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Sharoug Alzaidy and Hamad Binsalleeh
In the field of behavioral detection, deep learning has been extensively utilized. For example, deep learning models have been utilized to detect and classify malware. Deep learning, however, has vulnerabilities that can be exploited with crafted inputs,...
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Abigail Copiaco, Leena El Neel, Tasnim Nazzal, Husameldin Mukhtar and Walid Obaid
This study introduces an innovative all-in-one malware identification model that significantly enhances convenience and resource efficiency in classifying malware across diverse file types. Traditional malware identification methods involve the extractio...
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Maya Hilda Lestari Louk and Bayu Adhi Tama
Given their escalating number and variety, combating malware is becoming increasingly strenuous. Machine learning techniques are often used in the literature to automatically discover the models and patterns behind such challenges and create solutions th...
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Nureni Ayofe Azeez, Oluwanifise Ebunoluwa Odufuwa, Sanjay Misra, Jonathan Oluranti and Robertas Dama?evicius
In this Internet age, there are increasingly many threats to the security and safety of users daily. One of such threats is malicious software otherwise known as malware (ransomware, Trojans, viruses, etc.). The effect of this threat can lead to loss or ...
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Eman Ahmed,Amin A. Sorrour,Mohamed A. Sobh,Ayman M. Bahaa-Eldin
Pág. pp. 113 - 127
Malwares are increasing rapidly. The nature of distribution and effects of malwares attacking several applications requires a real-time response. Therefore, a high performance detection platform is required. In this paper, Hadoop is utilized to perform s...
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Chi Kin Lao,U Kin Che,Wei Chen,Sio Hang Pun,Peng Un Mak,Feng Wan,Mang I Vai
Pág. 18 - 26
In this paper, a miniature portable heart rate detector system is implemented by modern hardware ICs and simple sensor circuit with software executable on both PC and Android platform. The biosignal is first extracted via photoplethysmography (PPG) princ...
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