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Mobile Botnet Attacks - an Emerging Threat: Classification, Review and Open Issues
( Ahmad Karim ),( Syed Adeel Ali Shah ),( Rosli Bin Salleh ),( Muhammad Arif ),( Rafidah Md Noor ),( Shahaboddin Shamshirband ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.4
The rapid development of smartphone technologies have resulted in the evolution of mobile botnets. The implications of botnets have inspired attention from the academia and the industry alike, which includes vendors, investors, hackers, and researcher community. Above all, the capability of botnets is uncovered through a wide range of malicious activities, such as distributed denial of service (DDoS), theft of business information, remote access, online or click fraud, phishing, malware distribution, spam emails, and building mobile devices for the illegitimate exchange of information and materials. In this study, we investigate mobile botnet attacks by exploring attack vectors and subsequently present a well-defined thematic taxonomy. By identifying the significant parameters from the taxonomy, we compared the effects of existing mobile botnets on commercial platforms as well as open source mobile operating system platforms. The parameters for review include mobile botnet architecture, platform, target audience, vulnerabilities or loopholes, operational impact, and detection approaches. In relation to our findings, research challenges are then presented in this domain.
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최병하(Byungha Choi),조경산(Kyungsan Cho) 한국컴퓨터정보학회 2014 韓國컴퓨터情報學會論文誌 Vol.19 No.4
스마트폰 같은 모바일 장치의 대중적 보급과 발전으로 인해 PC 기반의 악성코드가 모바일 기반으로 빠르게 이동하고 있다. 특히 봇넷은 PC에서의 강력한 악성행위와 피해를 모바일 장치에서 재생산하며 새로운 기법을 추가하고 있다. 기존 PC 기반의 봇넷과 달리 모바일 봇넷은 동시에 다양한 공격 경로의 탐지가 어려워 네트워크 기반보다는 호스트 기반의 탐지 기법이 주를 이루고 있다. 본 논문에서는 호스트 기반 기법의 한계를 극복하기 위하여 네트워크 기반으로 모바일 봇넷을 탐지하는 HMM과 SVM을 적용한 2 가지 기법을 비교한다. 기계학습에 많이 사용되는 시계열 데이터와 단위시간 데이터를 추출하여 두 기법에 적용하여, 실제 봇넷이 설치된 환경의 트래픽 검증 분석을 통해 이들 데이터에 따른 두 기법의 탐지율과 탐지 특성을 제시한다. As mobile devices have become widely used and developed, PC based malwares can be moving towards mobile-based units. In particular, mobile Botnet reuses powerful malicious behavior of PC-based Botnet or add new malicious techniques. Different from existing PC-based Botnet detection schemes, mobile Botnet detection schemes are generally host-based. It is because mobile Botnet has various attack vectors and it is difficult to inspect all the attack vector at the same time. In this paper, to overcome limitations of host-based scheme, we compare two network-based schemes which detect mobile Botnet by applying HMM and SVM techniques. Through the verification analysis under real Botnet attacks, we present detection rates and detection properties of two schemes.
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Mobile Botnet Detection Model based on Retrospective Pattern Recognition
Meisam Eslahi,Moslem Yousefi,Maryam Var Naseri,Y.M.Yussof,N.M.Tahir,H. Hashim 보안공학연구지원센터 2016 International Journal of Security and Its Applicat Vol.10 No.9
The dynamic nature of Botnets along with their sophisticated characteristics makes them one of the biggest threats to cyber security. Recently, the HTTP protocol is widely used by Botmaster as they can easily hide their command and control traffic amongst the benign web traffic. This paper proposes a Neural Network based model to detect mobile HTTP Botnets with random intervals independent of the packet payload, commands content, and encryption complexity of Bot communications. The experimental test results that were conducted on existing datasets and real world Bot samples show that the proposed method is able to detect mobile HTTP Botnets with high accuracy.
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A Study and Model to protect Sophisticated Eurograbber attack
Md Nadeem Ahmed,Mohd Hussain 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.6
Smartphone now a days are very common to everyone and it is widely accepted. According to eMarketer [4] more than 2.56 billion people will use Smartphone by 2018 and up to 2015 more than one fourth of the world population is using Smartphone. It is widely because of its multiple functionality which allow us to accomplish more than what we expect. Apart from basic feature it is used for e banking, web browsing etc. Almost all restricted or confidential data is stored in Smartphone which leads to sophisticated attack like Eurograbber which happens through mobile botnet installation. This attack even fails the most successful two factor authentication which is worldwide accepted by banks. The Botnet differentiate itself from common other mobile virus because its infected system is capable to create a link with C&C (command and control centre) controlled by bot master. In this paper we will discuss about Eurograbber attack , the problem scenario and the model to prevent this sophisticated attack.
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