Detecting Mobile Spam Botnets Using Artificial immune Systems

Vural, Ickin; Venter, Hein S.

doi:10.1007/978-3-642-24212-0_14

Cited by 8 publications

(4 citation statements)

References 5 publications

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“…I. Vural et al [8] proposed a technique based on artificial immune system to detect botnet spamming programs on Android phones. The main idea of the observed research is the ability of artificial immune systems to train only the positive examples.…”

Section: Immunity-based Methodsmentioning

confidence: 99%

The Methods of Artificial Intelligence for Malicious Applications Detection in Android Os

Bezobrazov¹,

Sachenko²,

Komar³

et al. 2016

IJC

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This paper presents and discusses a method for Android's applications classification with the purpose of malware detection. Based on the application of an Artificial Immune System and Artificial Neural Networks we propose the "antivirus" system especially for Android system that can detect and block undesirable and malicious applications. This system can be characterized by self-adaption and self-evolution and can detect even unknown and previously unseen malicious applications. The proposed system is the part of our team's big project named "Intelligent Cyber Defense System" that includes malware detection and classification module, intrusions detection and classification module, cloud security module and personal cryptography module. This paper contains the extended research that was presented during the IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS'2015) [1].

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Section: Immunity-based Methodsmentioning

confidence: 99%

The Methods of Artificial Intelligence for Malicious Applications Detection in Android Os

Bezobrazov¹,

Sachenko²,

Komar³

et al. 2016

IJC

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“…A study in [153] presents the proof of concept for an Android botnet, which is able to turn smartphones that are running Android OS into SMS spamming zombies. The research in the area of mobile botnets and their detection is scant and only some of the researchers have explored it yet [136][137][138][139]. In addition, future botnets can be highly strong and robust against the existing defensive solutions developed for Email spam as they will become potentially massive and more harmful with multiple small botnets being joined together into one large "super-botnet" [119] [120].…”

Section: Future Trends and Challenges In Detecting Email Spamming Bmentioning

confidence: 99%

A Comprehensive Study of Email Spam Botnet Detection

Khan

Muhaya

et al. 2015

IEEE Commun. Surv. Tutorials

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The problem of Email spam has grown significantly over the past few years. It is not just a nuisance for users but also it is damaging for those who fall for scams and other attacks. This is due to the complexity intensification of Email spamming techniques which are advancing from traditional spamming (direct spamming) techniques to a more scalable, elusive and indirect approach of botnets for distributing Email spam messages. In this paper we first discuss the sources and architectures used by the spamming botnets for sending massive amount of email spam. Then we present detailed chronicles of spamming botnets which systematically describes the timeline of events and notable occurrences in the advancement of these spamming botnets. This paper also aims to represent a comprehensive analysis of particular Email spamming botnet detection techniques proposed in the literature. We attempt to categorize them according to both their nature of defense and method of detection, also revealing and comparing their advantages and disadvantages extensively. We also present a qualitative analysis of these techniques. Finally we summarize the future trends and challenges in detecting email spamming botnets.

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“…Mobile botnet detection has been studied by Vural et al [31,32]. In [31], the authors propose a detection technique based on network forensics and give a list of fuzzy metrics for building SMS (short message service) behavior profiles to use in detection.…”

Section: Special Mobile Botnet Detection Techniquesmentioning

confidence: 99%

“…In [31], the authors propose a detection technique based on network forensics and give a list of fuzzy metrics for building SMS (short message service) behavior profiles to use in detection. This approach was improved by introducing detection based on artificial immune system principles in [32]. Another system, which can perform dynamic behavioral analysis of Android malware automatically, is Copper-Droid [18].…”

Section: Special Mobile Botnet Detection Techniquesmentioning

confidence: 99%

MBotCS: A Mobile Botnet Detection System Based on Machine Learning

Meng

Spanoudakis

2016

Lecture Notes in Computer Science

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract. As the use of mobile devices spreads dramatically, hackers have started making use of mobile botnets to steal user information or perform other malicious attacks. To address this problem, in this paper we propose a mobile botnet detection system, called MBotCS. MBotCS can detect mobile device traffic indicative of the presence of a mobile botnet based on prior training using machine learning techniques. Our approach has been evaluated using real mobile device traffic captured from Android mobile devices, running normal apps and mobile botnets. In the evaluation, we investigated the use of 5 machine learning classifier algorithms and a group of machine learning box algorithms with different validation schemes. We have also evaluated the effect of our approach with respect to its effect on the overall performance and battery consumption of mobile devices. Permanent repository link

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Detecting Mobile Spam Botnets Using Artificial immune Systems

Cited by 8 publications

References 5 publications

The Methods of Artificial Intelligence for Malicious Applications Detection in Android Os

The Methods of Artificial Intelligence for Malicious Applications Detection in Android Os

A Comprehensive Study of Email Spam Botnet Detection

MBotCS: A Mobile Botnet Detection System Based on Machine Learning

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