Literature Survey on Different Malware Detection Techniques (original) (raw)
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Comparative Analysis of Malware Detection Techniques Using Signature, Behaviour and Heuristics
IJCSIS July Vol 17 No. 7, 2019
The rapid development of internet technologies alongside the technological advancement in information and communication technology have made malware a major cyber threat at the moment. Malwares are software or files that cause harm to the legitimate computer files or the computer system itself and as such are frequently used as tools by hackers to breach cyber security techniques. Different techniques had been applied at various times to detect malwares but malware developers always bypassed these techniques by their various concealment strategies. Notably, traditional malware detection using signature technique cannot detect polymorphic viruses while behavioural technique cannot also detect metamorphic viruses. Whereas the heuristic detection techniques which employ machine learning and data mining algorithms are relatively efficient but they mostly have high rate of false positive. This research therefore comparatively analyses these three different malware detection techniques stating their upsides and downsides with a conclusion that no single detection technique is good enough for the detection of recent time malwares but a combination of two or three of them. Keywords: Malware, Cybersecurity, Hacking, Heuristics, software
A Review on Malware Detection and Analyzing Techniques
2018
Malware is not defined in single word. It is collection of malicious code or instructions which spread through the connected system or internet. It’s using for gain illegally economic benefits and to damage other computer or network system. Malware detection is an important role in the cyber security. At present some anti malware software are used to detect malware, these are signature-based methods who cannot provide accurate result of malware attacks. Many metamorphic and polymorphic techniques are used to conceal the behavior of malicious program. These are the serious challenges to global security threat. Presently various malware detection techniques are available such as Heuristic based, Signature based and behavior based techniques. Most of the anti virus vendor uses signature based detection techniques, who already have known and well documented data base of signature value. Obfuscation and polymorphism technique impede the primary stage detection.
A Survey Paper on Malware Detection Techniques
International Journal of Advanced Trends in Computer Science and Engineering, 2021
The invasion of machine learning on various field in engineering in recent days is quite astonishing. The recent growth in new malwares have put a burden on our traditional anti malwares that use signature based or heuristic based techniques to detect malwares as these either cannot detect zero-day malwares or it would be insufficient to detect a certain type of malware. So, we need to find some new technique to deal with this situation. In this survey paper we shall look into how machine learning can potentially be used as an anti-malware.
A Literature Study on Malware Detection Techniques
Abstract Faced with the treat of malicious attacks from malware, researchers are spending sleepless nights trying to come up with the most suitable detection technique that would eliminate these attacks and render the systems safe. From the time malware came into existence, a number of methods have been formulated to handle the different malware forms. The different detection techniques identified and used operate based on either of the two principles, which are signature-based or behaviour-based. While significant progress has been made, the challenge has remained to be the dynamic form of the malware. Every day there comes a different form of malware, making it difficult to have a single technique for detection. Recently, researchers have proposed malware detection systems using data mining and machine learning techniques. This paper, therefore, looks at all these techniques and compares the different techniques used in different platforms
Survey on Malware Detection Methods
Malwares are malignant software's .It is designed to damage computer systems without the knowledge of the owner using the system. Software's from reputable vendors also contain malicious code that affects the system or leaks information's to remote servers.Malware's includes computer viruses, spyware, dihonest ad-ware,rootkits,Trojans,dialers etc. The paper focuses on various Malware detection methods like signature based detection, reverse engineering of obfuscated code, to detect malicious nature.
A Comprehensive Review on Malware Detection Approaches
IEEE Access
According to the recent studies, malicious software (malware) is increasing at an alarming rate, and some malware can hide in the system by using different obfuscation techniques. In order to protect computer systems and the Internet from the malware, the malware needs to be detected before it affects a large number of systems. Recently, there have been made several studies on malware detection approaches. However, the detection of malware still remains problematic. Signature-based and heuristic-based detection approaches are fast and efficient to detect known malware, but especially signature-based detection approach has failed to detect unknown malware. On the other hand, behavior-based, model checking-based, and cloud-based approaches perform well for unknown and complicated malware; and deep learning-based, mobile devices-based, and IoT-based approaches also emerge to detect some portion of known and unknown malware. However, no approach can detect all malware in the wild. This shows that to build an effective method to detect malware is a very challenging task, and there is a huge gap for new studies and methods. This paper presents a detailed review on malware detection approaches and recent detection methods which use these approaches. Paper goal is to help researchers to have a general idea of the malware detection approaches, pros and cons of each detection approach, and methods that are used in these approaches.
Malware and Malware Detection Techniques: A Survey
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
Malicious software is a kind of software or codes which took some: private data, information from the PC framework, its tasks is to do only malicious objectives to the PC framework, without authorization of the PC clients. The effect of malicious software are worsen to the client. Malicious software i.e malwares are programs that are made to mischief, hinder or harm PCs, organizations and different assets related with it. Malwares are moved in PCs without the information on its proprietor. Presently malicious program is a serious threat. It is created to harm the PC framework and some of them are spread over the associated framework in the organization or web association. Analysts are making great efforts in malware framework field with compelling malware detection techniques to safeguard PC framework. Two essential methodologies have been proposed for it for example signature-based and heuristic-based detection. These methodologies distinguish known malware precisely yet can't distinguish the new, obscure malware. Recently various analysts have proposed malware identification framework utilizing data mining and machine learning strategies to distinguish between obscure and non-obscure malwares. In this paper, an detailed examination has been led on the present status of malware infection and work done for finding it.
A survey of malware detection techniques
Tehnical Report, Department of Computer Science, …, 2007
Malware is a worldwide epidemic. Studies suggest that the impact of malware is getting worse. Malware detectors are the primary tools in defense against malware. The quality of such a detector is determined by the techniques it uses. It is therefore imperative that we study malware detection techniques and understand their strengths and limitations. This survey examines 45 malware detection techniques and offers an opportunity to compare them against one another aiding in the decision making process involved with developing a secure application/system. The survey also provides a comprehensive bibliography as an aid to researchers in malware detection.
State of the art study on metamorphic malware detection techniques
Metamorphism is considered the last and most effective technique used by malware creators toavoid anti-malware detection systems. A malware is considered metamorphic when is able totransform its own code, therefore resulting on a new obtained representative signature which cannotbe found on anti virus databases. Current security computational systems, based on the use ofsignatures for detection, deal with ineffectiveness to detect metamorphic malware since themalicious code replicates with different signatures on each infection demanding fast andcontinuously updates of the antivirus signature bases. Due to the importance of security and the lackof accuracy on dynamically obfuscated malware detection, many researchers have deeply studiedthe field resulting on a wide amount of works, so the topic is considered well known however recentsecurity reports address the persistence of this problem. This article is on the aim to study differentapproaches in a state-of-the-art research on the area of metamorphic malware detection.
Tools and Techniques for Malware Detection and Analysis
ArXiv, 2020
One of the major and serious threats that the Internet faces today is the vast amounts of data and files which need to be evaluated for potential malicious intent. Malicious software, often referred to as a malware that are designed by attackers are polymorphic and metamorphic in nature which have the capability to change their code as they spread. Moreover, the diversity and volume of their variants severely undermine the effectiveness of traditional defenses which typically use signature based techniques and are unable to detect the previously unknown malicious executables. The variants of malware families share typical behavioral patterns reflecting their origin and purpose. The behavioral patterns obtained either statically or dynamically can be exploited to detect and classify unknown malware into their known families using machine learning techniques. This survey paper provides an overview of techniques and tools for detecting and analyzing the malware.