PAIRED: An Explainable Lightweight Android Malware Detection System

Alani, Mohammed M.; Awad, Ali Ismail

doi:10.1109/access.2022.3189645

Cited by 31 publications

(10 citation statements)

References 25 publications

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“…During runtime, the Dalvik VM accesses register operands using Dalvik instructions, encompassing null operation (nop), data operation (move), return, data definition (const), method call (invoke), data conversion, and data operation instructions. This paper's approach relies on representing the relative frequency of Dalvik instructions and employs a feature selection algorithm [1].…”

Section: Proposed Methodologymentioning

confidence: 99%

Computer Malicious Code Signal Detection based on Big Data Technology

Liu

2023

SCPE

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The article addresses the challenges modelled by the inadequacy of traditional detection methods in effectively handling the substantial volume of software behavior samples, particularly in big data. A novel approach is proposed for leveraging big data technology to detect malicious computer code signals. Additionally, it seeks to attack the issues associated with machine learning-based mobile malware detection, namely the presence of a large number of features, low accuracy in detection, and imbalanced data distribution. To resolve these challenges, this paper presents a multifaceted methodology. First, it introduces a feature selection technique based on mean and variance analysis to eliminate irrelevant features hindering classification accuracy. Next, a comprehensive classification method is implemented, utilizing various feature extraction techniques such as principal component analysis (PCA), Kaehunen-Loeve transform (KLT), and independent component analysis (ICA). These techniques collectively contribute to enhancing the Precision of the detection process. Recognizing the issue of unbalanced data distribution among software samples, the study proposes a multi-level classification integration model grounded in decision trees. In response, the research focuses on enhancing accuracy and mitigating the impact of data imbalance through a combination of feature selection, extraction techniques, and a multi-level classification model. The empirical results highlight the effectiveness of the proposed methodologies, showcasing notable accuracy improvements ranging from 3.36% to 6.41% across different detection methods on the Android platform. The introduced malware detection technology, grounded in source code analysis, demonstrates a promising capacity to identify Android malware effectively.

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Section: Proposed Methodologymentioning

confidence: 99%

Computer Malicious Code Signal Detection based on Big Data Technology

Liu

2023

SCPE

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“…(3) L and the biases b (2) and b (3) for the two layers ,and a normalization constant Z. So we have the prior p H…”

Section: End If End Whilementioning

confidence: 99%

“…In fact Android recorded over 2.5 billion active devices on monthly basis in the year 2019 alone. In fact,Android has become quite popular not only as an operating system for smartphones but also for cars, tablets, smart appliances, and IoT gadgets [2]. Android has made millions of apps easily accessible and downloadable in an effort to promote further growth and keep current customers.…”

Section: Introductionmentioning

confidence: 99%

Android applications classification with deep neural networks

et al. 2023

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Article Title our neural network's classification performance against that of four traditional deep feed-forward neural networks and seven baseline models based on machine learning algorithms on the same data. According to experimental results, a deep belief neural network-based model could accurately categorize Android apps into benign and malicious categories with 98.7% of the time. Compared to all previous deep learning and machine learning methods, this represents a significant improvement. Also,the categorization accuracy of the DBN model is better than that of numerous other models examined by earlier researchers.

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“…An explainable fast, and accurate approach for detecting Android malware called PAIRED was illustrated by Mohammed et al in [161]. The proposed detection system achieved lightweight by reducing the number of features by a factor of 84% and deploying classifiers that are not resourceintensive.…”

Section: ) Malwarementioning

confidence: 99%

Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

et al. 2022

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This survey presents a comprehensive review of current literature on Explainable Artificial Intelligence (XAI) methods for cyber security applications. Due to the rapid development of Internetconnected systems and Artificial Intelligence in recent years, Artificial Intelligence including Machine Learning (ML) and Deep Learning (DL) has been widely utilized in the fields of cyber security including intrusion detection, malware detection, and spam filtering. However, although Artificial Intelligence-based approaches for the detection and defense of cyber attacks and threats are more advanced and efficient compared to the conventional signature-based and rule-based cyber security strategies, most ML-based techniques and DL-based techniques are deployed in the ''black-box'' manner, meaning that security experts and customers are unable to explain how such procedures reach particular conclusions. The deficiencies of transparencies and interpretability of existing Artificial Intelligence techniques would decrease human users' confidence in the models utilized for the defense against cyber attacks, especially in current situations where cyber attacks become increasingly diverse and complicated. Therefore, it is essential to apply XAI in the establishment of cyber security models to create more explainable models while maintaining high accuracy and allowing human users to comprehend, trust, and manage the next generation of cyber defense mechanisms. Although there are papers reviewing Artificial Intelligence applications in cyber security areas and the vast literature on applying XAI in many fields including healthcare, financial services, and criminal justice, the surprising fact is that there are currently no survey research articles that concentrate on XAI applications in cyber security. Therefore, the motivation behind the survey is to bridge the research gap by presenting a detailed and up-to-date survey of XAI approaches applicable to issues in the cyber security field. Our work is the first to propose a clear roadmap for navigating the XAI literature in the context of applications in cyber security.

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PAIRED: An Explainable Lightweight Android Malware Detection System

Cited by 31 publications

References 25 publications

Computer Malicious Code Signal Detection based on Big Data Technology

Computer Malicious Code Signal Detection based on Big Data Technology

Android applications classification with deep neural networks

Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

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