Bio-inspired computational paradigm for feature investigation and malware detection: interactive analytics

Firdaus, Ahmad; Anuar, Nor Badrul; Razak, Mohd Faizal Ab; Sangaiah, Arun Kumar

doi:10.1007/s11042-017-4586-0

Cited by 36 publications

(20 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the number of features actually matters in building a good and reliable predictive model. Besides that, an extra number of features may contribute in overfitting and increase the complexity of the predictive model [ 52 ]. This study performed feature selection method by doing a features importance ranking using XGBoost.…”

Section: Methodsmentioning

confidence: 99%

Detecting opinion spams through supervised boosting approach

et al. 2018

Self Cite

View full text Add to dashboard Cite

Product reviews are the individual’s opinions, judgement or belief about a certain product or service provided by certain companies. Such reviews serve as guides for these companies to plan and monitor their business ventures in terms of increasing productivity or enhancing their product/service qualities. Product reviews can also increase business profits by convincing future customers about the products which they have interest in. In the mobile application marketplace such as Google Playstore, reviews and star ratings are used as indicators of the application quality. However, among all these reviews, hereby also known as opinions, spams also exist, to disrupt the online business balance. Previous studies used the time series and neural network approach (which require a lot of computational power) to detect these opinion spams. However, the detection performance can be restricted in terms of accuracy because the approach focusses on basic, discrete and document level features only thereby, projecting little statistical relationships. Aiming to improve the detection of opinion spams in mobile application marketplace, this study proposes using statistical based features that are modelled through the supervised boosting approach such as the Extreme Gradient Boost (XGBoost) and the Generalized Boosted Regression Model (GBM) to evaluate two multilingual datasets (i.e. English and Malay language). From the evaluation done, it was found that the XGBoost is most suitable for detecting opinion spams in the English dataset while the GBM Gaussian is most suitable for the Malay dataset. The comparative analysis also indicates that the implementation of the proposed statistical based features had achieved a detection accuracy rate of 87.43 per cent on the English dataset and 86.13 per cent on the Malay dataset.

show abstract

Section: Methodsmentioning

confidence: 99%

Detecting opinion spams through supervised boosting approach

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…References of the articles with the use of the respective datasets Malgenome Anonymous (0000d) Yerima, Sezer & McWilliams (2014), Firdaus et al (2017), Firdaus & Anuar (2015), Firdaus et al (2018) Drebin Anonymous (0000c) Firdaus et al (2017); Firdaus et al (2018); Firdaus et al (2018) Android malware dataset (AMD) Badhani & Muttoo (2019) Contagio MilaParkour (2019) Feldman, Stadther & Wang (2014; Islamic & Minna (2015) Androzoo Université du Luxembourg ( 2018) Razak et al (2019); Firdaus et al (2017); Razak et al (2018); Firdaus et al (2017) Figure 4 Reverse engineer tools for static analysis. This is the example of reverse engineer tools that have been used by the previous researchers to extract the code for malware.…”

Section: Datasetmentioning

confidence: 99%

Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation)

Jusoh

Firdaus

Anwar³

et al. 2021

PeerJ Computer Science

Self Cite

View full text Add to dashboard Cite

Android is a free open-source operating system (OS), which allows an in-depth understanding of its architecture. Therefore, many manufacturers are utilizing this OS to produce mobile devices (smartphones, smartwatch, and smart glasses) in different brands, including Google Pixel, Motorola, Samsung, and Sony. Notably, the employment of OS leads to a rapid increase in the number of Android users. However, unethical authors tend to develop malware in the devices for wealth, fame, or private purposes. Although practitioners conduct intrusion detection analyses, such as static analysis, there is an inadequate number of review articles discussing the research efforts on this type of analysis. Therefore, this study discusses the articles published from 2009 until 2019 and analyses the steps in the static analysis (reverse engineer, features, and classification) with taxonomy. Following that, the research issue in static analysis is also highlighted. Overall, this study serves as the guidance for novice security practitioners and expert researchers in the proposal of novel research to detect malware through static analysis.

show abstract

“…A number of works such as [9][10][11][12] have used bio-inspired concepts to provide cybersecurity solutions. They show the significance of such concepts as the basis for developing new self-resilient algorithm.…”

Section: Related Workmentioning

confidence: 99%

Mobile Malware Classification based on Phylogenetics

Saudi,

Sukardi,

Syafiq

et al. 2019

IJEAT

View full text Add to dashboard Cite

Security researchers and practitioners face many challenges in mitigating mobile malware attacks against smartphones. Ranges of techniques have been developed by different developers to ensure that smartphones remain free from such attacks. However, we still lack efficient techniques to mitigate mobile malware attacks, especially for the iOS platform. Hence, this paper presents mobile malware classifications based on phylogenetics that can be used for mobile malware detection with regard to the iOS platform. Phylogenetics have been used as the basis concept associated with forming a mobile malware classification based on similar malware behavior, vulnerability exploitation and mobile phone surveillance features that originate from the same family of specific malware practices. A mobile malware classification based on the phylogenetic concept and on mathematical formulations has been developed for this purpose, and proof of the concept has been sought to support this new classification. This research was conducted in a controlled lab environment using open source tools and by applying dynamic analysis. Consequently, this paper can be used as reference for other researchers with the same interest in future.

show abstract

Bio-inspired computational paradigm for feature investigation and malware detection: interactive analytics

Cited by 36 publications

References 47 publications

Detecting opinion spams through supervised boosting approach

Detecting opinion spams through supervised boosting approach

Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation)

Mobile Malware Classification based on Phylogenetics

Contact Info

Product

Resources

About