The Automatic Discovery, Identification and Measurement of Botnets

Castle, I.; Buckley, Emilie

doi:10.1109/securware.2008.44

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…This is the initial phase where researcher may get the possibilities of any malicious activities specific to the botnet. Castle et al [45] showed a novel technique for the automatic Dacier et al [46] showed the attack attribution method. This method exhibits some real world result traces in low interaction honeypot.…”

Section: Botnet Forensic Identificationmentioning

confidence: 99%

Botnet Forensic: Issues, Challenges and Good Practices

Bijalwan

Solanki

Pilli

2018

NPA

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Unethical hacking of sites, probing, click frauds, phishing, denial of services attack and many such malicious practices affects the organizational integrity and sovereignty. Such activities are direct attacks on the safety, security and confidentiality of the organization. These activities put organizational privacy at stake. Botnet forensic is utilized to strengthen the security issues by understanding the modus operandi of the attacks. The available observations can be utilized in future also to prevent a potential threat to network security. This paper enlightens the novel summary of previous survey including life cycle, classification, framework, detection, analysis and the challenges for botnet forensics.

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Section: Botnet Forensic Identificationmentioning

confidence: 99%

Botnet Forensic: Issues, Challenges and Good Practices

Bijalwan

Solanki

Pilli

2018

NPA

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“…Castle and Buckley [17] presented a technique to detect spam botnets. At first, they extract the header information from an email message and enhance it with information from the envelope to generate a set of synthetic headers.…”

Section: Related Workmentioning

confidence: 99%

BotGrab: A negative reputation system for botnet detection

Yahyazadeh

Abadi

2015

Computers & Electrical Engineering

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“…Castle and Buckley [7] present a technique to detect botnets that are used for sending spam. They process the headers of email messages and generate a set of synthetic headers from them.…”

Section: Related Workmentioning

confidence: 99%

BotCatch: Botnet detection based on coordinated group activities of compromised hosts

Yahyazadeh

Abadi

2014

7'th International Symposium on Telecommunications (IST'2014)

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Botnets have become one of the major tools used by attackers to perform various malicious activities on the Internet, such as launching distributed denial of service attacks, sending spam, leaking personal information, and so on. In this paper, we present BotCatch, a behavior-based botnet detection system that considers multiple coordinated group activities in the monitored network to identify bot-infected hosts. To achieve this goal, it first identifies suspicious hosts participating in coordinated group activities by an online incremental clustering algorithm and then calculates a negative score for each of the hosts based on several fuzzy membership functions. It then makes an informed decision and identifies a host as bot-infected if its negative score is higher than a threshold. We demonstrate the effectiveness of BotCatch to detect various botnets including HTTP-, IRC-, and P2P-based botnets using a testbed network consisting of some bot-infected hosts. The experimental results show that BotCatch can successfully detect various botnets with a high detection rate while keeping false alarm rate significantly low.

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The Automatic Discovery, Identification and Measurement of Botnets

Cited by 5 publications

References 4 publications

Botnet Forensic: Issues, Challenges and Good Practices

Botnet Forensic: Issues, Challenges and Good Practices

BotGrab: A negative reputation system for botnet detection

BotCatch: Botnet detection based on coordinated group activities of compromised hosts

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