Malware detection using genetic programming

Le, Thi Anh; Chu, Thi Huong; Nguyen, Quang Uy; Nguyen, Xuan Phuong

doi:10.1109/cisda.2014.7035623

Cited by 13 publications

(8 citation statements)

References 21 publications

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“…Malware attacks cause significant financial misfortune consistently. In 2006, noxious virtual products lost $ 13.3 Billion internationally [36]. In this manner, protection against malware is vital for both PC clients and endeavors.…”

Section: Genetically Detection Of Malwarementioning

confidence: 99%

Advancements in Cyber Attacks and Security

Kalra*,

Upadhyay,

Patheja

2020

IJITEE

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Succeeding the entrustment of the personal computer by Ed Roberts in 1975, the absolute first virus became known to exist in a trifling time span of six years. The adversary was called Elk Cloner. Not long before they established themselves as adverse. Forthwith, attacks become frequent by the clock at all levels possible. It is a direct indication to how important cyber security is considering the prolonging effect and enormous expansion caused by such events in recent years. Another major issue is the huge scale arrangement of gadgets with almost no security or a wide default security design susceptible to cyber-attack. Cyber security is swiftly transforming into an article of everyday usage. As attacks are becoming sophisticated, we expect gadgets like advanced mobile phones, PCs and significantly progressively computerized frameworks to be secure and the protection of the data to be fundamental. Our paper explores the variety of Cyber Attacks, their methodologies and core ideas with the motivations behind it, also the ways to circumvent the threats by putting lights on the ongoing and future technologies to tackle advanced threats.

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Section: Genetically Detection Of Malwarementioning

confidence: 99%

Advancements in Cyber Attacks and Security

Kalra*,

Upadhyay,

Patheja

2020

IJITEE

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“…Several techniques have been developed in past in order to detect the presence of some suspicious behaviour of the file. [12] Makes use of the Genetic Programming for malware detection. A number of malwares were collected from the malware database available on the internet.…”

Section: Related Workmentioning

confidence: 99%

Feature extraction for enhanced malware detection using genetic algorithm

Srivastava¹,

Raj²

2018

IJET

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The use of internet has affected almost every field today. With the increase in use of internet, the number of malwares affecting the systems has also increased to a great deal. A number of techniques have been developed by the researchers in order to detect these malwares. The Malware Detection consists of two parts, the analysis part and the detection part. Malwares analysis can be categorized into Static analysis, Dynamic analysis and Hybrid Analysis. The Detection techniques can broadly be classified into Signature based techniques and Behaviour based techniques. A brief introduction of Malware Detection techniques is addressed here. The process of Feature Extraction plays a very important role in determining the efficiency and accuracy of the Malware Detection process. It aims at determining the subset of features that helps better differentiate between the malicious and benign files. We aim to provide a Feature Extraction process based on Genetic process that can be used for Malware Detection.

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“…Among them, GP is a well-known machine learning algorithm that is first proposed by Cramer [10] and later developed by Koza [11]. The very simple yet effective search framework of GP has made it so popular in the literature that it has been applied to solve a wide range of machine learning problems including malware detection [13], voice detection [14], software testing [15], and the like. However, in spite of a great adoption of evolutionary-based methods on different areas, the application of such methods on the domain of image classification area is still immature and hence needs to have further efforts.…”

Section: Introductionmentioning

confidence: 99%

An evolutionary-based image classification approach through facial attributes

Yılmaz¹,

Zalluhoglu²

2021

Turk J Elec Eng & Comp Sci

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With the recent development of the technology, it is seen that there is a significant increase in the studies on the analysis of human faces. Through the automatic analysing of the faces, it is possible to know the gender, emotional state, and even the identity of the people from an image. Of them, identity or face recognition has became the most important task which has been studied for a long time now as it is crucial to take measurement for public security, credit card verification, criminal identification, and the like. In this study, we have proposed an evolutionary-based framework that relies on genetic programming algorithm to evolve a binary-and multi-label image classifier program for gender classification, facial expression recognition, and face recognition tasks. The performance of the evolved programs has been compared with convolutional neural network, one of the most popular deep learning algorithms. The comparative results show that the proposed framework could better performance than the competitor algorithm. Therefore, it has been introduced to the research community as a new binary-and multi-label image classifier framework.

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Malware detection using genetic programming

Cited by 13 publications

References 21 publications

Advancements in Cyber Attacks and Security

Advancements in Cyber Attacks and Security

Feature extraction for enhanced malware detection using genetic algorithm

An evolutionary-based image classification approach through facial attributes

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