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RISS 인기검색어
- 검색키워드
- 저자 : ( Hamad Naeem ) (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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A Cross-Platform Malware Variant Classification based on Image Representation
( Hamad Naeem ),( Bing Guo ),( Farhan Ullah ),( Muhammad Rashid Naeem ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.7
Recent internet development is helping malware researchers to generate malicious code variants through automated tools. Due to this reason, the number of malicious variants is increasing day by day. Consequently, the performance improvement in malware analysis is the critical requirement to stop the rapid expansion of malware. The existing research proved that the similarities among malware variants could be used for detection and family classification. In this paper, a Cross-Platform Malware Variant Classification System (CP-MVCS) proposed that converted malware binary into a grayscale image. Further, malicious features extracted from the grayscale image through Combined SIFT-GIST Malware (CSGM) description. Later, these features used to identify the relevant family of malware variant. CP-MVCS reduced computational time and improved classification accuracy by using CSGM feature description along machine learning classification. The experiment performed on four publically available datasets of Windows OS and Android OS. The experimental results showed that the computation time and malware classification accuracy of CP-MVCS was higher than traditional methods. The evaluation also showed that CP-MVCS was not only differentiated families of malware variants but also identified both malware and benign samples in mix fashion efficiently.
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A Hybrid Proposed Framework for Object Detection and Classification
Aamir, Muhammad,Pu, Yi-Fei,Rahman, Ziaur,Abro, Waheed Ahmed,Naeem, Hamad,Ullah, Farhan,Badr, Aymen Mudheher Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.5
The object classification using the images' contents is a big challenge in computer vision. The superpixels' information can be used to detect and classify objects in an image based on locations. In this paper, we proposed a methodology to detect and classify the image's pixels' locations using enhanced bag of words (BOW). It calculates the initial positions of each segment of an image using superpixels and then ranks it according to the region score. Further, this information is used to extract local and global features using a hybrid approach of Scale Invariant Feature Transform (SIFT) and GIST, respectively. To enhance the classification accuracy, the feature fusion technique is applied to combine local and global features vectors through weight parameter. The support vector machine classifier is a supervised algorithm is used for classification in order to analyze the proposed methodology. The Pascal Visual Object Classes Challenge 2007 (VOC2007) dataset is used in the experiment to test the results. The proposed approach gave the results in high-quality class for independent objects' locations with a mean average best overlap (MABO) of 0.833 at 1,500 locations resulting in a better detection rate. The results are compared with previous approaches and it is proved that it gave the better classification results for the non-rigid classes.
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A Hybrid Proposed Framework for Object Detection and Classification
( Muhammad Aamir ),( Yi-fei Pu ),( Ziaur Rahman ),( Waheed Ahmed Abro ),( Hamad Naeem ),( Farhan Ullah ),( Aymen Mudheher Badr ) 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.5
The object classification using the images’ contents is a big challenge in computer vision. The superpixels’ information can be used to detect and classify objects in an image based on locations. In this paper, we proposed a methodology to detect and classify the image's pixels' locations using enhanced bag of words (BOW). It calculates the initial positions of each segment of an image using superpixels and then ranks it according to the region score. Further, this information is used to extract local and global features using a hybrid approach of Scale Invariant Feature Transform (SIFT) and GIST, respectively. To enhance the classification accuracy, the feature fusion technique is applied to combine local and global features vectors through weight parameter. The support vector machine classifier is a supervised algorithm is used for classification in order to analyze the proposed methodology. The Pascal Visual Object Classes Challenge 2007 (VOC2007) dataset is used in the experiment to test the results. The proposed approach gave the results in high-quality class for independent objects’ locations with a mean average best overlap (MABO) of 0.833 at 1,500 locations resulting in a better detection rate. The results are compared with previous approaches and it is proved that it gave the better classification results for the non-rigid classes.
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