Detection of SYN flooding attacks using generalized autoregressive conditional heteroskedasticity (GARCH) modeling technique

Ranjan, Nikhil; Murthy, Hema A.; Gonsalves, Timothy A.

doi:10.1109/ncc.2010.5430151

Cited by 9 publications

(7 citation statements)

References 12 publications

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“…Secondly, the exponential back-off and retransmission of SYN/ACK packets during a SYN attack happens in the order of seconds. It is this property of TCP which is being exploited for modeling the mechanism of a SYN attack (Divakaran et al 2006c;Ranjan et al 2010;Wang et al 2002a). For simplicity and to provide a representation of the traffic over 3 months, three data sets are created, each consisting of 5 days (i.e., 24 h × 5 days) of continuous traffic, one from each month.…”

Section: Ddos Attack Detection (Transport Layer)mentioning

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: Ddos Attack Detection (Transport Layer)mentioning

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 method used for estimation of the GARCH (1, 1) parameters is given in Pourahmadi (2001), Bollerslev (1986) and Zeng et al (2008). The predicted volatility (PV) (Ranjan et al, 2010) can be expressed as: The predicted volatility (PV) (Ranjan et al, 2010) can be expressed as:…”

Section: Garch Modelmentioning

confidence: 99%

“…The error between the actual RV (RV i ) and the PV (PV i ) is written as (Ranjan et al, 2010): The error between the actual RV (RV i ) and the PV (PV i ) is written as (Ranjan et al, 2010):…”

Section: Garch Modelmentioning

confidence: 99%

Energy efficient data gathering using prediction-based filtering in wireless sensor networks

Gupta

Misra

Garg

2013

IJICT

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In wireless sensor network, sensor readings generated by nearby nodes are redundant and highly correlated, both in space and time domains. Since transmitting redundant and highly correlated data incurs a huge waste of energy and bandwidth, spatial and temporal correlation should be exploited in order to reduce redundant data transmission. In this paper, we propose an energy efficient data gathering protocol that uses a prediction-based filtering (EEDGPF) mechanism to solve the problem of redundant data transmissions. Our data gathering protocol organises a WSN into clusters, using data similarity that exists in readings of sensor nodes and cluster heads and uses a GARCH (1, 1) model-based non-linear predictor to exploit the temporal correlation of sensor readings. Experimental results over real dataset show that our protocol significantly outperforms linear predictor (AR(3))-based protocol proposed in Jiang et al. (2011), in terms of number of data packets delivered, number of successful predictions and average energy consumption.

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“…With the traffic prediction, researchers in proposed some detection schemes, which obtain good effectiveness. Moreover, there are also some other related studies such as active probing technique , Client ACK , Web farm DDoS Attack attenuator , partial completion filters , and Stream Control Transmission Protocol against DoS attacks .…”

Section: Related Workmentioning

confidence: 99%

Detecting SYN flooding attacks based on traffic prediction

Wang

Sun

Zou

et al. 2012

Security Comm Networks

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SYN flooding attacks are a common type of distributed denial‐of‐service attacks. Up to now, many defense schemes have been proposed against SYN flooding attacks. Traditional defense schemes rely on passively sniffing an attacking signature and are inaccurate in the early stages of an attack. These schemes are effective only at the later stages when attacking signatures are obvious. In this paper, we propose a detection approach that makes use of SYN traffic prediction to determine whether SYN flooding attacks happen at the early stage. We firstly adopt grey prediction model to predict SYN traffic, and then, we employ cumulative sum algorithm to detect SYN flooding attack traffic among forecasted SYN traffic. Trace‐driven simulation results demonstrate that our proposed detection approach can detect SYN flooding attacks effectively. Copyright © 2012 John Wiley & Sons, Ltd.

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Detection of SYN flooding attacks using generalized autoregressive conditional heteroskedasticity (GARCH) modeling technique

Cited by 9 publications

References 12 publications

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

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

Energy efficient data gathering using prediction-based filtering in wireless sensor networks

Detecting SYN flooding attacks based on traffic prediction

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