Titlebook: Information Security Applications; 21st International C Ilsun You Conference proceedings 2020 Springer Nature Switzerland AG 2020 Computer

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Detecting Block Cipher Encryption for Defense Against Crypto Ransomware on Low-End Internet of Thingkages were trained and evaluated through the deep learning network. The proposed method successful classifies the general software and potential ransomware virus by identifying the cryptography function call, which is evaluated in terms of recall rate, precision rate and F-measure.

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/i/image/465300.jpg

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Spatially Localized Perturbation GAN (SLP-GAN) for Generating Invisible Adversarial Patchesly, many studies about adversarial patches have been conducted that aims to misclassify the image classifier model by attaching patches to images. However, most of the previous research employs adversarial patches that are visible to human vision, making them easy to be identified and responded to.

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Detecting Block Cipher Encryption for Defense Against Crypto Ransomware on Low-End Internet of Thingo victims. In this paper, we present a novel defense against crypto ransomware by detecting block cipher encryption for low-end Internet of Things (IoT) environment. The proposed method analyzes the binary code of low-end microcontrollers in the base-station (i.e. server) and it is classified in eit

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CAPTCHAs Are Still in Danger: An Efficient Scheme to Bypass Adversarial CAPTCHAsots. However, attackers bypass the CAPTCHA schemes using deep learning (DL) based solvers. To defeat the attackers, CAPTCHA defense methods utilizing adversarial examples that are known for fooling deep learning models have been proposed..In this paper, we propose an efficient CAPTCHA solver that pe

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Unsupervised Intrusion Detection System for Unmanned Aerial Vehicle with Less Labeling Effort for the UAV. Past rule-based approaches provided a concrete baseline IDS model, and the machine learning-based method achieved a precise intrusion detection performance on the UAV with supervised learning models. However, previous methods have room for improvement to be implemented in the real worl