Malware and Malware Detection Techniques: A Survey (original) (raw)

Review on Malware and Malware Detection ‎Using Data Mining Techniques

JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences

Malicious software is any type of software or codes which hooks some: private information, data from the computer system, computer operations or(and) merely just to do malicious goals of the author on the computer system, without permission of the computer users. (The short abbreviation of malicious software is Malware). However, the detection of malware has become one of biggest issues in the computer security field because of the current communication infrastructures are vulnerable to penetration from many types of malware infection strategies and attacks. Moreover, malwares are variant and diverse in volume and types and that strictly explode the effectiveness of traditional defense methods like signature approach, which is unable to detect a new malware. However, this vulnerability will lead to a successful computer system penetration (and attack) as well as success of more advanced attacks like distributed denial of service (DDoS) attack. Data mining methods can be used to ove...

IJERT-Malware and Malware Detection Techniques : A Survey

International Journal of Engineering Research and Technology (IJERT), 2013

https://www.ijert.org/malware-and-malware-detection-techniques-a-survey https://www.ijert.org/research/malware-and-malware-detection-techniques-a-survey-IJERTV2IS120163.pdf Now a day's malicious program is a serious threat. It is developed to damage the computer system and some of them are spread over the connected system in the network or internet connection. Researchers are taking great efforts to produce anti-malware system with effective malware detection methods to protect computer system. Two basic approaches have been proposed for it i.e. signature-based and heuristic-based detection. These approaches detect known malware accurately but cannot detect the new, unknown malware. Recently different researchers have proposed malware detection system using data mining and machine learning methods to detect known as well as unknown malwares. In this paper, a detailed analysis has been conducted on the current state of malware infection and work done to improve the malware detection systems.

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

A Survey on Malware detection using ML

International Journal for Research in Applied Science & Engineering Technology (IJRASET) , 2022

This Malware detection is a field of computer security that deals with the study and prevention of malicious software. It is not the only way to defend a company against a cyber-attack. In order to be effective, companies should analyse their risk and identify the vulnerabilities. In this paper, we will examine different techniques used to detect computer malware and malicious websites as well as future directives in this area of study and also, we will discuss the growth in computer malware and how traditional methods of detection are being replaced by innovative techniques like behavioural-based model and Signature-based model. Future directives involve developing better security products in order to fight against cyber fraud which is on a rise in recent years especially in Asia Pacific region. With this increase in cyber frauds and other malicious activities, traditional methods are not enough to block computers from it as this method has many drawbacks. In order to tackle these issues, researchers have been developing new techniques such as heuristic analysis, static & dynamic analysis which can detect more than 90% of malware samples without any false positives or negatives.

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 Survey on Malware Detection Schemes Using on Machine Learning Techniques

Malware is a one kind of programming which can harm the network and it might likewise steal the individual data from the PC. Malware can be made by utilizing any programming dialect by the software engineer. It is exceptionally hard to characterize a malware with a solitary term or a solitary name. A malware can be considered as a vindictive programming or malcode or it is otherwise called a vindictive code .Malware do the heft of the nosy exercises on a framework furthermore, that spreads itself over the hosts in a system. Malware detection techniques can be characterized into 2 classifications - the static investigation systems and the dynamic examination procedures. The static systems include investigating the pairs straightforwardly or the figuring out. The code for examples is the same. This paper endeavors to give a brief study of all the work that has been done in the field of malware detection. Literature have properly evaluated and examined for their pros and cons.

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.

A Survey on Malware Attacks Analysis and Detected

International Research Journal of Innovations in Engineering and Technology

Malware is one of the biggest problems modern internet users face. Private data and pricey computing resources are seriously threatened by the rise in malware attacks. Anti-malware businesses rely on signatures, which do in fact involve regular expressions and strings, to find malware and its related families. Recent malware attacks in recent years have demonstrated that signature-based techniques are error-prone and easily avoided by sophisticated malware programs. This essay provides an introductory overview of malware and analysis techniques used, as well as detection techniques used by researchers.

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 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.