A survey of malware detection techniques (original) (raw)

Literature Survey on Different Malware Detection Techniques

Malware was essentially developed to alert vendors about their security bugs, however, with the rise of malicious intents, computer systems suffer intelligent types of malware which are classified under four categories. Encrypted, Oligomorphic, Polymorphic, and Metamorphic malwares. Each are based on evasive techniques utilizing mutation engines and encryption methods to bypass anti-viruses undetected. On the other hand, malware detection methods are categorized into three main sectors, anomaly-based, specificationbased, and signature-based. With each detection method having its benefits and limitations. This paper aims to help researchers have a complete overview of malware detection techniques with its advantages and disadvantages.

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 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 and Malware Detection Systems

International Journal of Computer Applications, 2013

Over the last decades, there were lots of studies made on malware and their countermeasures. The most recent reports emphasize that the invention of malicious software is rapidly increasing. Moreover, the intensive use of networks and Internet increases the ability of the spreading and the effectiveness of this kind of software. On the other hand, researchers and manufacturers making great efforts to produce anti-malware systems with effective detection methods for better protection on computers. In this paper, a detailed review has been conducted on the current situation of malware infection and the work done to improve anti-malware or malware detection systems. Thus, it provides an up-to-date comparative reference for developers of malware detection systems.

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.

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.

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.

Taxonomy of malware detection techniques: A systematic literature review

2016 14th Annual Conference on Privacy, Security and Trust (PST), 2016

Malware is an international software disease. Research shows that the effect of malware is becoming chronic. To protect against malware detectors are fundamental to the industry. The effectiveness of such detectors depends on the technology used. Therefore, it is paramount that the advantages and disadvantages of each type of technology are scrutinized analytically. This study's aim is to scrutinize existing publications on this subject and to follow the trend that has taken place in the advancement and development with reference to the amount of information and sources of such literature. Many of the malware programs are huge and complicated and it is not easy to comprehend the details. Dissemination of malware information among users of the Internet and also training them to correctly use anti-malware products are crucial to protecting users from the malware onslaught. This paper will provide an exhaustive bibliography of methods to assist in combating malware.

A SURVEY OF TECHNIQUES AND TOOLS FOR DETECTING AND CLASSIFYING MALWARE THREATS

2023

The rapid advancement of technology has brought about significant growth of the Internet and digital devices are becoming increasingly prevalent. However, it has also resulted in the proliferation of malware threats. The ongoing conflict between security analysts and malware developers is an incessant struggle, as the complexity of malware evolves in tandem with the rapid pace of innovation. This perpetual arms race requires constant vigilance and adaptation on the part of security experts in order to identify and neutralize emerging threats. In this seminar, we will survey various techniques and tools used for detecting and classifying malware threats. This includes both signature-based and behavior-based detection methods, as well as machine learning-based approaches. There are several techniques and tools that this seminar will cover but more focus will be on the windows operating system malware because most malware are created as window executable binary. Each of these methods has its own strengths and limitations, and the choice of which method to use will depend on the specific needs and requirements of the organization or individual. Regardless of the method used, it is important to stay up to date with the latest developments in malware detection and to employ multiple layers of protection to ensure the highest level of security against malware threats.

A Survey on Malware Detection and Analysis Tools

International Journal of Network Security & Its Applications, 2020

The huge amounts of data and information that need to be analyzed for possible malicious intent are one of the big and significant challenges that the Web faces today. Malicious software, also referred to as malware developed by attackers, is polymorphic and metamorphic in nature which can modify the code as it spreads. In addition, the diversity and volume of their variants severely undermine the effectiveness of traditional defenses that typically use signature-based techniques and are unable to detect malicious executables previously unknown. Malware family variants share typical patterns of behavior that indicate their origin and purpose. The behavioral trends observed either statically or dynamically can be manipulated by using machine learning techniques to identify and classify unknown malware into their established families. This survey paper gives an overview of the malware detection and analysis techniques and tools.