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Cryptovirology

About: Cryptovirology is a research topic. Over the lifetime, 1085 publications have been published within this topic receiving 42444 citations.


Papers
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Proceedings ArticleDOI
20 May 2012
TL;DR: Systematize or characterize existing Android malware from various aspects, including their installation methods, activation mechanisms as well as the nature of carried malicious payloads reveal that they are evolving rapidly to circumvent the detection from existing mobile anti-virus software.
Abstract: The popularity and adoption of smart phones has greatly stimulated the spread of mobile malware, especially on the popular platforms such as Android. In light of their rapid growth, there is a pressing need to develop effective solutions. However, our defense capability is largely constrained by the limited understanding of these emerging mobile malware and the lack of timely access to related samples. In this paper, we focus on the Android platform and aim to systematize or characterize existing Android malware. Particularly, with more than one year effort, we have managed to collect more than 1,200 malware samples that cover the majority of existing Android malware families, ranging from their debut in August 2010 to recent ones in October 2011. In addition, we systematically characterize them from various aspects, including their installation methods, activation mechanisms as well as the nature of carried malicious payloads. The characterization and a subsequent evolution-based study of representative families reveal that they are evolving rapidly to circumvent the detection from existing mobile anti-virus software. Based on the evaluation with four representative mobile security software, our experiments show that the best case detects 79.6% of them while the worst case detects only 20.2% in our dataset. These results clearly call for the need to better develop next-generation anti-mobile-malware solutions.

2,122 citations

Proceedings ArticleDOI
17 Oct 2011
TL;DR: The method is shown to be an effective means of isolating the malware and alerting the users of a downloaded malware, showing the potential for avoiding the spreading of a detected malware to a larger community.
Abstract: The sharp increase in the number of smartphones on the market, with the Android platform posed to becoming a market leader makes the need for malware analysis on this platform an urgent issue.In this paper we capitalize on earlier approaches for dynamic analysis of application behavior as a means for detecting malware in the Android platform. The detector is embedded in a overall framework for collection of traces from an unlimited number of real users based on crowdsourcing. Our framework has been demonstrated by analyzing the data collected in the central server using two types of data sets: those from artificial malware created for test purposes, and those from real malware found in the wild. The method is shown to be an effective means of isolating the malware and alerting the users of a downloaded malware. This shows the potential for avoiding the spreading of a detected malware to a larger community.

1,035 citations

Proceedings ArticleDOI
01 Dec 2007
TL;DR: A binary obfuscation scheme that relies on opaque constants, which are primitives that allow us to load a constant into a register such that an analysis tool cannot determine its value, demonstrates that static analysis techniques alone might no longer be sufficient to identify malware.
Abstract: Malicious code is an increasingly important problem that threatens the security of computer systems. The traditional line of defense against malware is composed of malware detectors such as virus and spyware scanners. Unfortunately, both researchers and malware authors have demonstrated that these scanners, which use pattern matching to identify malware, can be easily evaded by simple code transformations. To address this shortcoming, more powerful malware detectors have been proposed. These tools rely on semantic signatures and employ static analysis techniques such as model checking and theorem proving to perform detection. While it has been shown that these systems are highly effective in identifying current malware, it is less clear how successful they would be against adversaries that take into account the novel detection mechanisms. The goal of this paper is to explore the limits of static analysis for the detection of malicious code. To this end, we present a binary obfuscation scheme that relies on the idea of opaque constants, which are primitives that allow us to load a constant into a register such that an analysis tool cannot determine its value. Based on opaque constants, we build obfuscation transformations that obscure program control flow, disguise access to local and global variables, and interrupt tracking of values held in processor registers. Using our proposed obfuscation approach, we were able to show that advanced semantics-based malware detectors can be evaded. Moreover, our opaque constant primitive can be applied in a way such that is provably hard to analyze for any static code analyzer. This demonstrates that static analysis techniques alone might no longer be sufficient to identify malware.

838 citations

Proceedings ArticleDOI
28 Oct 2007
TL;DR: This work proposes a system, Panorama, to detect and analyze malware by capturing malicious information access and processing behavior, which separates these malicious applications from benign software.
Abstract: Malicious programs spy on users' behavior and compromise their privacy. Even software from reputable vendors, such as Google Desktop and Sony DRM media player, may perform undesirable actions. Unfortunately, existing techniques for detecting malware and analyzing unknown code samples are insufficient and have significant shortcomings. We observe that malicious information access and processing behavior is the fundamental trait of numerous malware categories breaching users' privacy (including keyloggers, password thieves, network sniffers, stealth backdoors, spyware and rootkits), which separates these malicious applications from benign software. We propose a system, Panorama, to detect and analyze malware by capturing this fundamental trait. In our extensive experiments, Panorama successfully detected all the malware samples and had very few false positives. Furthermore, by using Google Desktop as a case study, we show that our system can accurately capture its information access and processing behavior, and we can confirm that it does send back sensitive information to remote servers in certain settings. We believe that a system such as Panorama will offer indispensable assistance to code analysts and malware researchers by enabling them to quickly comprehend the behavior and innerworkings of an unknown sample.

796 citations

Proceedings ArticleDOI
08 May 2005
TL;DR: Experimental evaluation demonstrates that the malware-detection algorithm can detect variants of malware with a relatively low run-time overhead and the semantics-aware malware detection algorithm is resilient to common obfuscations used by hackers.
Abstract: A malware detector is a system that attempts to determine whether a program has malicious intent. In order to evade detection, malware writers (hackers) frequently use obfuscation to morph malware. Malware detectors that use a pattern-matching approach (such as commercial virus scanners) are susceptible to obfuscations used by hackers. The fundamental deficiency in the pattern-matching approach to malware detection is that it is purely syntactic and ignores the semantics of instructions. In this paper, we present a malware-detection algorithm that addresses this deficiency by incorporating instruction semantics to detect malicious program traits. Experimental evaluation demonstrates that our malware-detection algorithm can detect variants of malware with a relatively low run-time overhead. Moreover our semantics-aware malware detection algorithm is resilient to common obfuscations used by hackers.

791 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202380
2022173
20215
20202
20191
201815