A statistical and distributed packet filter against DDoS attacks in Cloud environment

Pandey, Vikash C; Peddoju, Sateesh K.; Deshpande, Prachi

doi:10.1007/s12046-018-0800-7

Cited by 23 publications

(5 citation statements)

References 16 publications

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“…16 The author introduced the statistical method of detecting the malicious packets of DDoS attacks. 17 Bayesian theorem checks the conditional legitimate probability of the data packet. 18 The author proposed a related metric such as the correlation coefficient to determine the attack flow and discriminate the suspicious flow.…”

Section: Literature Surveymentioning

confidence: 99%

“…There are N numbers of techniques present fire DDoS attack, like Gregorian pointed out the SYN, SYN‐ACK packets change the TCP handshakes' flaws and determine the SYN attacks 16 . The author introduced the statistical method of detecting the malicious packets of DDoS attacks 17 . Bayesian theorem checks the conditional legitimate probability of the data packet 18 …”

Section: Literature Surveymentioning

confidence: 99%

“…Though few rules lead to some vulnerability and feasibility, SDN makes the network protected and increases the network area 16 . In Reference 17, the proposed algorithm is implemented in the NSL‐KDD intrusion detection system. The results explain the proposed distributed intrusion detection scheme and perform well with the IDS system along with cloud computing.…”

Section: Literature Surveymentioning

confidence: 99%

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A novel sensitive DDoS attacks against statistical test in network traffic fusion

Kulandaivel,

Kumar,

Sathiyamoorthi

et al. 2023

Trans Emerging Tel Tech

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Distributed denial of service (DDoS) attacks are common attacks that limit casual users from cracking critical network resources. A method to find the DDoS attack comes to light as an essential and hot research issue. Therefore, the increment in network usage scale demands more challenges to see the DDoS attack. Non‐real‐time collection of network traffic will make sincere complications inaccuracy; DDoS attack detection, and efficiency. Lately, sketch data structures play an excellent role in high‐speed networks to suppress and combine network traffic issues. However, the sketch data structure has issues rebuilding the reverse keys that behave abnormally due to the reversible hash function. To rectify the above difficulties, we include the Chinese Remainder Theorem‐based Reverse Sketching (CRTRS). However, Reverse Sketching is not good at suppressing and combining heavy network traffic, but it can easily detect abnormal keys. According to the network traffic records setup by CRTRS, we add the Modified Cumulative Sum Quality Control Scheme (MCSQCS) that encourages DDoS attack detection. The enforcement of the proposed work with DDoS attack detection has two kind of different data sets. The result of the experiments shows that it gives more accuracy and efficiency in detecting DDoS attacks. Therefore, comparing other DDoS detecting methods with the sketch data structure has fewer computational complications. Improving the anomalous keys is the most critical challenge in this proposed work. To illustrate the accuracy rate, we experiment with a new technique called CIC‐Friday Heavy network traffic fragment. We also explain the experimental results of the MAWI heavy network fragment with the various parameter setups. To compare our proposed method with other similar existing methods, we use a sketch data structure to manage the network traffic data. It also uses the MSPCA method to detect the DDoS attack. The information‐based analysis helps to see the DDoS attack. The result analysis of True Positive Value (TPV) and False Positive Value (FPV) with MSPCA techniques has 79% and .1% accuracy. Similarly, information‐based detection has an accuracy of about 90% and 5%. Hence, the proposed method gives more accuracy for the detection of DDoS attacks.

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Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

See 1 more Smart Citation

A novel sensitive DDoS attacks against statistical test in network traffic fusion

Kulandaivel,

Kumar,

Sathiyamoorthi

et al. 2023

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…It can be written as [1][2][3]5,7,10], [6,[8][9]. Similarly, a − c can be translated into [1][2][3]5,7,10], [10,12]. 4.…”

Section: Map Policies To Rulesmentioning

confidence: 99%

“…Existing packet filtering techniques [8][9][10][11][12] were initially used in the network to provide security protection. The access control function of the network packet is implemented on the router, and the security function of it is separated.…”

Section: Introductionmentioning

confidence: 99%

FMNISCF: Fine-Grained Multi-Domain Network Interconnection Security Control Framework

et al. 2020

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The integrated air-ground multi-domain network provides users with a set of shared infrastructures. Security policies can be defined flexibly in the context of multi-domain network semantics. The packet filter module in the security gateway can run efficiently, which is an urgent requirement in this network environment. The framework combined with multi-domain network semantics implements the transformation into rules. It replaces the traditional manual method of configuring rules. The framework supports the whole life cycle management of rules from generation state and distribution state to execution state. In the aspect of security, the map security and semantic security are analyzed and optimized, respectively. Finally, through a series of experiments, compared with iptables/DPDK-IPFW/BSD-IPFW/BSD-pfsense, the high efficiency of the scheme is verified.

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A Network-Based Intrusion Detection System

Deshpande

Sharma

Peddoju

2019

Studies in Big Data

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A statistical and distributed packet filter against DDoS attacks in Cloud environment

Cited by 23 publications

References 16 publications

A novel sensitive DDoS attacks against statistical test in network traffic fusion

A novel sensitive DDoS attacks against statistical test in network traffic fusion

FMNISCF: Fine-Grained Multi-Domain Network Interconnection Security Control Framework

A Network-Based Intrusion Detection System

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