Titlebook: Artificial Intelligence for Cybersecurity; Mark Stamp,Corrado Aaron Visaggio,Fabio Di Troia Book 2022 The Editor(s) (if applicable) and Th

Contort

Assessing the Robustness of an Image-Based Malware Classifier with Smali Level Perturbations Techniqf previously known threats, they are not able to catch new malware and also generalize their knowledge to different variants of the same known malware. Deep learning approaches have been adopted to address this problem, and one of the most promising attempts is based on the representation of malware

Phonophobia

Detecting Botnets Through Deep Learning and Network Flow Analysisich uniquely distinguishes it from other typical malware threats. The C&C server sends commands to the botnets to execute malicious activities using common Internet protocols, such as Hypertext transfer (HTTP), and Internet Relay Chat (IRC). Since these protocols are common, detecting botnet activit

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Machine Learning for Malware Evolution Detectiono that appropriate countermeasures can be undertaken. In this research, we perform a variety of experiments on a significant number of malware families to determine when malware evolution is likely to have occurred. All of the evolution detection techniques that we consider are based on machine lear

两栖动物

Gambling for Success: The Lottery Ticket Hypothesis in Deep Learning-Based Side-Channel Analysisural networks that perform well for any setting. Based on the developed neural network architectures, we can distinguish between small neural networks that are easier to tune and less prone to overfitting but could have insufficient capacity to model the data. On the other hand, large neural network

错误

Evaluating Deep Learning Models and Adversarial Attacks on Accelerometer-Based Gesture Authenticatio techniques have relied on classical machine learning techniques, but recently, deep learning techniques have been applied this problem. Although prior research has shown that deep learning models are vulnerable to adversarial attacks, relatively little research has been done in the adversarial doma

Initiative

Clickbait Detection for YouTube Videosenue for the person who posted the video. This creates an incentive for people to post clickbait videos, in which the content might deviate significantly from the title, description, or thumbnail. In effect, users are tricked into clicking on clickbait videos. In this research, we consider the chall

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