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Toward Robust Classifiers for PDF Malware Detection

Computers, Materials & Continua 2021年第11期69卷 2181-2202页

作者： Marwan Albahar mohammed thanoon Monaj Alzilai Alaa Alrehily Munirah Alfaar Maimoona Algamdi Norah Alassaf College of Computers in Al-Leith Umm Al Qura UniversityMakkahSaudi Arabia

Malicious Portable Document Format(PDF)files represent one of the largest threats in the computer security *** research has been done using handwritten signatures and machine learning based on detection via manual feature *** approaches are time consuming,require substantial prior knowledge,and the list of features must be updated with each newly discovered vulnerability *** this study,we propose two models for PDF malware *** first model is a convolutional neural network(CNN)integrated into a standard deviation based regularization model to detect malicious PDF *** second model is a support vector machine(SVM)based ensemble model with three different *** two models were trained and tested on two different *** experimental results show that the accuracy of both models is approximately 100%,and the robustness against evasive samples is ***,the robustness of the models was evaluated with malicious PDF documents generated using *** models can distinguish the different vulnerabilities exploited in malicious files and achieve excellent performance in terms of generalization ability,accuracy,and robustness.

关键词： Malicious PDF classification robustness guiding principles convolutional neural network new regularization

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Artificial Intelligence Based Sentence Level Sentiment Analysis of COVID-19

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Computer Systems Science & Engineering 2023年第10期47卷 791-807页

作者： Sundas Rukhsar Mazhar Javed Awan Usman Naseem Dilovan Asaad Zebari Mazin Abed mohammed Marwan Ali Albahar mohammed thanoon Amena Mahmoud Department of Software Engineering University of Management and TechnologyLahore54770Pakistan School of Computer Science The University of SydneySydneyAustralia Department of Computer Science College of ScienceNawroz UniversityDuhok42001Kurdistan RegionIraq College of Computer Science and Information Technology University of AnbarAnbar31001Iraq Department of Computer Science Umm Al Qura UniversityMecca24211Saudi Arabia Computer Science Department Faculty of Computers and InformationKafrelsheikh UniversityKafrelsheikhEgypt

Web-blogging sites such as Twitter and Facebook are heavily influenced by emotions,sentiments,and data in the modern ***,a widely used microblogging site where individuals share their thoughts in the form of tweets,has become a major source for sentiment *** recent years,there has been a significant increase in demand for sentiment analysis to identify and classify opinions or expressions in text or *** or expressions of people about a particular topic,situation,person,or product can be identified from sentences and divided into three categories:positive for good,negative for bad,and neutral for mixed or confusing *** process of analyzing changes in sentiment and the combination of these categories is known as“sentiment analysis.”In this study,sentiment analysis was performed on a dataset of 90,000 tweets using both deep learning and machine learning *** deep learning-based model long-short-term memory(LSTM)performed better than machine learning *** short-term memory achieved 87%accuracy,and the support vector machine(SVM)classifier achieved slightly worse results than LSTM at 86%.The study also tested binary classes of positive and negative,where LSTM and SVM both achieved 90%accuracy.

关键词： COVID-19 artificial intelligence machine learning deep learning sentimental analysis support vector classifier

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A New Approach for Smoothing Soil Grain Size Curve Determined by Hydrometer

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International Journal of Geosciences 2013年第9期4卷 1285-1291页

作者： mohammed Q. H. AL-Jumaily thanoon H. AL-Dabbagh Geology Department College of Science University of Mosul Mosul Iraq

In hydrometer analysis for soil grain size distribution, usually, the grains passing sieve No. 200 (<0.074 mm) are used. However, the hydrometer results occasionally give diameters greater than 0.074 mm. This event causes a mismatch in the curve of grain size distribution obtained from sieving and hydrometer methods. Hence, a new approach is proposed for smoothing soil grain size curve determined by hydrometer using Excel-2007 with simple statistical methods. The treatments show that in case of large sizes, there are big differences between the values of soil grain diameters smoothed by Excel-2007 in comparison and the values measured by references. These differences generally decrease with decreasing soil grain size diameters. The statistical treatments also divulge whether the hydrometer results are accurate or not. Furthermore, a general equation has been derived to estimate values of K factor, which is used for calculating the grain diameters in hydrometer analysis. The equation can be applied for any specific gravity of soils and for wide range temperatures.

关键词： Hydrometer Soil Mechanics Grain Size

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