Systematic downloading: Analysis and detection

Bhandari, Akshay; Khare, Shreya; Murthy, Hema A.

doi:10.1109/icspcs.2014.7021098

Cited by 1 publication

(1 citation statement)

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“…It logs the details of all the requests from the client. User access pattern for the subscribed content, which is available in the logs, can be used to train models for detection of systematic downloading (Bhandari et al 2014). For this task, Normal and Systematic download traffic request are analysed as mentioned below.…”

Section: 3a Datasetmentioning

confidence: 99%

Multi-level anomaly detection: Relevance of big data analytics in networks

Sait

Bhandari

Khare

et al. 2015

Sadhana

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The Internet has become a vital source of information; internal and external attacks threaten the integrity of the LAN connected to the Internet. In this work, several techniques have been described for detection of such threats. We have focussed on anomaly-based intrusion detection in the campus environment at the network edge. A campus LAN consisting of more than 9000 users with a 90 Mbps internet access link is a large network. Therefore, efficient techniques are required to handle such big data and to model user behaviour. Proxy server logs of a campus LAN and edge router traces have been used for anomalies like abusive Internet access, systematic downloading (internal threats) and DDoS attacks (external threat); our techniques involve machine learning and time series analysis applied at different layers in TCP/IP stack. Accuracy of our techniques has been demonstrated through extensive experimentation on huge and varied datasets. All the techniques are applicable at the edge and can be integrated into a Network Intrusion Detection System.

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Section: 3a Datasetmentioning

confidence: 99%