BotNet Detection: Enhancing Analysis by Using Data Mining Techniques

Alparslan, Erdem; Karahoca, Adem; Karahoca, Dilek

doi:10.5772/48804

Cited by 10 publications

(7 citation statements)

References 32 publications

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“…Packet flow provides full informat ion of flow data but in large file type. Anomaly based techniques are mostly based on network behavior ano malies such as high network latency, activit ies on unused ports [9]. Data mining technique can be applied for optimizat ion purpose.…”

Section: Botnet Detecti On Techni Quesmentioning

confidence: 99%

“…All Honeypots have a unique concept. They are computer systems that don't have any production value [9]. All Honeypots have a unique concept.…”

Section: Botnet Detecti On Techni Quesmentioning

confidence: 99%

“…2) Intrusion Detection System (IDS): It can be characterized main ly in two ways [9]. a) Signature Based Botnet Detection: Rule based intrusion detection systems like Snort are running by using known malware signatures.…”

Section: Botnet Detecti On Techni Quesmentioning

confidence: 99%

“…Once an attack is initiated by a group of computer nodes having different locations controlled by a malicious individual or controller, it may be very hard to trace back to the origin due to the complexity of the Internet [9]. Because of these reasons it has become very serious problem nowadays.…”

Section: Introductionmentioning

confidence: 99%

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Botnet and Detection Techniques: A Review

2015

IJAERD

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Abstract-Among the diverse forms of mal w are, Botnet is the most w idespread and serious threat which occurs commonly in today's cyberattacks. A botnet is a group of compromised computers whi ch are remotely controlled by attacker to launch various network attacks, such as DDoS attack, spam, click fraud, identity theft and information phishing. Botnet has become a popular and productive tool behind many cyberattacks. The defining characteristic of botnets i s the use of command and control channels through which they can be updated and di rected. Recently malicious botnets evol ve into HTTP botnets out of typi cal IRC botnets. Data mining algorithms allow us to automate d etecting characteristics from large amount of data, which the conventional heuri stics and signature based methods could not apply.

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Section: Botnet Detecti On Techni Quesmentioning

confidence: 99%

“…All Honeypots have a unique concept. They are computer systems that don't have any production value [9]. All Honeypots have a unique concept.…”

Section: Botnet Detecti On Techni Quesmentioning

confidence: 99%

Section: Botnet Detecti On Techni Quesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Botnet and Detection Techniques: A Review

2015

IJAERD

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“…Although the subject of botnet and botnet detection has gained a lot of attention and thus, there are several surveys on botnets [12][13][14] [20][21][22][23][24][25][26][27][28][29][30][31] in the literature but all of them cover the botnet problem in general, discussing different aspects like botnet phenomenon, botnet detection techniques and countermeasures. Also what was missing in these surveys is that all of these studies review only a few of all the research efforts intended only for detecting Email spamming botnets.…”

mentioning

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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Robust Review on Privacy and Security Issues in Present Day Attacks of Data Mining

Sudha

2021

Advances in Intelligent Systems and Computing

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BotNet Detection: Enhancing Analysis by Using Data Mining Techniques

Cited by 10 publications

References 32 publications

Botnet and Detection Techniques: A Review

Botnet and Detection Techniques: A Review

A Comprehensive Study of Email Spam Botnet Detection

Robust Review on Privacy and Security Issues in Present Day Attacks of Data Mining

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