A Two-Layered Malware Detection Model Based on Permission for Android

Lu, Tianliang; Hou, Su

doi:10.1109/ccet.2018.8542215

Cited by 17 publications

(11 citation statements)

References 6 publications

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“…Then the approach is used Rules for sensitive authorization, to analyse this detection mechanism and validate the efficacy of the system, a number of assessment approaches have been applied. Utilized to assess the method of detection and to validate the system's efficacy [83]. DAMBA, a novel prototype system based on C/S architecture, is presented in their article.…”

Section: Fig 2 Architecture Of Droid Deep [73]mentioning

confidence: 99%

A Comprehensive Study of Malware Detection in Android Operating Systems

Hamdi

Ibrahim

Omar

et al. 2021

AJRCoS

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Android is now the world's (or one of the world’s) most popular operating system. More and more malware assaults are taking place in Android applications. Many security detection techniques based on Android Apps are now available. The open environmental feature of the Android environment has given Android an extensive appeal in recent years. The growing number of mobile devices are incorporated in many aspects of our everyday lives. This paper gives a detailed comparison that summarizes and analyses various detection techniques. This work examines the current status of Android malware detection methods, with an emphasis on Machine Learning-based classifiers for detecting malicious software on Android devices. Android has a huge number of apps that may be downloaded and used for free. Consequently, Android phones are more susceptible to malware. As a result, additional research has been done in order to develop effective malware detection methods. To begin, several of the currently available Android malware detection approaches are carefully examined and classified based on their detection methodologies. This study examines a wide range of machine-learning-based methods to detecting Android malware covering both types dynamic and static.

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Section: Fig 2 Architecture Of Droid Deep [73]mentioning

confidence: 99%

A Comprehensive Study of Malware Detection in Android Operating Systems

Hamdi

Ibrahim

Omar

et al. 2021

AJRCoS

View full text Add to dashboard Cite

show abstract

“…They provided numerous optimization ideas for hybrid analysis to achieve improved efficiency and precision in their papers. The complicated computation work of the PC customer was finished to maintain the limited resources of the mobile customer [102].Cloak & Dagger, a specific kind of assaulting action, is detailed in length. Detection algorithm for harmful software packages, The Cloak and Dagger attack algorithm is presented for the detection of malicious software packages.…”

Section: Fig 2 Architecture Of Droid Deep [57]mentioning

confidence: 99%

“…In order to train the pre-processed sequences DexCNN can achieve accuracy of 93.4 percent, while the DexCRNN can reach accuracy of 95.8 per cent. [102] Utilized to assess the method of detection and to validate the system's efficacy.…”

Section: Dexcrnnmentioning

confidence: 99%

A State of Art Survey for Understanding Malware Detection Approaches in Android Operating System

Hamdi

Omar

Al-Zebari

et al. 2021

AJRCoS

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Mobile malware is malicious software that targets mobile phones or wireless-enabled Personal digital assistants (PDA), by causing the collapse of the system and loss or leakage of confidential information. As wireless phones and PDA networks have become more and more common and have grown in complexity, it has become increasingly difficult to ensure their safety and security against electronic attacks in the form of viruses or other malware. Android is now the world's most popular OS. More and more malware assaults are taking place in Android applications. Many security detection techniques based on Android Apps are now available. Android applications are developing rapidly across the mobile ecosystem, but Android malware is also emerging in an endless stream. Many researchers have studied the problem of Android malware detection and have put forward theories and methods from different perspectives. Existing research suggests that machine learning is an effective and promising way to detect Android malware. Notwithstanding, there exist reviews that have surveyed different issues related to Android malware detection based on machine learning. The open environmental feature of the Android environment has given Android an extensive appeal in recent years. The growing number of mobile devices, they are incorporated in many aspects of our everyday lives. In today’s digital world most of the anti-malware tools are signature based which is ineffective to detect advanced unknown malware viz. Android OS, which is the most prevalent operating system (OS), has enjoyed immense popularity for smart phones over the past few years. Seizing this opportunity, cybercrime will occur in the form of piracy and malware. Traditional detection does not suffice to combat newly created advanced malware. So, there is a need for smart malware detection systems to reduce malicious activities risk. The present paper includes a thorough comparison that summarizes and analyses the various detection techniques.

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“…The anomaly detection is the model of intrusion detection mode with negligible false positives in the industrial process. Lu and Hou [10] described the model of android malware detection in order to detect the android malware effectively. This model is based on the declared permission.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of Malware attacks in smart phones using Machine Learning

Niveditha¹,

Ananthan²

2019

IJITEE

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In recent years, security has become progressively vital in mobile devices. The biggest security problems in android devices are malware attack which has been exposed to different threats. The volume of new applications by the production of mobile devices and their related app-stores is too big to manually examine the each and every application for malicious behavior. Installing applications which may leads to security vulnerabilities on the smart phones request access to sensitive information. There are various malwares can attack android device namely virus, worms, Botnet, Trojans, Backdoor and Root kits due to these attacks the users is compromised by privacy. Root kits and viruses in mobile phone and IoT devices improve along with smart device versions are very difficult to detect or to the least costly. There are 3 places where the trace of these root kits / virus is visible namely CPU, Baseband and Memory. In the new approach we will use machine learning to detect “anomaly” usage pattern and a remote (master server) will analyze and verify the presence of such threats. This research work aims to develop a pipeline to investigate if any application present in a smart device is a malware or not. This pipeline uses HMM algorithm to read anomaly in application behavior, deep learning with Deep Belief Networks (DBN) to classify application events, and bootstrapping algorithm using random forest to categorize the application itself after malware or benign

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A Two-Layered Malware Detection Model Based on Permission for Android

Cited by 17 publications

References 6 publications

A Comprehensive Study of Malware Detection in Android Operating Systems

A Comprehensive Study of Malware Detection in Android Operating Systems

A State of Art Survey for Understanding Malware Detection Approaches in Android Operating System

Detection of Malware attacks in smart phones using Machine Learning

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