cybersecurity threat detection

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Comparative Study of Streamers＇ Characteristics in Different Seed Based Insulating Oils under Lightning Impulse Voltages

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Journal of Energy and Power Engineering 2014年第4期8卷 735-740页

作者： Abderrahmane Beroual1 Viet-Hung Dang Essam Al-Ammar Muhammad Iqbal Qureshi Laboratoire Ampere-CNRS UMR 5005 Ecole Centrale de Lyon University of Lyon Ecully 69130 France saudi aramco chair in Electrical Power Department of Electrical Engineering King Saud University Riyadh 11421 Saudi Arabia

This paper is aimed at the streamers in natural esters (vegetable oils) in point―plane electrode arrangement under lightning impulse voltage. The shape, stopping length, velocity and current of streamers are investigated. Six untreated commercial oils extracted from grape seeds, sunflower and rape seeds, corn, rice and sesame that could constitute potential liquids for high voltage applications are tested. A naphthenic mineral oil is also tested for comparison. It"s shown that the streamers are filamentary for both polarities. For a given voltage, the stopping lengths (L f ) of streamers are longer when the point is positive than when it is negative; also, except mineral oil when the point is negative, the values of L f are very close in all tested oils. The streamers" velocities are in the same range for all vegetable oils and they vary between 0.4 km/s and 1.2 km/s for positive polarity and 0.2 km/s and 0.8 km/s for negative polarity.

关键词：雷电冲击电压绝缘油基础特性平面电极植物油矿物油葡萄籽

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Nutritional status of children with inflammatory boweldisease in saudi Arabia

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World Journal of Gastroenterology 2016年第5期22卷 1854-1858页

作者： Mohammad Issa El Mouzan Mohammed Hadi Al Edreesi Abdulrahman Abdullah Al-Hussaini Omar Ibrahim Saadah Abdulaziz Abdullatif Al Qourain Mohammad Abdullah Al Mofarreh Khalid Abdulrahman Al Saleem Department of Pediatrics HeadPediatric IBD Research GroupMember of Prince Abdullah Bin Khalid Celiac Disease Research ChairKing Saud UniversityRiyadh 11461Saudi Arabia Department of Pediatrics Saudi Aramco Medical Services Organization Dhahran Health CenterDhahran 31311Saudi Arabia Division of Gastroenterology HepatologyKing Saud Bin Abdul Aziz University for Health SciencesThe Children’s HospitalKing Fahad Medical CityRiyadh 11525Saudi Arabia Department of Pediatrics Faculty of Medicine&Inflammatory Bowel Disease Research GroupKing Abdul Aziz UniversityJeddah 21589Saudi Arabia Department of Medicine Division of GastroenterologyKing Fahad Hospital of the UniversityDammam UniversityDammam 31952Saudi Arabia Department of Gastroenterology Mofarreh Poly ClinicRiyadh 11423Saudi Arabia Section of Pediatric Gastroenterology Department of PediatricsKing Faisal Specialist Hospital&Research CentreRiyadh 11525Saudi Arabia

AIM: To assess the prevalence of nutritional disorders in children with inflammatory bowel disease(IBD) in saudi Arabia.METHODS: The data from a national cohort of children newly diagnosed with IBD between 2003 and 2012 were analyzed. The diagnosis of IBD and the differentiation between Crohn's disease(CD) and ulcerative colitis(UC) were confirmed by gastroenterologists according to the standard criteria. The body mass index(BMI) of each child [weight(kg)/height^2(m)] was calculated at the time of diagnosis. The World Health Organization standards and references were used and the BMI for age > +1 and saudi nationals, and 68(57%) of the UC and 150(59%) of the CD children were males. A positive history of anorexia at the time of diagnosis was found in 30(25%) patients with UC and 99(39%) patients with CD. The prevalence of thinness was 31%, 35% and 24% in children with IBD, CD and UC, respectively, with a significantly higher prevalence of thinness in children with CD than in children with UC(P = 0.037) only in the age group of 10-17 years(P = 0.030). The prevalence of overweight was 16 %, 15% and 20 % in the children with IBD, CD and UC, respectively, indicating a higher prevalence in UC that was statistically significant only in the age group of 10-17 years(P = 0.020). CONCLUSION: A high proportion of children with IBD presented with overweight instead of the classical underweight. Awareness of this finding is important for patient care.

关键词： Overweight Thinness Ulcerative colitis Crohn disease Children saudi Arabia

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Enhance Intrusion Detection in Computer Networks Based on Deep Extreme Learning Machine

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Computers, Materials & Continua 2021年第1期66卷 467-480页

作者： Muhammad Adnan Khan Abdur Rehman Khalid Masood Khan Mohammed A.Al Ghamdi Sultan H.Almotiri Department of Computer Science Lahore Garrison UniversityLahore54792Pakistan School of Computer Science NCBA&ELahore54000Pakistan Computer Science Department Umm Al-Qura UniversityMakkah City715Saudi Arabia

Networks provide a significant function in everyday life,and cybersecurity therefore developed a critical field of study.The Intrusion detection system(IDS)becoming an essential information protection strategy that tracks the situation of the software and hardware operating on the network.Notwithstanding advancements of growth,current intrusion detection systems also experience difficulties in enhancing detection precision,growing false alarm levels and identifying suspicious activities.In order to address above mentioned issues,several researchers concentrated on designing intrusion detection systems that rely on machine learning approaches.Machine learning models will accurately identify the underlying variations among regular information and irregular information with incredible efficiency.Artificial intelligence,particularly machine learning methods can be used to develop an intelligent intrusion detection framework.There in this article in order to achieve this objective,we propose an intrusion detection system focused on a Deep extreme learning machine(DELM)which first establishes the assessment of safety features that lead to their prominence and then constructs an adaptive intrusion detection system focusing on the important features.In the moment,we researched the viability of our suggested DELMbased intrusion detection system by conducting dataset assessments and evaluating the performance factors to validate the system reliability.The experimental results illustrate that the suggested framework outclasses traditional algorithms.In fact,the suggested framework is not only of interest to scientific research but also of functional importance.

关键词： Intrusion detection system DELM network security machine learning

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