Droid-Sec

By Zhenlong Yuan, Yongqiang Lu, Zhaoguo Wang, Yibo Xue. 2014

262 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Software, Privacy & Security, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Software

In Privacy & Security

In Signal Processing

Citations in the corpus

Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks (2015, neutral): “As a result, DNNs are used in many settings [1], [2], [3], some of which are increasingly security-sensitive [4], [5], [6].”
Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “[109] proposed a DL framework to identify malwares in Android apps.”
Defeating Image Obfuscation with Deep Learning (2016, methodology): “Deep neural networks have been used for malware classiﬁcation in general [8, 37] and for speciﬁcally detecting Android malware [53].”
DeepXplore: Automated Whitebox Testing of Deep Learning Systems (2017, background): “These advances have led to widespread adoption and deployment of DL in security- and safety-critical systems such as self-driving cars [10], malware detection [88], and aircraft collision avoidance systems [35].”
Adversarial Examples: Attacks and Defenses for Deep Learning (2017, neutral): “malware [105]–[108].”
DeepFense: Online Accelerated Defense Against Adversarial Deep Learning (2017, background): “state-of-the-art deep neural networks are widely used in various scientiﬁc ﬁelds ranging from speech recognition [4], [5] and computer vision [6], [7] to ﬁnancial fraud [8] and malware detection [9], [10]. These applications have been mainly developed with an implicit security assumption about the g”
Learning Adversary-Resistant Deep Neural Networks (2016, unknown): “[38] and self-driving cars [10], [14]. In the domain of cybersecurity, security companies have demonstrated that deep learning could offer a far better way to classify all types of malware [8], [30], [39]. However, though powerful, deep neural architectures, as well as all other machine learning met”
DL-Droid: Deep learning based android malware detection using real devices (2019, methodology): “Droid-Sec Yuan et al. (2014) is one of the ﬁrst frameworks that applied deep learning to classify Android malware, achieving 96.5% accuracy using 200 features extracted by means of a hybrid (static + dynamic) approach evaluated on 250 clean and 250 malware Android apps.”
A Survey of Deep Learning Methods for Cyber Security (2019, methodology): “References [76,77] developed DL-based detectors of malicious Android applications using features from static and dynamic analyses, with the second study improving on the ﬁrst.”
A comprehensive survey on model compression and acceleration (2020, background): “Moreover, mobile computing has a wide range of applications available in every domain (Yuan et al. 2014; Vu et al. 2016).”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “However, unlike in MLPs where only the input vector can affect the hidden units, with RBMs the state of v can affect the state of h, and vice versa.”
Automated Crowdturfing Attacks and Defenses in Online Review Systems (2017, background): “Work in this category mainly includes proposals to improve network intrusion detection [8, 25], malware classification [62, 66, 81], and passwordbased authentication systems [39].”
Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss (2019, unknown): “, 2017], and malware detection [Yuan et al., 2014].”
DroidDetector: Android Malware Characterization and Detection Using Deep Learning (2016, unknown): “[23].”