Packer classification based on association rule mining

Dam, Khanh Huu The; Given-Wilson, Thomas; Legay, Axel; Veroneze, Rosana

doi:10.1016/j.asoc.2022.109373

Cited by 10 publications

(4 citation statements)

References 31 publications

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“…The method demonstrated an average accuracy of 91.6% in identifying various packing algorithms. Moreover, Dam et al [28] proposed an association rule mining method for multiclass packer detection based on YARA rules. Despite achieving high accuracy for malicious programs, the study did not address unknown packer detection.…”

Section: Static Analysismentioning

confidence: 99%

Identifying Malware Packers through Multilayer Feature Engineering in Static Analysis

Alkhateeb,

Ghorbani,

Habibi Lashkari

2024

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This research addresses a critical need in the ongoing battle against malware, particularly in the form of obfuscated malware, which presents a formidable challenge in the realm of cybersecurity. Developing effective antivirus (AV) solutions capable of combating packed malware remains a crucial endeavor. Packed malicious programs employ encryption and advanced techniques to obfuscate their payloads, rendering them elusive to AV scanners and security analysts. The introduced research presents an innovative malware packer classifier specifically designed to adeptly identify packer families and detect unknown packers in real-world scenarios. To fortify packer identification performance, we have curated a meticulously crafted dataset comprising precisely packed samples, enabling comprehensive training and validation. Our approach employs a sophisticated feature engineering methodology, encompassing multiple layers of analysis to extract salient features used as input to the classifier. The proposed packer identifier demonstrates remarkable accuracy in distinguishing between known and unknown packers, while also ensuring operational efficiency. The results reveal an impressive accuracy rate of 99.60% in identifying known packers and 91% accuracy in detecting unknown packers. This novel research not only significantly advances the field of malware detection but also equips both cybersecurity practitioners and AV engines with a robust tool to effectively counter the persistent threat of packed malware.

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Section: Static Analysismentioning

confidence: 99%

Identifying Malware Packers through Multilayer Feature Engineering in Static Analysis

Alkhateeb,

Ghorbani,

Habibi Lashkari

2024

Information

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“…An association rule can be defined as a truth table that results from the combination of two or more features [33]. Association rules are a set of "if-then" statements designed to provide probability of relationships among data features [34], in large data sets of various types of databases. Association rule mining makes a variety of use cases and is widely used to help discover sales correlations in transactional data sets.…”

Section: Association Rulesmentioning

confidence: 99%

Machine learning based recommender system for e-commerce

Loukili

Messaoudi

Ghazi

2023

IJ-AI

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<span>Nowadays, e-commerce is becoming an essential part of business for many reasons, including the simplicity, availability, richness and diversity of products and services, flexibility of payment methods and the convenience of shopping remotely without losing time. These benefits have greatly optimized the lives of users, especially with the technological development of mobile devices and the availability of the Internet anytime and anywhere. Because of their direct impact on the revenue of e-commerce companies, recommender systems are considered a must in this field. Recommender systems detect items that match the customer's needs based on the customer's previous actions and make them appear in an interesting way. Such a customized experience helps to increase customer engagement and purchase rates as the suggested items are tailored to the customer's interests. Therefore, perfecting recommendation systems that allow for more personalized and accurate item recommendations is a major challenge in the e-marketing world. In our study, we succeeded in developing an algorithm to suggest personal recommendations to customers using association rules via the Frequent-Pattern-Growth algorithm. Our technique generated good results with a high average probability of purchasing the next product suggested by the recommendation system.</span>

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“…In recent years, specifc constraints-based sequential pattern mining has been paid much attention. Sequential association rule mining [17,18] looks up association rules in transactional data. It does not consider the sequence of items but focuses on the fact that there is an intersection between the front and back itemsets.…”

Section: Related Workmentioning

confidence: 99%

“…For s � L to U do (7) num � r(s, n 1 ) (8) p � P B (X)//by Lemma 3 (9) total+ � num (10) P.add (<X, p, and num>) (11) End for (12) End for (13) End for (14) For each FSSP candidate X do (15) px � P B (X) (16) For each item Pitem in P do (17) If Pitem.p < px then (18) number+ � Pitem.num (19) End If (20)…”

Section: Complexity Analysismentioning

confidence: 99%

Discovering Significant Sequential Patterns in Data Stream by an Efficient Two-Phase Procedure

Tang

Wang

Liu

et al. 2022

Mathematical Problems in Engineering

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One essential topic of mining sequential patterns in the data stream is to optimize the time-space computations. However, more importantly, it should pay more attention to the significance of mining results as a large portion of them just response to the user-defined constraints purely by accident and they may have no statistical significance. In this paper, we propose FSSPDS, an efficient two-phase algorithm to discover the significant sequential patterns (SSPs) in the data stream with typical sliding windows, which has never been considered in existing problems. First, for generating SSPs candidates with high-quality, FSSPDS takes testable support and pattern length constraints into account and insignificant patterns were removed timely by a pattern-growth method. In the second phase, appropriate permutation testing is used to test the significance of the SSPs candidates. Exact permutation p values are obtained in a novel combination way based on unconditional Barnard’s test statistic which better reflects the process of data generations and collections. Experimental evaluations show that FSSPDS allows the discovery of SSPs in the data stream and rivals the state-of-the-art approaches efficiently under the control of family-wise error rate (FWER), especially for time efficiency, which was approximately an order of magnitude higher.

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Packer classification based on association rule mining

Cited by 10 publications

References 31 publications

Identifying Malware Packers through Multilayer Feature Engineering in Static Analysis

Identifying Malware Packers through Multilayer Feature Engineering in Static Analysis

Machine learning based recommender system for e-commerce

Discovering Significant Sequential Patterns in Data Stream by an Efficient Two-Phase Procedure

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