A_Survey_of_Botnet_Detection_Techniques.pdf

A_Survey_of_Botnet_Detection_Techniques.pdf - IJSRD...

This preview shows page 1 - 2 out of 5 pages.

IJSRD - International Journal for Scientific Research & Development| Vol. 1, Issue 9, 2013 | ISSN (online): 2321-0613 All rights reserved by 1 908 A Survey of Botnet Detection Techniques Parmar Riya H. 1 Harshita Kanani 2 1 P. G. Student 2 Assistant Professor 1, 2 Department of Computer Engineering 1, 2 LDRP-ITR, Kadi Sarvavidhyalaya, Gandhinagar, India Abstract Botnets are emerging as the most serious threat against cyber-security as they provide a distributed platform for several illegal activities such as launching distributed denial of service attacks against critical targets, malware dissemination, phishing, and click fraud. The defining characteristic of botnets is the use of command and control channels through which they can be updated and directed. Recently, botnet detection has been an interesting research topic related to cyber-threat and cyber-crime prevention. This paper is a survey of botnet and botnet detection. The survey clarifies botnet phenomenon and discusses botnet detection techniques. This survey classifies botnet detection techniques into four classes: signature-based, anomaly- based, DNS-based, and mining-base. Key words: Botnet; Botnet Detection; Cyber-security I. I NTRODUCTION According to explanation in [1, 2], malicious botnet is a network of compromised computers called Bots under the remote control of a human operator called Botm aster”. The term Bo t” is derived from the word Robot ; and similar to robots, bots are designed to perform some predefined functions in automated way. In other words, the individual bots are software programs that run on a host computer allowing the Botmaster to control host actions remotely [1, 2]. Botnets pose a significant and growing threat against cyber-security as they provide a distributed platform for many cyber-crimes such as Distributed Denial of Service (DDoS) attacks against critical targets, malware dissemination, phishing, and click fraud[3,4]. Botnet detection has been a major research topic in recent years. Researchers have proposed several approaches for botnet detection to combat botnet threat against cyber-security. In this survey, botnet phenomenon will be clarified and advances in botnet detection techniques will be discussed. This survey classifies botnet detection approaches into four classes: signature-based, anomaly-based, DNS- based, and mining-based. Furthermore, it summarizes botnet detection techniques in each class and provides a brief comparison of these techniques. The remainder of the paper is organized as follows: Section II describes botnet phenomenon. In this section, botnet characteristics and botnet life-cycle are explained to provide better understanding of botnet technology. Section III discusses botnet detection and tracking. In this section four classes of botnet detection approaches including signature-based, anomaly-based, DNS- based, and mining-based are discussed respectively. The survey concludes in Section IV.
Image of page 1

Subscribe to view the full document.

Image of page 2
  • Summer '17
  • SIR AZHAR
  • Data Mining, Denial-of-service attack, Host-based intrusion detection system, Intrusion detection system, Botnet

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern