4.7 Article

TI-MVD: A temporal interaction-enhanced model for malware variants detection

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Artificial Intelligence

Metagraph-Based Learning on Heterogeneous Graphs

Yuan Fang et al.

Summary: This study introduces a novel and effective approach to proximity search on graphs using the concept of metagraphs, outperforming competitors significantly and improving computation efficiency with a matching algorithm. Additionally, a general representation for nodes and edges is proposed to support various machine learning tasks, consistently outperforming existing techniques.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2021)

Article Computer Science, Artificial Intelligence

Semi-supervised two-phase familial analysis of Android malware with normalized graph embedding

Qian Li et al.

Summary: The article introduces a system for analyzing the familial of Android malware, named GSFDroid. This system utilizes graph features and Graph Convolutional Networks to embed features, improving the efficiency of downstream analytics tasks. By using a simple graph feature normalization method to standardize embedded APK features, the system effectively clusters new malware samples from unknown families.

KNOWLEDGE-BASED SYSTEMS (2021)

Article Computer Science, Artificial Intelligence

Leveraging malicious behavior traces from volatile memory using machine learning methods for trusted unknown malware detection in Linux cloud environments

Tomer Panker et al.

Summary: This paper presents the first trusted framework for detecting unknown malware in Linux VM cloud-environments using machine-learning algorithms and informative traces from volatile memory. The framework was rigorously evaluated in experiments and showed high accuracy in detecting unknown malware and categorizing them by attack category.

KNOWLEDGE-BASED SYSTEMS (2021)

Article Computer Science, Artificial Intelligence

IGE plus : A Framework for Learning Node Embeddings in Interaction Graphs

Yao Zhang et al.

Summary: Node embedding techniques are gaining attention for producing continuous and low-dimensional features. Real-world applications involve bipartite graphs with dynamic and attributed edges, known as attributed interaction graphs. Learning embeddings in such graphs is challenging due to the dynamics and heterogeneous attributes of edges.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2021)

Article Computer Science, Theory & Methods

Malware Dynamic Analysis Evasion Techniques: A Survey

Amir Afianian et al.

ACM COMPUTING SURVEYS (2020)

Article Computer Science, Artificial Intelligence

LN-SNE: Log-Normal Distributed Stochastic Neighbor Embedding for Anomaly Detection

Zahra Ghafoori et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2020)

Article Computer Science, Theory & Methods

Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection

Xiao Chen et al.

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY (2020)

Article Computer Science, Information Systems

A feature-hybrid malware variants detection using CNN based opcode embedding and BPNN based API embedding

Jixin Zhang et al.

COMPUTERS & SECURITY (2019)

Proceedings Paper Computer Science, Information Systems

Heterogeneous Graph Neural Network

Chuxu Zhang et al.

KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING (2019)

Proceedings Paper Computer Science, Information Systems

Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks

Srijan Kumar et al.

KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING (2019)

Proceedings Paper Computer Science, Artificial Intelligence

An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation

Yanru Qu et al.

1ST INTERNATIONAL WORKSHOP ON DEEP LEARNING PRACTICE FOR HIGH-DIMENSIONAL SPARSE DATA WITH KDD (DLP-KDD 2019) (2019)

Proceedings Paper Computer Science, Theory & Methods

Heterogeneous Graph Attention Network

Xiao Wang et al.

WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019) (2019)

Article Computer Science, Artificial Intelligence

Scalable Detection of Server-Side Polymorphic Malware

Yehonatan Cohen et al.

KNOWLEDGE-BASED SYSTEMS (2018)

Proceedings Paper Computer Science, Artificial Intelligence

Graph Convolutional Neural Networks for Web-Scale Recommender Systems

Rex Ying et al.

KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING (2018)

Proceedings Paper Computer Science, Artificial Intelligence

Gotcha - Sly Malware! Scorpion: A Metagraph2vec Based Malware Detection System

Yujie Fan et al.

KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING (2018)

Article Computer Science, Information Systems

Ensuring data confidentiality via plausibly deniable encryption and secure deletion - a survey

Qionglu Zhang et al.

CYBERSECURITY (2018)

Article Computer Science, Theory & Methods

Monet: A User-Oriented Behavior-Based Malware Variants Detection System for Android

Mingshen Sun et al.

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY (2017)

Article Mathematics, Applied

Using Dalvik opcodes for malware detection on android

Jose Gaviria de la Puerta et al.

LOGIC JOURNAL OF THE IGPL (2017)

Article Computer Science, Information Systems

Opcode sequences as representation of executables for data-mining-based unknown malware detection

Igor Santos et al.

INFORMATION SCIENCES (2013)

Article Computer Science, Information Systems

Code obfuscation techniques for metamorphic viruses

Jean-Marie Borello et al.

JOURNAL OF COMPUTER VIROLOGY AND HACKING TECHNIQUES (2008)