نتایج جستجو برای: malware detection

تعداد نتایج: 569207  

2006
MILA DALLA PREDA

The Problem. A malware is a program with a malicious behaviour, that is designed to replicate with no user consent and to damage software and/or data on infected machines. Malwares are generally classified according to their goals and propagation methods into viruses, worms, backdoors, Trojans, etc. A malware detector is a system that attempts to verify whether a program presents a malicious be...

2012
Fu Song Tayssir Touili

Over the past decade, malware costs more than $10 billion every year and the cost is still increasing. Classical signature-based and emulation-based methods are becoming insufficient, since malware writers can easily obfuscate existing malware such that new variants cannot be detected by these methods. Thus, it is important to have more robust techniques for malware detection. In our previous w...

2014
Heqing Huang Kai Chen Peng Liu Sencun Zhu Dinghao Wu

With the rapid increase in Android device popularity, a new evolving arms-race is happening between the malware writers and AntiVirus Detectors (AVDs) on the popular mobile system. In its latest comparison of AVDs, independent test lab AV-TEST reported that AVDs have around 95% malware recognition rate. However, as mobile systems are specially designed, we consider that the power of AVDs’ shoul...

2018
Mahmoud Hammad Joshua Garcia

The Android platform has been the dominant mobile platform in recent years resulting inmillions of apps and security threats against those apps. Anti-malware products aim to protect smartphone users from these threats, especially frommalicious apps. However, malware authors use code obfuscation on their apps to evade detection by anti-malware products. To assess the effects of code obfuscation ...

Journal: :CoRR 2017
Shun-Wen Hsiao Yeali S. Sun Meng Chang Chen

The proliferation of malwares have been attributed to the alternations of the original malware source codes. The malwares alternated from the same origin share some intrinsic behaviors and form a malware family. Expediently, identifying its malware family when a malware is first seen can provide useful clues to mitigating the threat. In this paper, a malware profiler (VMP) is proposed to profil...

Journal: :TinyToCS 2012
Collin Mulliner

Today’s malware contains sophisticated analysis countermeasures to protect itself against reverse engineering. Countermeasures fall into two categories: offline and runtime. Encryption and obfuscation of binaries are widely used offline protections. Therefore today, most analysis is done during runtime and so malware authors implement runtime countermeasures. Runtime countermeasures include ant...

2013
Antonios Atlasis

Despite advances in detection, malware remains an active and high-risk threat to organizations. Understanding the characteristics of malware traffic can be vital in detecting, as well as responding to an incident inside an organization. In this paper, over 20,000 PCAPS generated by known malware are explored to find these characteristics. The focus of the research is on HTTP traffic since this ...

2014
Milan Jain Punam Bajaj

Today computer field has gained a lot of importance in our day to day life to deal with many aspects like education, entertainment purpose etc. System security is warned by weapons named as malicious software to fulfill malicious intention of its authors. Malicious software known as malware is one of the common problem faced by the internet today. The key to detect these threats are also availa...

2011
Mojtaba Eskandari Sattar Hashemi

Metamorphic malware changes the syntax of its code in each infection. This process makes it extremely hard to detect. While the byte sequence of the metamorphic malware may be quite different from its parent, the main functionality of the malware has to stay the same. Therefore, traditional methods based on static signature detection cannot detect such malwares, and need to be designed semantic...

2014
Adrian Tang Simha Sethumadhavan Salvatore J. Stolfo

Recent works have shown promise in using microarchitectural execution patterns to detect malware programs. These detectors belong to a class of detectors known as signaturebased detectors as they catch malware by comparing a program’s execution pattern (signature) to execution patterns of known malware programs. In this work, we propose a new class of detectors — anomaly-based hardware malware ...

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