Essays about: "malware dataset"
Showing result 1 - 5 of 11 essays containing the words malware dataset.
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1. Android Malware Detection Using Machine Learning
University essay from Blekinge Tekniska Högskola/Institutionen för datavetenskapAbstract : Background. The Android smartphone, with its wide range of uses and excellent performance, has attracted numerous users. Still, this domination of the Android platform also has motivated the attackers to develop malware. The traditional methodology which detects the malware based on the signature is unfit to discover unknown applications. READ MORE
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2. Evaluation of machine learning models for classifying malicious URLs
University essay from Högskolan i Gävle/DatavetenskapAbstract : Millions of new websites are created daily, making it challenging to determine which ones are safe. Cybersecurity involves protecting companies and users from cyberattacks. Cybercriminals exploit various methods, including phishing attacks, to trick users into revealing sensitive information. READ MORE
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3. Fast Classification of Obfuscated Malware with an Artificial Neural Network
University essay from KTH/DatavetenskapAbstract : Malware has posed a problem ever since the first variant was created in the 1980s. As malware detection techniques have advanced, malware developers have in turn found better ways to hide and obfuscate malware. Machine learning (ML) has seen great expansion into many fields over the last years, this includes the field of cybersecurity. READ MORE
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4. Discovering and masking environmental features in modern sandboxes
University essay from Blekinge Tekniska Högskola/Institutionen för datavetenskapAbstract : Background. The awareness of cyber attacks in businesses is increasing with the rising number of cyber incidents for businesses. With nearly 350 000 new malware detected per day, there is a big incentive to allocate resources to company infrastructure to mitigate malware. These solutions require scalability not to become bottlenecks and expensive. READ MORE
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5. Increased evasion resilience in modern PDF malware detectors : Using a more evasive training dataset
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : The large scale usage of the PDF coupled with its versatility has made the format an attractive target for carrying and deploying malware. Traditional antivirus software struggles against new malware and PDF's vast obfuscation options. In the search of better detection systems, machine learning based detectors have been developed. READ MORE
