Browsing by Author "Alazab, Mamoun"

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    AndroShow
    (Studies in Computational Intelligence, Springer, 2020-12-15) Russel, Md. Omar Faruque Khan; Rahman, Sheikh Shah Mohammad Motiur; Alazab, Mamoun
    This paper represents a static analysis based research of android’s feature in obfuscated android malware. Android smartphone’s security and privacy of personal information remain threatened because of android based device popularity. It has become a challenging and diverse area to research in information security. Though malware researchers can detect already identified malware, they can not detect many obfuscated malware. Because, malware attackers use different obfuscation techniques, as a result many anti malware engines can not detect obfuscated malware applications. Therefore, it is necessary to identify the obfuscated malware pattern made by attackers. A large-scale investigation has been performed in this paper by developing python scripts, named it AndroShow, to extract pattern of permission, app component, filtered intent, API call and system call from an obfuscated malware dataset named Android PRAGuard Dataset. Finally, the patterns in a matrix form have been found and stored in a Comma Separated Values (CSV) file which will be the base of detecting the obfuscated malware in future.
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    AndroShow
    (Scopus, 2021) Russel, Md. Omar Faruque Khan; Rahman, Sheikh Shah Mohammad Motiur; Alazab, Mamoun
    This paper represents a static analysis based research of android’s feature in obfuscated android malware. Android smartphone’s security and privacy of personal information remain threatened because of android based device popularity. It has become a challenging and diverse area to research in information security. Though malware researchers can detect already identified malware, they cannot detect many obfuscated malware. Because, malware attackers use different obfuscation techniques, as a result many anti malware engines cannot detect obfuscated malware applications. Therefore, it is necessary to identify the obfuscated malware pattern made by attackers. A large-scale investigation has been performed in this paper by developing python scripts, named it AndroShow, to extract pattern of permission, app component, filtered intent, API call and system call from an obfuscated malware dataset named Android PRAGuard Dataset. Finally, the patterns in a matrix form have been found and stored in a Comma Separated Values (CSV) file which will be the base of detecting the obfuscated malware in future.
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    IntAnti-Phish
    (Scopus, 2021) Rahman, Sheikh Shah Mohammad Motiur; Gope, Lakshman; Islam, Takia; Alazab, Mamoun
    Among the cybercriminals, the popularity of phishing has been rapidly growing day by day. Therefore, phishing has become an alarming issue to solve in the field of cyber security. Many researchers have already proposed several anti-phishing approaches to detect phishing in terms of email, webpages, images, or links. This study also aimed to propose and implement an intelligent framework to detect phishing URLs (Uniform Resource Locator). It has been observed in this study that Back propagation Neural Network-based systems need to tune various hyper parameters to obtain the optimized output. With a maximum of two hidden layers along with 400 epochs can reach maximum accuracy of 0.93, the minimum mean squared error of 0.27, and also a minimum error rate of 0.07 which measurements lead this study to generate an optimized model for phishing detection. The detailed process of feature extraction and optimized model generation along with the detection of unknown URLs are considered and proposed during the development of IntAnti-Phish (An Intelligent Anti-Phishing Framework).
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    IntAnti-Phish
    (Studies in Computational Intelligence , Springer, 2020-12-15) Rahman, Sheikh Shah Mohammad Motiur; Gope, Lakshman; Islam, Takia; Alazab, Mamoun
    Among the cybercriminals, the popularity of phishing has been rapidly growing day by day. Therefore, phishing has become an alarming issue to solve in the field of cybersecurity. Many researchers have already proposed several anti-phishing approaches to detect phishing in terms of email, webpages, images, or links. This study also aimed to propose and implement an intelligent framework to detect phishing URLs (Uniform Resource Locator). It has been observed in this study that Backpropagation Neural Network-based systems need to tune various hyperparameters to obtain the optimized output. With a maximum of two hidden layers along with 400 epochs can reach maximum accuracy of 0.93, the minimum mean squared error of 0.27, and also a minimum error rate of 0.07 which measurements lead this study to generate an optimized model for phishing detection. The detailed process of feature extraction and optimized model generation along with the detection of unknown URLs are considered and proposed during the development of IntAnti-Phish (An Intelligent Anti-Phishing Framework).