Semantics-aware malware detection

By Mihai Christodorescu, Somesh Jha, Sanjit A. Seshia, Dawn Song, Randal E. Bryant. 2005

818 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

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Citations in the corpus

Automatic analysis of malware behavior using machine learning (2011, methodology): “Finally, semantics-aware analysis of malware binaries has been devised by [12] and later on extended by [48,49].”
PolyUnpack: Automating the Hidden-Code Extraction of Unpack-Executing Malware (2006, background): “, [9, 3]) that require either prior knowledge of the exact unpacking technique or explicit programming of the semantic behavior capturing all instances in an unpacking class (e.”
Polonium: Tera-Scale Graph Mining and Inference for Malware Detection (2011, methodology): “In 2008, Symantec, a global security software provider, reported that the release rate of malicious code and other unwanted programs may be exceeding that of legitimate software applications [1].”
Limits of Static Analysis for Malware Detection (2007, methodology): “However, based on their description, a more powerful static analyzer such as the one introduced by the same authors in [3] can undo these obfuscations.”
Detecting energy-greedy anomalies and mobile malware variants (2008, background): “, file name and a code value), thus being vulnerable to simple obfuscation, polymorphism [31, 33], and packing tech-”
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification (2015, methodology): “While instruction replacement techniques can be used to evade detection [18], their effects on malware classification tasks is limited, both for its rare use, and, consequently, for its negligible contribution to the computation of the statistics.”
A Comprehensive Review on Malware Detection Approaches (2020, neutral): “Semantics-aware malware detection is proposed in [73].”
Learning and Classification of Malware Behavior (2008, methodology): “These techniques are especially e«ective against byte-level content analysis [18] [20] and static malware analysis methods [8] [10] [12].”
Dynamic Analysis of Malicious Code (2006, background): “A number of static binary analysis techniques [2, 3, 8] have been introduced to detect different types of malware.”
Detection of Intrusions and Malware, and Vulnerability Assessment (2012, methodology): “Other researchers used these applications to demonstrate that they could use the tool they developed for application rewriting to automatically exploit a vulnerability to get virtual goods for free [16].”
Behavioral detection of malware on mobile handsets (2008, background): “, system events or resource-access) than system calls [21, 51] and machine instructions [34].”
Panorama: capturing system-wide information flow for malware detection and analysis (2007, background): “Although semantic-aware signature checking [11] improves its resilience to polymorphic and metamorphic variants, the inherent limitation of the signature based approach is its incapability of detecting previously unseen malware instances.”
Following Devil's Footprints: Cross-Platform Analysis of Potentially Harmful Libraries on Android and iOS (2016, background): “Conventionally, a program’s behavior is described by its API sequences, which has been extensively used in PHA detection [27].”
Ether: malware analysis via hardware virtualization extensions (2008, background): “Recent advances in malware analysis [17,24,25,33] show promise in understanding modern malware, but before these and other approaches can be used to determine what a malware instance does or might do, the runtime behavior of that instance and/or an unobstructed view of its code must be obtained.”
Detection of Malicious Code Variants Based on Deep Learning (2018, methodology): “[4] proposed a novel malware-detection algorithm that used trace semantics to characterize the behavior of malware.”
TTAnalyze: A Tool for Analyzing Malware (2006, background): “A number of static binary analysis techniques (Christodorescu & Jha, 2003; Christodorescu, Jha, Seshia, Song, & Bryant, 2005; Kruegel, Robertson,& Vigna, 2004)have been introducedto detect differenttypes of malware.”
Semantics-Aware Android Malware Classification Using Weighted Contextual API Dependency Graphs (2014, neutral): “D.2.4 [Software Engineering]: Software/Program Verification— Validation; D.4.6 [Operating Systems]: Security and Protection— Invasive software”