SAGMAD—A Signature Agnostic Malware Detection System Based on Binary Visualisation and Fuzzy Sets

Saridou, Betty; Rose, Joseph; Shiaeles, Stavros; Papadopoulos, Basil K.

doi:10.3390/electronics11071044

Cited by 14 publications

(5 citation statements)

References 71 publications

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“…Real-time detection, particularly in streaming environments, is becoming imperative to swiftly identify and counteract malware propagation. As the volume of malware samples and feature spaces continues to expand, scalability concerns must be addressed [44,[86][87][88][89][90][91][92][93][94][95][96][97][98].…”

Section: Open Challengesmentioning

confidence: 99%

Deep learning-powered malware detection in cyberspace: a contemporary review

Redhu,

Choudhary,

Srinivasan

et al. 2024

Front. Phys.

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This article explores deep learning models in the field of malware detection in cyberspace, aiming to provide insights into their relevance and contributions. The primary objective of the study is to investigate the practical applications and effectiveness of deep learning models in detecting malware. By carefully analyzing the characteristics of malware samples, these models gain the ability to accurately categorize them into distinct families or types, enabling security researchers to swiftly identify and counter emerging threats. The PRISMA 2020 guidelines were used for paper selection and the time range of review study is January 2015 to Dec 2023. In the review, various deep learning models such as Recurrent Neural Networks, Deep Autoencoders, LSTM, Deep Neural Networks, Deep Belief Networks, Deep Convolutional Neural Networks, Deep Generative Models, Deep Boltzmann Machines, Deep Reinforcement Learning, Extreme Learning Machine, and others are thoroughly evaluated. It highlights their individual strengths and real-world applications in the domain of malware detection in cyberspace. The review also emphasizes that deep learning algorithms consistently demonstrate exceptional performance, exhibiting high accuracy and low false positive rates in real-world scenarios. Thus, this article aims to contribute to a better understanding of the capabilities and potential of deep learning models in enhancing cybersecurity efforts.

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Section: Open Challengesmentioning

confidence: 99%

Deep learning-powered malware detection in cyberspace: a contemporary review

Redhu,

Choudhary,

Srinivasan

et al. 2024

Front. Phys.

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“…Deveci et al, [10] showed the Delphi method for interval type-2 fuzzy evidence. Saridou et al, [11] industrialized the recognition systems created on fuzzy logic. Many decision-makers have used the theory of FS in a choice of contexts, such as artificial networks, game theory, pattern recognition, and medicine.…”

Section: Introductionmentioning

confidence: 99%

Multi-Attribute Decision-Making for T-Spherical Fuzzy Information Utilizing Schweizer-Sklar Prioritized Aggregation Operators for Recycled Water

Sarfraz

2024

Decis. Mak. Adv.

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To handle problematic and ambiguous data, Schweizer and Sklar added a parameter p in 1960, which helped to develop the theory of SS t-norm (SSTN) and t-conorm (SSTCN). The parameter p=-1.1 can be used to easily derive the information of the Hamacher and Lukasiewicz t-norms. Furthermore, prioritized aggregation operators (PAOs) choose which data will be collected into a singleton set. The main contribution of this work is the construction of new aggregation operators for T-spherical fuzzy (T-SF) information based on SS t-norm and t-conorm. Moreover, the fundamental characteristics of the operators are identified. Further, we developed MADM (Multi-Attribute Decision-Making) models and deduced several useful properties from the operators T-SFSSPA, T-SFSSWPA, T-SFSSPG, and T-SFSSWPG. Finally, using an actual case study, we were able to draw the conclusion that, in comparison to the ground-breaking and current methods to enhance the value and capability of the diagnosed operators, the proposed MADM algorithm performs noticeably better than the operators in place for resolving the water recycling problem in a way that is easy to understand.

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“…For this reason, two feature selection strategies, static and dynamic, are proposed for the best possible malware classification in the used dataset. Using fuzzy logic [27,28] in conjunction with metaheuristic optimization, a twostage feature selection strategy is proposed for selecting dynamic features. To detect and categorize An-droid malware applications, a hybrid model based on fuzzy optimization mixed with meta-heuristic optimization methods, hybrid of enhanced MFO [29] and MVO [30] is evaluated as wrappers.…”

Section: Introductionmentioning

confidence: 99%

DroidDetectMW: A Hybrid Intelligent Model for Android Malware Detection

et al. 2023

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Malicious apps specifically aimed at the Android platform have increased in tandem with the proliferation of mobile devices. Malware is now so carefully written that it is difficult to detect. Due to the exponential growth in malware, manual methods of malware are increasingly ineffective. Although prior writers have proposed numerous high-quality approaches, static and dynamic assessments inherently necessitate intricate procedures. The obfuscation methods used by modern malware are incredibly complex and clever. As a result, it cannot be detected using only static malware analysis. As a result, this work presents a hybrid analysis approach, partially tailored for multiple-feature data, for identifying Android malware and classifying malware families to improve Android malware detection and classification. This paper offers a hybrid method that combines static and dynamic malware analysis to give a full view of the threat. Three distinct phases make up the framework proposed in this research. Normalization and feature extraction procedures are used in the first phase of pre-processing. Both static and dynamic features undergo feature selection in the second phase. Two feature selection strategies are proposed to choose the best subset of features to use for both static and dynamic features. The third phase involves applying a newly proposed detection model to classify android apps; this model uses a neural network optimized with an improved version of HHO. Application of binary and multi-class classification is used, with binary classification for benign and malware apps and multi-class classification for detecting malware categories and families. By utilizing the features gleaned from static and dynamic malware analysis, several machine-learning methods are used for malware classification. According to the results of the experiments, the hybrid approach improves the accuracy of detection and classification of Android malware compared to the scenario when considering static and dynamic information separately.

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SAGMAD—A Signature Agnostic Malware Detection System Based on Binary Visualisation and Fuzzy Sets

Cited by 14 publications

References 71 publications

Deep learning-powered malware detection in cyberspace: a contemporary review

Deep learning-powered malware detection in cyberspace: a contemporary review

Multi-Attribute Decision-Making for T-Spherical Fuzzy Information Utilizing Schweizer-Sklar Prioritized Aggregation Operators for Recycled Water

DroidDetectMW: A Hybrid Intelligent Model for Android Malware Detection

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