Corpus ID: 53218238

AiDroid: When Heterogeneous Information Network Marries Deep Neural Network for Real-time Android Malware Detection

  title={AiDroid: When Heterogeneous Information Network Marries Deep Neural Network for Real-time Android Malware Detection},
  author={Yanfang Ye and Shifu Hou and Lingwei Chen and Jingwei Lei and Wenqiang Wan and Jiabin Wang and Qi Xiong and Fudong Shao},
The explosive growth and increasing sophistication of Android malware call for new defensive techniques that are capable of protecting mobile users against novel threats. In this paper, we first extract the runtime Application Programming Interface (API) call sequences from Android apps, and then analyze higher-level semantic relations within the ecosystem to comprehensively characterize the apps. To model different types of entities (i.e., app, API, IMEI, signature, affiliation) and the rich… Expand
A Survey of Android Malware Detection with Deep Neural Models
  • Junyang Qiu, Jun Zhang, Wei Luo, Lei Pan, S. Nepal, Yang Xiang
  • Computer Science
  • ACM Comput. Surv.
  • 2021
This survey aims to address the challenges in DL-based Android malware detection and classification by systematically reviewing the latest progress, including FCN, CNN, RNN, DBN, AE, and hybrid models, and organize the literature according to the DL architecture. Expand
Intelligent Malware Detection Using File-to-file Relations and Enhancing its Security against Adversarial Attacks
Intelligent Malware Detection Using File-to-file Relations and Enhancing its Security against Adversarial Attacks and improving its security against adversarial attacks is presented. Expand


HinDroid: An Intelligent Android Malware Detection System Based on Structured Heterogeneous Information Network
This paper represents the Android applications, related APIs, and their rich relationships as a structured heterogeneous information network (HIN) as well as creating higher-level semantics which require more effort for attackers to evade the detection of Android malware. Expand
Gotcha - Sly Malware!: Scorpion A Metagraph2vec Based Malware Detection System
A new HIN embedding model metagraph2vec is proposed on the first attempt to learn the low-dimensional representations for the nodes in HIN, where both the HIN structures and semantics are maximally preserved for malware detection. Expand
DroidDelver: An Android Malware Detection System Using Deep Belief Network Based on API Call Blocks
Using a real sample collection from Comodo Cloud Security Center, a comprehensive experimental study is performed to compare various malware detection approaches and promising experimental results demonstrate that DroidDelver which integrates the proposed method outperform other alternative Android malware detection techniques. Expand
DroidMiner: Automated Mining and Characterization of Fine-grained Malicious Behaviors in Android Applications
A new, complementary system, called DroidMiner, which uses static analysis to automatically mine malicious program logic from known Android malware, abstracts this logic into a sequence of threat modalities, and then seeks out these threat modality patterns in other unknown (or newly published) Android apps. Expand
DroidDolphin: a dynamic Android malware detection framework using big data and machine learning
Dolphin is proposed, a dynamic malware analysis framework which leverages the technologies of GUI-based testing, big data analysis, and machine learning to detect malicious Android applications. Expand
DroidMat: Android Malware Detection through Manifest and API Calls Tracing
A static feature-based mechanism to provide a static analyst paradigm for detecting the Android malware and shows that the recall rate of the approach is better than one of well-known tool, Androguard, published in Black hat 2011, which focuses on Android malware analysis. Expand
SecureDroid: Enhancing Security of Machine Learning-based Detection against Adversarial Android Malware Attacks
This paper explores the security of machine learning in Android malware detection on the basis of a learning-based classifier with the input of a set of features extracted from the Android applications (apps) and proposes an ensemble learning approach (named SecENS) by aggregating the individual classifiers that are constructed using the proposed feature selection method SecCLS. Expand
MADAM: Effective and Efficient Behavior-based Android Malware Detection and Prevention
MADAM is a novel host-based malware detection system for Android devices which simultaneously analyzes and correlates features at four levels: kernel, application, user and package, to detect and stop malicious behaviors. Expand
HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning
Empirical results show that HIN2Vec soundly outperforms the state-of-the-art representation learning models for network data, including DeepWalk, LINE, node2vec, PTE, HINE and ESim, by 6.6% to 23.8% of $micro$-$f_1$ in multi-label node classification and 5% to 70.8%, in link prediction. Expand
DepthLGP: Learning Embeddings of Out-of-Sample Nodes in Dynamic Networks
This work designs a high-order Laplacian Gaussian process (hLGP) to encode network properties, which permits fast and scalable inference, and designs a deep neural network to learn a nonlinear transformation from latent states of the hLGP to node embeddings. Expand