Droid-Sec

Yuan, Zhenlong; Lü, Yongqiang; Wang, Zhaoguo; Xue, Yibo

doi:10.1145/2619239.2631434

Cited by 220 publications

(33 citation statements)

References 2 publications

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“…DL is considered to be an improvement of artificial neural networks, consisting of more layers that permit higher levels of abstraction and improved predictions from data [60]. Actually, the DL model can be trained in various ways with different approaches or algorithms [61]. Thus, a DL architecture becomes a multilayer stack of simple modules subject to learning, and many of which compute non-linear input-output mappings or classifications.…”

Section: Deep Learning Principlesmentioning

confidence: 99%

Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review

et al. 2020

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Human healthcare is one of the most important topics for society. It tries to find the correct effective and robust disease detection as soon as possible to patients receipt the appropriate cares. Because this detection is often a difficult task, it becomes necessary medicine field searches support from other fields such as statistics and computer science. These disciplines are facing the challenge of exploring new techniques, going beyond the traditional ones. The large number of techniques that are emerging makes it necessary to provide a comprehensive overview that avoids very particular aspects. To this end, we propose a systematic review dealing with the Machine Learning applied to the diagnosis of human diseases. This review focuses on modern techniques related to the development of Machine Learning applied to diagnosis of human diseases in the medical field, in order to discover interesting patterns, making non-trivial predictions and useful in decision-making. In this way, this work can help researchers to discover and, if necessary, determine the applicability of the machine learning techniques in their particular specialties. We provide some examples of the algorithms used in medicine, analysing some trends that are focused on the goal searched, the algorithm used, and the area of applications. We detail the advantages and disadvantages of each technique to help choose the most appropriate in each real-life situation, as several authors have reported. The authors searched Scopus, Journal Citation Reports (JCR), Google Scholar, and MedLine databases from the last decades (from 1980s approximately) up to the present, with English language restrictions, for studies according to the objectives mentioned above. Based on a protocol for data extraction defined and evaluated by all authors using PRISMA methodology, 141 papers were included in this advanced review.

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Section: Deep Learning Principlesmentioning

confidence: 99%

Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review

et al. 2020

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“…Recently, there has been a push towards deep learning based approaches that does not require hand-crafted feature engineering. Some of these techniques, such as classifying Android malware [45], make use of low level data sources that are not accessible from browsers. Other security tools deploy directly on the browser [11], [28] and also highlight the benefits of an inbrowser defense.…”

Section: Behavioral Analysis Using Learningmentioning

confidence: 99%

Browser-Based Deep Behavioral Detection of Web Cryptomining with CoinSpy

Kelton¹,

Balasubramanian²,

Raghavendra³

et al. 2020

Proceedings 2020 Workshop on Measurements, Attacks, and Defenses for the Web

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Although the cryptocurrency hype over the past year may be seen by some as a benign social fad, to the Web community it is the center point for a series of ethically dubious ransomware attacks. Browser based cryptomining, or cryptojacking has gained widespread attention. Cryptojacking consists of Web servers delivering cryptocurrency mining scripts to clients, and using the client resources to play part in a distributed coin mining scheme. Although Web server operators defend the ethics of their involvement by quoting mining as a substitute for advertisement revenue, these scripts can hog massive amounts of client-side resources and can be delivered without client consent, presenting a high potential for abuse. Regardless of how ethical these campaigns are, what remains constant is the need for their detection. While there has been an array of work in defending against such cryptojacking campaigns, these defenses remain quite preliminary. We present CoinSpy, an entirely in-browser tool built using deep learning techniques for the detection of cryptomining activity within Web pages. A key challenge is that there is limited visibility into the client resource usage from within the browser sandbox. CoinSpy extracts several signals from information available from the browser and combines them using deep learning to build a powerful cryptojacking classifier. We argue why CoinSpy is the most robust defense against current and future cryptojacking attacks as compared to recent work, and show that it can detect various cryptojacking campaigns with 97% accuracy.

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“…In [41], the authors proposed a machine learning approach to the detection of malware in android using 200 features. These features were taken out of both the static and dynamic analysis of android applications.…”

Section: Review Of Past Workmentioning

confidence: 99%