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An Enhanced Intelligent Intrusion Detection System to Secure E-Commerce Communication Systems

Computer Systems Science & Engineering 2023年第11期47卷 2513-2528页

作者： adil hussain Kashif Naseer Qureshi Khalid Javeed Musaed Alhussein Department of Transportation Engineering Chang’an UniversityXi’anChina Department of Electronic&Computer Engineering University of LimerickLimerickV94 T9PXIreland Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjahUnited Arab Emirates Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Kingdom of Saudi Arabia

Information and communication technologies are spreading rapidly due to their fast proliferation in many fields.The number of Internet users has led to a spike in cyber-attack incidents.E-commerce applications,such as online banking,marketing,trading,and other online businesses,play an integral role in our lives.Network Intrusion Detection System(NIDS)is essential to protect the network from unauthorized access and against other cyber-attacks.The existing NIDS systems are based on the Backward Oracle Matching(BOM)algorithm,which minimizes the false alarm rate and causes of high packet drop ratio.This paper discussed the existing NIDS systems and different used pattern-matching techniques regarding their weaknesses and limitations.To address the existing system issues,this paper proposes an enhanced version of the BOM algorithm by using multiple pattern-matching methods for the NIDS system to improve the network performance.The proposed solution is tested in simulation with existing solutions using the Snort and NSL-KDD datasets.The experimental results indicated that the proposed solution performed better than the existing solutions and achieved a 5.17%detection rate and a 0.22%lower false alarm rate than the existing solution.

关键词： E-commerce NIDS security algorithm network applications CIA detection

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Effects of lead(Pb)-induced oxidative stress on morphological and physio-biochemical properties of rice

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BIOCELL 2021年第5期45卷 1413-1423页

作者： MURTAZA KHAN IBA NAZAR IBRAHIM AL AZZAWI MUHAMMAD IMRAN adil hussain BONG-GYU MUN ANJALI PANDE BYUNG-WOOK YUN Laboratory of Plant Functional Genomics School of Applied BiosciencesKyungpook National UniversityDaegu41566South Korea Ministry of Agriculture State Company for Agriculture Supplies Baghdad10064Iraq Laboratory of Crop Physiology School of Applied BiosciencesKyungpook National UniversityDaegu41566South Korea Department of Entomology Abdul Wali Khan University MardanKhyber Pakhtunkhwa23200Pakistan

In rice,high concentration of lead(Pb)can cause phyto-toxicity affecting several physiological functions.Cultivation of rice varieties that are resistant to Pb-induced oxidative stress is an important management strategy in Pb-contaminated soils.In the current study,we evaluated four different rice cultivars for their response to Pb-induced stress.Three japonica type cultivars X-Jigna,Ediget,and Furat,and one Indica type cultivar Amber 33 were grown in soil containing different Pb concentrations(0 mM,0.6 mM,and 1.2 mM).The soil was treated with 0 mM or 0.6 mM or 1.2 mM Pb solution one month prior to rice seedling transplantation.Thereafter,four-week-old rice seedlings were transplanted into the treated soil and their responses were observed until maturity.The data revealed that a highest concentration of Pb(1.2 mM)induced significant reduction in agronomic traits such as plant height,number of tillers per plant,number of panicles per plant,and number of spikelets per panicle in all the rice cultivars.However,least reduction in the agronomic traits was observed in X-Jigna,whereas the highest reduction in the agronomic traits was observed in Ediget.Antioxidant activity of catalase(CAT),peroxidase(POD),polyphenol oxidase(PPO),and superoxide dismutase(SOD),was evaluated along with the accumulation of superoxide ions(O2.-),protein,proline,chlorophyll,sucrose,glucose,and fructose contents in all the rice cultivars.A significant increase in antioxidant activity and in the accumulation of proline and sucrose contents with the least reduction in the chlorophyll and protein contents was observed in X-Jigna suggesting that X-Jigna is the most tolerant among all the rice cultivars tested against Pb-stress.On the other hand,non-significant and slightly significant increase in the antioxidant activity,less accumulation of proline and sucrose contents,and higher reduction in the chlorophyll and protein contents was observed in Ediget,which further suggest that Ediget is the most susceptible rice cultivar to Pb-stress.In additio

关键词： Lead(Pb)toxicity Oxidative stress Morphological and physio-biochemical properties of rice

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基于电压源换流器的稳定器阻尼电力系统低频振荡研究

基于电压源换流器的稳定器阻尼电力系统低频振荡研究

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作者： adil hussain 华北电力大学(北京)

学位级别：硕士

电压源换流器(VSC)是交直流缓和系统中最重要的动态组件。VSC与传统电网的交互作用可能会产生低频振荡(LFO)问题,将会降低电力系统的稳定性和可靠性。众所周知,电力系统低频振荡的主要原因是电力系统缺乏足够的阻尼,主要由以下几个原因:... 详细信息

电压源换流器(VSC)是交直流缓和系统中最重要的动态组件。VSC与传统电网的交互作用可能会产生低频振荡(LFO)问题,将会降低电力系统的稳定性和可靠性。众所周知,电力系统低频振荡的主要原因是电力系统缺乏足够的阻尼,主要由以下几个原因:(1)大功率长距离传输,(2)大电网的弱连接,(3)高增益快速调节的AVR。此外,这些低频振荡也是提高电力系统动态特性的主要障碍。如果阻尼不足,低频振荡不仅可能引发严重的故障,而且可能会导致电力系统崩溃。在电力系统中引入一种基于VSC控制的稳定器,可以提高电力系统的阻尼,能够较好地解决上述问题。本文通过采用模态分析法研究了基于VSC结构的稳定器与常规电网集成的问题,并提出一种适用于单机无穷大电力系统的方案。基于模态分析法的特征值计算,在电力系统中增加附加阻尼控制器,可以为电力系统提供正阻尼来抑制低频振荡,而其安装位置和配置参数也将影响稳定器的性能。为了获得更好的控制性能,本文采用粒子群优化算法,以参与因子为基础,选取合适的目标函数,来求取稳定器的最佳安装位置和最优配置参数。其中,目标函数是在系统工作点线性化处理后所得系统模型的临界模式对应的阻尼比。最终,时域仿真结果表明,电力系统稳定器的配置参数及安装位置优化显著提高了电力系统的阻尼和稳定性。

关键词：电压源变换器(VSC) 电力系统稳定器(PSS) 低频振荡(LFOs) 粒子群优化(PSO)

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Healthcare Device Security Assessment through Computational Methodology

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Computer Systems Science & Engineering 2022年第5期41卷 811-828页

作者： Masood Ahmad Jehad F.Al-Amri Ahmad F.Subahi Sabita Khatri adil hussain Seh Mohd Nadeem Alka Agrawal Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025Uttar PradeshIndia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science University College of Al JamoumUmm Al Qura UniversityMakkah21421Saudi Arabia

The current study discusses the different methods used to secure healthcare devices and proposes a quantitative framework to list them in order of significances.The study uses the Hesitant Fuzzy(HF),Analytic Hierarchy Process(AHP)integrated with Fuzzy Technical for Order Preference by Similarities to Ideal Solution(TOPSIS)to classify the best alternatives to security techniques for healthcare devices to securing the devices.The technique is enlisted to rate the alternatives based on the degree of satisfaction of their weights.The ranks of the alternatives consequently decide the order of priority for the techniques.A1 was the most probable alternative of all the alternatives,according to the ranks of the alternatives acquired.This means that the security of A2 healthcare devices is the greatest of all the alternatives picked.A corroborative guide for the developers and the makers in quantitatively determining the security of healthcare devices to engineer efficacious devices will be the findings drawn up with the assistance of the proposed framework.The assessments performed using the proposed framework are systematic,precise,and definitive.Therefore,the results of the present empirical analysis are a stronger and accurate choice than the manual assessment of the device’s security.

关键词： Healthcare devices HF-AHP.TOPSIS device security

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An Analysis of Integrating Machine Learning in Healthcare for Ensuring Confidentiality of the Electronic Records

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Computer Modeling in Engineering & Sciences 2022年第3期130卷 1387-1422页

作者： adil hussain Seh Jehad F.Al-Amri Ahmad F.Subahi Alka Agrawal Nitish Pathak Rajeev Kumar Raees Ahmad Khan Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025India Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science Umm Al-Qura UniversityMakkah21421Saudi Arabia Department of Information Technology Guru Gobind Singh Indraprastha UniversityDelhi110078India Department of Computer Applications Shri Ramswaroop Memorial UniversityBarabanki225003India Department of Computer Science and Engineering Babu Banarasi Das UniversityLucknow226028India

The adoption of sustainable electronic healthcare infrastructure has revolutionized healthcare services and ensured that E-health technology caters efficiently and promptly to the needs of the stakeholders associated with healthcare.Despite the phenomenal advancement in the present healthcare services,the major obstacle that mars the success of E-health is the issue of ensuring the confidentiality and privacy of the patients’data.A thorough scan of several research studies reveals that healthcare data continues to be the most sought after entity by cyber invaders.Various approaches and methods have been practiced by researchers to secure healthcare digital services.However,there are very few from the Machine learning(ML)domain even though the technique has the proactive ability to detect suspicious accesses against Electronic Health Records(EHRs).The main aim of this work is to conduct a systematic analysis of the existing research studies that address healthcare data confidentiality issues through ML approaches.B.A.Kitchenham guidelines have been practiced as a manual to conduct this work.Seven well-known digital libraries namely IEEE Xplore,Science Direct,Springer Link,ACM Digital Library,Willey Online Library,PubMed(Medical and Bio-Science),and MDPI have been included to performan exhaustive search for the existing pertinent studies.Results of this study depict that machine learning provides a more robust security mechanism for sustainable management of the EHR systems in a proactive fashion,yet the specified area has not been fully explored by the researchers.K-nearest neighbor algorithm and KNIEM implementation tools are mostly used to conduct experiments on EHR systems’log data.Accuracy and performance measure of practiced techniques are not sufficiently outlined in the primary studies.This research endeavour depicts that there is a need to analyze the dynamic digital healthcare environment more comprehensively.Greater accuracy and effective implementation of ML-based models are the need of the day for ensu

关键词： EHRs healthcare machine learning systematic analysis

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Growth, Yield and Quality Performance of Pearl Millet (<i>Pennisetum americanum</i>L.)

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American Journal of Plant Sciences 2014年第15期5卷 2215-2223页

作者： Mahmood Ul Hassan Azraf-ul-Haq Ahmad Shahid Ibni Zamir Ihtishamul Haq Farhan Khalid Tassadduq Rasool adil hussain Department of Agronomy University of Agriculture Faisalabad Pakistan

Livestock industry of Pakistan is expanding day by day. To meet its growing demand high fodder yielding and nutritious varieties of fodder crops are needed. Pearl millet (Pennisetum americanum L.) is an excellent choice for this purpose. In order to explore the possibility of the better yield potential varieties of pearl millet performed in a good manner under agro ecological conditions of Faisalabad during the year 2012. A field experiment was conducted at Agronomic Research Area, University of Agriculture, Faisalabad, Pakistan. Randomized complete block design was used with three replications;the net plot size was 1.8 m × 6 m. The experiment was comprised of nine millet varieties named Cholistani Bajra, Barani Bajra, MB-87, Sargodha Bajra 2011, 18-BY, Super Bajra-1, PARC-MS-2, 86-M-52 and FB-822. All other agronomic practices were kept normal and constant. Data on yield and yield components were recorded by standard procedure. Significant results were recorded among the varieties for forage growth and yield. The variety 86-M-52 produced maximum forage and dry matter yield because of more number of leaves (14), leaf area (3540.1 cm2) followed by Sargodha Bajra-2011. All cultivars have statistically significant differences in respect of quality characteristics. However, non-significant differences were observed among cultivars regarding ash contents. The cultivar Sargodha Bajra-2011 has the highest crude protein (10.347%) and the cultivar FB-822 has the minimum crude protein percentage (6.733%). While PARC-MS-2 has the highest crude fiber percentage (34.667%) but variety MB-87 has the minimum crude fiber (24.333%). Variety 86-M-52 proved better for getting higher forage yield followed by Sargodha Bajra-2011 than all other varieties. Sargodha Bajra-2011 is the best cultivar that performed well in respect of quality parameters under irrigated conditions of Faisalabad.

关键词： Forage Fiber Varieties Quality

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Hybrid Computational Modeling for Web Application Security Assessment

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Computers, Materials & Continua 2022年第1期70卷 469-489页

作者： adil hussain Seh Jehad F.Al-Amri Ahmad F.Subahi Md Tarique Jamal Ansari Rajeev Kumar Mohammad Ubaidullah Bokhari Raees Ahmad Khan Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025Uttar PradeshIndia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science University College of Al JamoumUmm Al Qura UniversityMakkah21421Saudi Arabia Department of Computer Applications Shri Ramswaroop Memorial UniversityBarabanki225003Uttar PradeshIndia Department of Computer Science Aligarh Muslim UniversityAligarh202002Uttar PradeshIndia

Transformation from conventional business management systems to smart digital systems is a recurrent trend in the current era.This has led to digital revolution,and in this context,the hardwired technologies in the software industry play a significant role However,from the beginning,software security remains a serious issue for all levels of stakeholders.Software vulnerabilities lead to intrusions that cause data breaches and result in disclosure of sensitive data,compromising the organizations’reputation that translates into,financial losses as well.Most of the data breaches are financially motivated,especially in the healthcare sector.The cyber invaders continuously penetrate the E-Health data because of the high cost of the data on the dark web.Therefore,security assessment of healthcare web-based applications demands immediate intervention mechanisms to weed out the threats of cyber-attacks.The aim of this work is to provide efficient and effective healthcare web application security assessment.The study has worked with the hybrid computational model of Multi-Criteria Decision Making(MCDM)based on Analytical Hierarchy Process(AHP)and Technique for Order of Preference by Similarity to Ideal-Solutions(TOPSIS)under the Hesitant Fuzzy(HF)environment.Hesitant fuzzy sets provide effective solutions to address decision making problems where experts counter hesitation to make a decision.The proposed research endeavor will support designers and developers in identifying,selecting and prioritizing the best security attributes for web applications’development.The empirical analysis concludes that Robustness got highest priority amongst the assessed security attributes set followed by Encryption,Authentication,Limit Access,Revoke Access,Data Validation,and Maintain Audit Trail.The results of this research endeavor depict that this proposed computational procedure would be the most conversant mechanism for determining the web application security.The study also establishes guidelines which the developers can refer for the

关键词： Healthcare web application security assessment hesitant fuzzy sets AHP-TOPSIS

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Estimating the Impact of COVID-19 Pandemic on the Research Community in the Kingdom of Saudi Arabia

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Computer Modeling in Engineering & Sciences 2021年第1期126卷 419-436页

作者： Abdulaziz Attaallah Masood Ahmad adil hussain Seh Alka Agrawal Rajeev Kumar Raees Ahmad Khan Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025India Department of Computer Application Shri Ramswaroop Memorial UniversityBarabanki225003India

Ever since its outbreak inWuhan,COVID-19 has cloaked the entireworld in a pall of despondency and uncertainty.The present study describes the exploratory analysis of all COVID cases in Saudi Arabia.Besides,the study has executed the forecastingmodel for predicting the possible number of COVID-19 cases in Saudi Arabia till a defined period.Towards this intent,the study analyzed different age groups of patients(child,adult,elderly)who were affected by COVID-19.The analysis was done city-wise and also included the number of recoveries recorded in different cities.Furthermore,the study also discusses the impact of COVID-19 on the economy.For conducting the stated analysis,the authors have created a list of factors that are known to cause the spread of COVID-19.As an effective countermeasure to contain the spread of Coronavirus in Saudi Arabia,this study also proposes to identify the most effective Computer Science technique that can be used by healthcare professionals.For this,the study employs the Fuzzy-Analytic Hierarchy Process integrated with the Technique for Order Performance by Similar to Ideal Solution(F.AHP.TOPSIS).After prioritizing the various Computer Science techniques,the ranking order that was obtained for the different techniques/tools to contain COVID-19 was:A4>A1>A2>A5>A3.Since the Blockchain technique obtained the highest priority,the study recommends that it must be used extensively as an efficacious and accurate means to combat COVID-19.

关键词： Coronavirus social impact safety precautions fuzzy-AHP.TOPSIS block-chain technique COVID-19 monitoring

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Adversarial Attacks on Featureless Deep Learning Malicious URLs Detection

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Computers, Materials & Continua 2021年第7期68卷 921-939页

作者： Bader Rasheed adil Khan S.M.Ahsan Kazmi Rasheed hussain Md.Jalil Piran Doug Young Suh Institute of Data Science and Articial Intelligence Innopolis UniversityInnopolis420500Russia Institute of Information Security and Cyberphysical Systems Innopolis UniversityInnopolis420500Russia Department of Computer Science and Engineering Sejong UniversitySeoulKorea Department of Electronics Engineering Kyung Hee UniversityYonginKorea

Detecting malicious Uniform Resource Locators(URLs)is crucially important to prevent attackers from committing cybercrimes.Recent researches have investigated the role of machine learning(ML)models to detect malicious URLs.By using ML algorithms,rst,the features of URLs are extracted,and then different ML models are trained.The limitation of this approach is that it requires manual feature engineering and it does not consider the sequential patterns in the URL.Therefore,deep learning(DL)models are used to solve these issues since they are able to perform featureless detection.Furthermore,DL models give better accuracy and generalization to newly designed URLs;however,the results of our study show that these models,such as any other DL models,can be susceptible to adversarial attacks.In this paper,we examine the robustness of these models and demonstrate the importance of considering this susceptibility before applying such detection systems in real-world solutions.We propose and demonstrate a black-box attack based on scoring functions with greedy search for the minimum number of perturbations leading to a misclassication.The attack is examined against different types of convolutional neural networks(CNN)-based URL classiers and it causes a tangible decrease in the accuracy with more than 56%reduction in the accuracy of the best classier(among the selected classiers for this work).Moreover,adversarial training shows promising results in reducing the inuence of the attack on the robustness of the model to less than 7%on average.

关键词： Malicious URLs detection deep learning adversarial attack web security

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Association Mapping of Quantitative Trait Loci for Grain Size in Introgression Line Derived from Oryza rufipogon

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Rice science 2020年第3期27卷 246-254页

作者： Kashif hussain ZHANG Yingxing Workie ANLEY Aamir RIAZ adil ABBAS MdHasanuzzaman RANI WANG Hong SHEN Xihong CAO Liyong CHENG Shihua Key Laboratory for Zhejiang Super Rice Research&State Key Laboratory of Rice Biology China National Rice Research InstituteHangzhou 310006China Southern Rice Research Institute Thatta Agriculture Research WingGovernment of SindhSindh 73130Pakistan Department of Plant Sciences University of GondarP.O.Box 196Ethiopia Bangladesh Institute of Nuclear Agriculture Dhaka 2202Bangladesh

Grain size is one of the critical agronomic traits governing grain yield and quality in rice.However,the underlying genetic mechanisms that control grain size in rice are poorly understood.We used an introgression line derived from Zhonghui 8015 and Oryza rufipogon Griff.This introgression line was evaluated under two different environmental conditions to dissect the quantitative trait loci controlling grain size.Genome-wide association study(GWAS)was performed using 28193 SNPs through a general linear model,and 56 significant SNPs on different loci associated with the 4 grain size traits were detected.Cloned genes including GS3 and q GL3 showed substantial effects on grain length and size.Seven new stable loci were identified with pleiotropic effects on grain size.Haplotype,gene expression analyses,combined gene-based associations,and functional annotations permitted the shortlisting of important dominant genes including GS3 and q GL3.

关键词： grain size association mapping Oryza sativa Oryza rufipogon quantitative trait locus genome-wide association analysis

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