Using Renyi Cross Entropy to Analyze Traffic Matrix and Detect DDoS Attacks

Yan, Ruoyu; Zheng, Qinghua

doi:10.3923/itj.2009.1180.1188

Cited by 14 publications

(9 citation statements)

References 2 publications

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“…Omni-directional sensing networks are typically used to achieve simple data acquisition, transmission and processing. Most study on deployment of omni-directional sensing model concentrated on the random deployment of target coverage [9], regional coverage deployment [10][11] and deterministic deployment [12]. For area deterministic deployment, it usually adopts normalized covering algorithm.…”

Section: Introductionmentioning

confidence: 99%

Deterministic Deployment for Wireless Image Sensor Nodes

Zhang¹,

Lei²,

Lin³

et al. 2014

TOEEJ

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Wireless sensor networks composed of camera enabled source nodes can provide visual information of an area of interest, potentially enriching monitoring applications. The node deployment is one of the key issues in the application of wireless sensor networks. In this paper, we take the effective coverage and connectivity as the evaluation indices to analyze the effect of the perceivable angle and the ratio of communication radius and sensing radius for the deterministic circular deployment. Experimental results demonstrate that the effective coverage area of the triangle deployment is the largest when using the same number of nodes. When the nodes are deployed in the same monitoring area in the premise of ensuring connectivity, rhombus deployment is optimal when √2 < rc / rs < √3 . The research results of this paper provide an important reference for the deployment of the image sensor networks with the given parameters.

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Section: Introductionmentioning

confidence: 99%

Deterministic Deployment for Wireless Image Sensor Nodes

Zhang¹,

Lei²,

Lin³

et al. 2014

TOEEJ

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show abstract

“…Yan et al use a traffic matrix to represent network state, and use Renyi cross entropy to analyze matrix traffic and detect anomalies rather than Shannon entropy. The results show Renyi cross entropy based method can detect DDoS attacks at the beginning with higher detection rate and lower false rate than Shannon entropy based method [11]. Gu et al proposed an approach to detect anomalies in the network traffic using Maximum Entropy estimation and relative entropy [12].…”

Section: Introductionmentioning

confidence: 99%

“…, each V(t) is unitized to ( ) V t using(11) and(12); Secondly, the Shannon entropy can be calculated using(15). Its unitized form is( , ) V t n .…”

mentioning

confidence: 99%

A New Attack Detection in Large Scale Network based on Entropy

Qia¹,

Wang²

2012

JNW

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Intrusion Detection System (IDS) typically generates a huge number of alerts with high false rate, especially in the large scale network, which result in a huge challenge on the efficiency and accuracy of the network attack detection. In this paper, an entropy-based method is proposed to analyze the numerous IDS alerts and detect real network attacks. We use Shannon entropy to examine the distribution of the source IP address, destination IP address, source threat and destination threat and datagram length of IDS alerts; employ Renyi cross entropy to fuse the Shannon entropy vector to detect network attack. In the experiment, we deploy the Snort to monitor part of Xi’an Jiaotong University (XJTU) campus network including 32 C-class network (more than 4000 users), and gather more than 40,000 alerts per hour on average. The entropy-based method is employed to analyze those alerts and detect network attacks. The experiment result shows that our method can detect 96% attacks with very low false alert rate.

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“…For each combination of η base and n, the training data is analyzed in the following method. Firstly, each V(t) is unitized toV(t) using (11) and (12); Secondly, the Shannon entropy can be calculated using (14). Its unitized form isV(t, n).…”

Section: Entropy-based Attack Detectionmentioning

confidence: 99%

“…Renyi cross entropy based method can detect DDoS attacks at the beginning with higher detection rate and lower false rate than Shannon entropy based method [11]. Gu et al proposed an approach to detect anomalies in the network traffic using Maximum Entropy estimation and relative entropy [12].…”

Section: Introductionmentioning

confidence: 99%

An Entropy-based Method for Attack Detection in Large Scale Network

Liu

Wang

et al. 2014

INT J COMPUT COMMUN

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Abstract:Intrusion Detection System (IDS) typically generates a huge number of alerts with high false rate, especially in the large scale network, which result in a huge challenge on the efficiency and accuracy of the network attack detection. In this paper, an entropy-based method is proposed to analyze the numerous IDS alerts and detect real network attacks. We use Shannon entropy to examine the distribution of the source IP address, destination IP address, source threat and destination threat and datagram length of IDS alerts; employ Renyi cross entropy to fuse the Shannon entropy vector to detect network attack. In the experiment, we deploy the Snort to monitor part of Xi'an Jiaotong University (XJTU) campus network including 32 C-class network (more than 4000 users), and gather more than 40,000 alerts per hour on average. The entropy-based method is employed to analyze those alerts and detect network attacks. The experiment result shows that our method can detect 96% attacks with very low false alert rate.

show abstract

Using Renyi Cross Entropy to Analyze Traffic Matrix and Detect DDoS Attacks

Cited by 14 publications

References 2 publications

Deterministic Deployment for Wireless Image Sensor Nodes

Deterministic Deployment for Wireless Image Sensor Nodes

A New Attack Detection in Large Scale Network based on Entropy

An Entropy-based Method for Attack Detection in Large Scale Network

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