Browsing by Subject "text categorization"

Now showing 1 - 3 of 3

Access status: Open Access ,
Adapting text categorization for manifest based android malware detection
(Wydawnictwa AGH, 2019) Çoban, Önder; Özel, Selma Ayşe
Malware is a shorthand of malicious software that are created with the intent of damaging hardware systems, stealing data, and causing a mess to make money, protest something, or even make war between governments. Malware is often spread by downloading some applications for your hardware from some download platforms. It is highly probable to face with a malware while you try to load some applications for your smart phones nowadays. Therefore it is very important that some tools are needed to detect malware before loading them to the hardware systems. There are mainly three different approaches to detect malware: i) static, ii) dynamic, and iii) hybrid. Static approach analyzes the suspicious program without executing it. Dynamic approach, on the other hand, executes the program in a controlled environment and obtains information from operating system during runtime. Hybrid approach, as its name implies, is the combination of these two approaches. Although static approach may seem to have some disadvantages, it is highly preferred because of its lower cost. In this paper, our aim is to develop a static malware detection system by using text categorization techniques. To reach our goal, we apply text mining techniques like feature extraction by using bag-of-words, n-grams, etc. from manifest content of suspicious programs, then apply text classification methods to detect malware. Our experimental results revealed that our approach is capable of detecting malicious applications with an accuracy between 94.0% and 99.3%.
Access status: Open Access ,
Assessment of nature-inspired algorithms for text feature selection
(Wydawnictwa AGH, 2022) Çoban, Önder
This paper provides a comprehensive assessment of basic feature selection (FS) methods that have originated from nature-inspired (NI) meta-heuristics, two well-known filter-based FS methods are also included for comparison. The performances of the considered methods are compared on four balanced highdimensional and real-world text data sets regarding the accuracy, the number of selected features, and computation time. This study differs from existing studies in terms of the extent of experimental analyses that were performed under different circumstances where the classifier, feature model, and term-weighting scheme were different. The results of the extensive experiments indicated that basic NI algorithms produce slightly different results than filter-based methods for the text FS problem. However, filter-based methods often provide better results by using lower numbers of features and computation times.
Access status: Restricted ,
Zastosowanie uczenia maszynowego w zagadnieniu kategoryzacji dokumentów
(Data obrony: 2018-07-06) Górnisiewicz, Roland
Wydział Elektrotechniki, Automatyki, Informatyki i Inżynierii Biomedycznej