Computing Adaptive Feature Weights with PSO to Improve Android Malware Detection

Xu, Yanwu; Wu, Chunhua; Zheng, Kan; Wang, Xu; Niu, Xinxin; Lu, Tianliang

doi:10.1155/2017/3284080

Cited by 17 publications

(12 citation statements)

References 35 publications

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“…Several ensemble learning-based detection solutions, such as those of Rhode, Burnap [28], Homayoun, Dehghantanha [40], were proposed to detect the ransomware early. The idea is that by combining decisions of several weak classifiers, the ensemble can make more accuracy detection [28,45,[92][93][94][95]. This is attributed to the complementary nature of the ensemble's base classifiers, which allows the introspection of the versatility of the dataset that contributes to increase the detection accuracy [45][46][47][48].…”

Section: Detection Techniquesmentioning

confidence: 99%

A Survey of Crypto Ransomware Attack Detection Methodologies: An Evolving Outlook

Alqahtani

Sheldon

2022

Sensors

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Recently, ransomware attacks have been among the major threats that target a wide range of Internet and mobile users throughout the world, especially critical cyber physical systems. Due to its unique characteristics, ransomware has attracted the attention of security professionals and researchers toward achieving safer and higher assurance systems that can effectively detect and prevent such attacks. The state-of-the-art crypto ransomware early detection models rely on specific data acquired during the runtime of an attack’s lifecycle. However, the evasive mechanisms that these attacks employ to avoid detection often nullify the solutions that are currently in place. More effort is needed to keep up with an attacks’ momentum to take the current security defenses to the next level. This survey is devoted to exploring and analyzing the state-of-the-art in ransomware attack detection toward facilitating the research community that endeavors to disrupt this very critical and escalating ransomware problem. The focus is on crypto ransomware as the most prevalent, destructive, and challenging variation. The approaches and open issues pertaining to ransomware detection modeling are reviewed to establish recommendations for future research directions and scope.

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Section: Detection Techniquesmentioning

confidence: 99%

A Survey of Crypto Ransomware Attack Detection Methodologies: An Evolving Outlook

Alqahtani

Sheldon

2022

Sensors

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“…If the expressions and constraints of an application are in this rule library, this application will be considered as a malicious application. For example, to reduce the high false- [15], [16], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [94], [95], [98], [99], [100], [101], [102], [105], [109], [111],…”

Section: ) Publication Sourcementioning

confidence: 99%

A Systematic Literature Review of Android Malware Detection Using Static Analysis

et al. 2020

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Android malware has been in an increasing trend in recent years due to the pervasiveness of Android operating system. Android malware is installed and run on the smartphones without explicitly prompting the users or without the user's permission, and it poses great threats to users such as the leakage of personal information and advanced fraud. To address these threats, various techniques are proposed by researchers and practitioners. Static analysis is one of these techniques, which is widely applied to Android malware detection and can detect malware quickly and prohibit malware before installation. To provide a clarified overview of the latest work in Android malware detection using static analysis, we perform a systematic literature review by identifying 98 studies from January 2014 to March 2020. Based on the features of applications, we first divide static analysis in Android malware detection into four categories, which include Android characteristic-based method, opcode-based method, program graph-based method, and symbolic execution-based method. Then we assess the malware detection capability of static analysis, and we compare the performance of different models in Android malware detection by analyzing the results of empirical evidence. Finally, it is concluded that static analysis is effective to detect Android malware. Moreover, there is a preliminary result that neural network model outperforms the non-neural network model in Android malware detection. However, static analysis still faces many challenges. Thus, it is necessary to derive some novel techniques for improving Android malware detection based on the current research community. Moreover, it is essential to establish a unified platform that is used to evaluate the performance of a series of techniques in Android malware detection fairly. INDEX TERMS Android malware detection, static analysis, systematic literature review.

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“…For event risk value, calculate information leakage risk coefficient according to weight. Reference [9][10][11][12] pointed out that machine learning has been widely applied in the fields of healthcare, cybersecurity, etc. due to its powerful data mining capabilities, where SVM is one of the most popular machine learning algorithms; therefore use SVM algorithm to divide information leakage risk coefficient and get a final evaluation.…”

Section: Principles Of Intelligent Detection Systemmentioning

confidence: 99%

An Intelligent Detection Method of Personal Privacy Disclosure for Social Networks

Kang

Xiao

Yin

2021

Security and Communication Networks

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With the increase of the number of users in the current social network platform (taking WeChat as an example), personal privacy security issues are important. This paper proposes an intelligent detection method for personal privacy disclosure in social networks. Firstly, we propose and construct the eigenvalue in social platform. Secondly, by calculating the value of user account assets, we can obtain the eigenvalue to calculate the possibility of threat occurrence and the impact of threat. Thirdly, we analyse the situation that the user may leak the privacy information and make a score. Finally, SVM algorithm is used to classify the results, and some suggestions for warning and modification are put forward. Experiments show that this intelligent detection method can effectively analyse the privacy leakage of individual users.

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Computing Adaptive Feature Weights with PSO to Improve Android Malware Detection

Cited by 17 publications

References 35 publications

A Survey of Crypto Ransomware Attack Detection Methodologies: An Evolving Outlook

A Survey of Crypto Ransomware Attack Detection Methodologies: An Evolving Outlook

A Systematic Literature Review of Android Malware Detection Using Static Analysis

An Intelligent Detection Method of Personal Privacy Disclosure for Social Networks

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