Feature Optimization for Run Time Analysis of Malware in Windows Operating System using Machine Learning Approach

Irshad, Areeba; Maurya, Ritesh; Dutta, Malay Kishore; Bürget, Radim; Uher, Václav

doi:10.1109/tsp.2019.8768808

Cited by 18 publications

(11 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SVM [126], DT [93], and N-grams [102] have the highest detection accuracy rate, at 100%, while NB [86] has the lowest detection accuracy rate, at 64.7%. However, only a limited dataset was used to examine the performance of DT and, Ngrams for malware detection.…”

Section: B Malware Detectionmentioning

confidence: 99%

“…Range of Detection Accuracy Rate K-Means [53], [54] 88% -> 90% NB [29], [38], [39], [40], [41], [54], [57], Pang, [58], [59], [60], [61], [74], [84], [91], [97], [101] 73.01% -98% SVM [36], [39], [40], [41], [45], [46], [47], [48], [51], [55], [61], [64], [65], [75], [78], [79], [80], [81], [82], [83], [84], [85], [86], [94], [95], [99], [100], [101] 64.7% -100% DT [29], [33], [34], [35]<...…”

Section: Related Workmentioning

confidence: 99%

“…This method is essentially a manual comparison of some systems' typical actions. Lowering the , [55], [58], [59], [62], [63], [65], [68], [72], [73], [74], [75], [78], [79], [80], [82], [86], [91], [92], [93], [95], [97], [103], [104], [106], [107], [108], [110], [111], [112], [114], [115], [116], [117], [118], [120], [122], [123], [126] Behaviorbased…”

Section: D: Specification-basedmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions

et al. 2023

View full text Add to dashboard Cite

Malware has emerged as a cyber security threat that continuously changes to target computer systems, smart devices, and extensive networks with the development of information technologies. As a result, malware detection has always been a major worry and a difficult issue, owing to shortcomings in performance accuracy, analysis type, and malware detection approaches that fail to identify unexpected malware attacks. This paper seeks to conduct a thorough literature review and offer a taxonomy of machine learning methods for malware detection that considers these problems by analyzing 77 chosen research works. The research investigates malware and machine learning in the context of cybersecurity, including malware detection taxonomy and machine learning algorithm classification into numerous categories. Furthermore, the taxonomy was used to evaluate the most recent machine learning algorithm and analysis. The paper also examines the obstacles and associated concerns encountered in malware detection and potential remedies. Finally, to address the related issues that would motivate researchers in their future work, an empirical study was utilized to assess the performance of several machine learning algorithms.INDEX TERMS Malware detection, machine learning algorithms, state-of-the-art.

show abstract

Section: B Malware Detectionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: D: Specification-basedmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions

et al. 2023

View full text Add to dashboard Cite

show abstract

“…A machine learning technique is presented towards optimizing the feature to be used in analyzing the malwares in windows [9]. The method uses genetic algorithm in feature selection where the behavior features are used in classification with Cuckoo search.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive-Feature Centric XGBoost Ensemble Classifier Model for Improved Malware Detection and Classification

Pavithra¹,

Selvakumarasamy²

2022

Journal of Cyber Security

View full text Add to dashboard Cite

Machine learning (ML) is often used to solve the problem of malware detection and classification, and various machine learning approaches are adapted to the problem of malware classification; still acquiring poor performance by the way of feature selection, and classification. To address the problem, an efficient novel algorithm for adaptive feature-centered XG Boost Ensemble Learner Classifier "AFC-XG Boost" is presented in this paper. The proposed model has been designed to handle varying data sets of malware detection obtained from Kaggle data set. The model turns the XG Boost classifier in several stages to optimize performance. At preprocessing stage, the data set given has been noise removed, normalized and tamper removed using Feature Base Optimizer "FBO" algorithm. The FBO would normalize the data points, as well as perform noise removal according to the feature values and their base information. Similarly, the performance of standard XG Boost has been optimized by adapting the selection using Class Based Principle Component Analysis "CBPCA" algorithm, which performs the selection according to the fitness of any feature for different classes. Based on the selected features, the method generates a regression tree for each feature considered. Based on the generated trees, the method performs classification by computing the tree-level ensemble similarity 'TLES' and the class-level ensemble similarity 'CLES'. Using both methods calculates the value of the class match similarity 'CMS' based on which the malware has been classified. The proposed approach achieves 97% accuracy in malware detection and classification with the less time complexity of 34 s for 75000 samples.

show abstract

“…But it is very difficult to detect this malicious software through this method. In [12], analyzing applications that first Contamination, It then extracted its features and selected the necessary ones and then categorized it. Genetic and SVM have used algorithms to perform these functions.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Surface Roughness and Corrosion Resistance of Wedmed Al 7075 Alloy

Rajnaveen*,

Rambabu,

Naik

et al. 2019

IJRTE

View full text Add to dashboard Cite

Android Malware is pernicious software. It is configured to attack the hardware such as android or mobile phone or smart phone. It is designed to exploit the flaw in specific mobile phone software technologies and operating systems. Nowadays, the mobile phone is the number one most vulnerable to malware attacks. Malware can be in the form of adware, Trojans, viruses, root kits and spyware. They delete important documents or steal protected data or bring software that is not authorized by the user. To solve this problem you need to categorize the applications on the mobile. The techniques used in machine learning are used here to differentiate between applications in mobile as good or bad. In this paper, present two methods as using the Genetic algorithm for feature selection and the Nearest Neighbor for classification.

show abstract

Feature Optimization for Run Time Analysis of Malware in Windows Operating System using Machine Learning Approach

Cited by 18 publications

References 5 publications

Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions

Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions

An Adaptive-Feature Centric XGBoost Ensemble Classifier Model for Improved Malware Detection and Classification

Enhancement of Surface Roughness and Corrosion Resistance of Wedmed Al 7075 Alloy

Contact Info

Product

Resources

About