Towards Privacy-Preserving Malware Detection Systems for Android

Cui, Helei; Zhout, Yajin; Li, Qi; Rent, Kui

doi:10.1109/padsw.2018.8644924

Cited by 10 publications

(10 citation statements)

References 10 publications

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“…Works in this category either process the encrypted network traffic using software-based cryptographic techniques, such as searchable encryption, or process the network traffic inside encrypted memory regions in order to ensure the preservation of privacy. With searchable encryption, tools are able to search directly on encrypted data without decrypting it, and thus, without leaking information in plaintext (e.g., for privacy preserving malware detection [56]). This is achieved by encrypting the rules to be searched against the already encrypted traffic (e.g., [41]).…”

Section: Techniquesmentioning

confidence: 99%

A Survey on Encrypted Network Traffic Analysis Applications, Techniques, and Countermeasures

2021

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The adoption of network traffic encryption is continually growing. Popular applications use encryption protocols to secure communications and protect the privacy of users. In addition, a large portion of malware is spread through the network traffic taking advantage of encryption protocols to hide its presence and activity. Entering into the era of completely encrypted communications over the Internet, we must rapidly start reviewing the state-of-the-art in the wide domain of network traffic analysis and inspection, to conclude if traditional traffic processing systems will be able to seamlessly adapt to the upcoming full adoption of network encryption. In this survey, we examine the literature that deals with network traffic analysis and inspection after the ascent of encryption in communication channels. We notice that the research community has already started proposing solutions on how to perform inspection even when the network traffic is encrypted and we demonstrate and review these works. In addition, we present the techniques and methods that these works use and their limitations. Finally, we examine the countermeasures that have been proposed in the literature in order to circumvent traffic analysis techniques that aim to harm user privacy.

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Section: Techniquesmentioning

confidence: 99%

A Survey on Encrypted Network Traffic Analysis Applications, Techniques, and Countermeasures

2021

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“…Multi-layer perceptron's were found to work best with 99.4 percent accuracy, their project was designed to test Android malware classification algorithms [5]. A security detection approach based on the Metropolis algorithm is proposed in their article on Android introduce a concept method named PPMDroid to conserve bandwidth and speed up the process with many optimizations [71]. Today, in all countries, the usage of cell phones is increasing and sadly, cyber criminals are constantly targeting mobile phones.…”

Section: Related Workmentioning

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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“…Multi-layer perceptron's were found to work best with 99.4 percent accuracy, their project was designed to test Android malware classification algorithms [60]. A security detection approach based on the Metropolis algorithm is proposed in their article on Android introduce a concept method named PPMDroid to conserve bandwidth and speed up the process with many optimizations [90]. Today, in all countries, the usage of cell phones is increasing and sadly, cyber criminals are constantly targeting mobile phones.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This study condenses the progression of malware detection techniques supported [90] First, the privacy problems of current static and dynamic malware detection methods are highlighted…”

Section: Metropolis Algorithmmentioning

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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Towards Privacy-Preserving Malware Detection Systems for Android

Cited by 10 publications

References 10 publications

A Survey on Encrypted Network Traffic Analysis Applications, Techniques, and Countermeasures

A Survey on Encrypted Network Traffic Analysis Applications, Techniques, and Countermeasures

A Comprehensive Study of Malware Detection in Android Operating Systems

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

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