To Incorporate Sequential Dynamic Features in Malware Detection Engines (original) (raw)

A study of signature-based and behaviour-based malware detection approaches

International Journal of Advance Research, Ideas and Innovations in Technology, 2019

In the present scenario, one of the biggest threats to computers and mobile devices is malware. There are two approaches to detect and prevent malware infections: Signature-based and Behavior-based approach. The Signature-based approach is more widely used, but this outlook can only be used to detect existing and old malware and it does not allow understanding future threats and militating against these threats. The Behavior based approach uses a dynamic analysis method to understand and classify malware. However, it is still not as favored as its counterpart due to its limiting behavior. In this paper, we study both Signature-based and Behavior-based approaches to determine which the favorable approach to malware detection is.

Malware Detection and Signature Generation

2020

Now a day, the malware detection is needful to enhance the performance of the systems and omit the effect of malware to system. The conventional signature-based detection of malware did not detect a major of new variants. This paper presented a hybrid technique for automatic malware signature generation and classification. The hybrid method is named as a ANFIS-SSA approach. Using this observation, we present a hybrid method for detection of malware using the correlation between the semantics of the malware and its API calls. Here, develops a base signature for a whole malware class more than for a solitary specimen of malware. The signature can able of find out even advanced variants and unknown which related to that class. Here, demonstrates our method on some well-known malware classes and presented that any advanced variants classes is detected from the base signature.

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

Comparative Analysis of Feature Extraction Methods of Malware Detection

2015

Recent years have encountered massive growth in malwares which poses a severe threat to modern computers and internet security. Existing malware detection systems are confronting with unknown malware variants. Recently developed malware detection systems investigated that the diverse forms of malware exhibit similar patterns in their structure with minor variations. Hence, it is required to discriminate the types of features extracted for detecting malwares. So that potential of malware detection system can be leveraged to combat with unfamiliar malwares. We mainly focus on the categorization of features based on malware analysis. This paper highlights general framework of malware detection system and pinpoints strengths and weaknesses of each method. Finally we presented overview of performance of present malware detection systems based on features.

Classification System for Malware Identification and Detection

International Journal of Advanced Research in Computer Science and Software Engineering, 2017

Signature based malware detection systems have been a tremendously utilized reaction to the unavoidable issue of malware. Distinguishing proof of malware variations is basic to a detection system furthermore, is made conceivable by distinguishing invariant qualities in related examples. To characterize the packed and polymorphic malware, this paper proposes a novel system, named malwise, for malware characterization utilizing a quick application level emulator to switch the code pressing change, furthermore, two flowgraph coordinating algorithms to perform characterization. A correct flowgraph coordinating algorithm is utilized that utilizations string based signatures, and can recognize malware with close constant execution. Furthermore, a more powerful surmised stream chart coordinating algorithm is recommended that uses the decompilation method of organizing to produce string based signatures managable to the string alter remove. We utilize genuine and manufactured malware to show the adequacy what's more, productivity of Malwise. Utilizing more than 15,000 genuine malware, gathered from honeypots, the viability is approved by demonstrating that there is an 88% likelihood that new malware is distinguished as a variation of existing malware. The effectiveness is shown from a littler example set of malware where 86% of the specimens can be classified in under 1.3 seconds.

Detecting malicious files using non-signature-based methods

International Journal of Information and Computer Security, 2014

Malware or malicious code intends to harm the computer systems without the knowledge of system users. Malware are unknowingly installed by naïve users while browsing the internet. Once installed, the malicious programs perform unintentional activities like: a) steal user name, password; b) install spy software to provide remote access to the attackers; c) flood spam messages; d) perform denial of service attacks, etc. With the emergence of metamorphic malware (that uses complex obfuscation techniques), signature-based detectors fail to identify new variants of malware. In this paper, we investigate non-signature techniques for malware detection and demonstrate methods of feature selection that are best suited for detection purposes. Features are produced using mnemonic n-grams and instruction opcodes (opcodes along with addressing modes). The redundant features are eliminated using class-wise document frequency, scatter criterion and principal component analysis (PCA). The experiments are conducted on the malware dataset collected from VX Heavens and benign executables (gathered from fresh installation of Windows XP operating system and other utility software's). The experiments also demonstrate that proposed methods that do not require signatures are effective in identifying and classifying morphed malware.

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.

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 Survey of different machine learning models for static and dynamic malware detection

2020

Malicious software (malware) plays a vital role in cybercrime security. As the number of malicious attacks and its target sources is increasing, it is difficult to find and prevent the attack due to its change in behaviour. Most of the traditional malware detection models are based on the statistical, analytical, and machine learning models. Detection of malware usually utilizes virus signature methods to defend against malicious software. Most antivirus tools to categorize malware depend on regular expression and pattern. Antivirus is less likely to update their databases to detect and prevent malware as file features have to update a newly created malware. The practically maximum human effort was required in order to generate attack signatures. In this paper, different types of malware detection models and their problems are discussed. This paper provides an extensive survey on the malware attack detection using traditional supervised, unsupervised models. Different types of malwa...