Detection of DDoS Attack using Optimized Hop Count Filtering Technique

Devi, G. Usha; Kumar, Priyan Malarvizhi; Balan, E. Vishnu; Nath, Chandrika; Chandrasekhar, M.

doi:10.17485/ijst/2015/v8i26/83981

Cited by 18 publications

(2 citation statements)

References 5 publications

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“…Peng et al 20 , Sindhu et al 21 , and Rai et al 22 have applied a decision tree based intrusion detection system to detect the attacks. Gwon et al 23 and Devi 24 proposed the LSTM model to detect the network attacks. Dhaliwal et al 25 , Pattawaro et al 26 , and Jiang 27 , deployed XGBoost for dimensionality reduction to reduce the irrelevant features for intrusion detection.…”

Section: Related Workmentioning

confidence: 99%

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Retracted: An empirical intrusion detection system based on XGBoost and bidirectional long‐short term model for 5G and other telecommunication technologies

Karthikraja¹,

Senthilkumar²,

Rajadurai

et al. 2022

Computational Intelligence

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Recently, intrusion detection system (IDS) has become an essential remedy to protect networks from malicious activities and attacks. The attacks force the network activities under risk and cause network data as an unrecoverable one. The attacks have different features in nature and their detection rate is low. An efficient attack detection system requires a huge volume of network transactions and relevant features for training. But the existing datasets have significant and nonsignificant features together. Hence, the significant feature selection plays a major role to detect the attacks. For this reason, this paper adopts the XGBoost approach to identify the relevant (significant) features of the attacks in the dataset. After this XGBoost, this article applies the bidirectional long short term model (Bi-LSTM) for the detection and classification of the attacks. This Bi-LSTM model is a novel approach to the IDS and very effective to increase the detection rate. Thus, this article focuses on feature reduction and classification of the attacks.The experimental results show that the proposed 1216

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Section: Related Workmentioning

confidence: 99%

“…The input layer for the classification consists of one timestep and the number features are set to 24. Thus input shape becomes (1,24). The hidden layer contains 20 memory units and the output layer is fully connected.…”

Section: Evaluation With Bi-lstmmentioning

confidence: 99%

Retracted: An empirical intrusion detection system based on XGBoost and bidirectional long‐short term model for 5G and other telecommunication technologies

Karthikraja¹,

Senthilkumar²,

Rajadurai

et al. 2022

Computational Intelligence

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Modified secure AODV protocol to prevent wormhole attack in MANET

Narayanan

Murugaboopathi

2018

Concurrency and Computation

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SummaryAn Adhoc network is a combination of mobile nodes that works without centralized infrastructure. Every mobile node acts as host and routers. Furthermore, it also forward packets to other mobile nodes in network which are not within the direct transmission range. Mobile Ad hoc networks are easily exposed to various network layer attacks that include black hole, wormhole, and DOS attack. Wormhole attack is one among the severe attacks in MANET. Wormhole attacker receives the packets at any particular location in the network and disturbs the flow of packet by tunneling them to another location. In this paper, advanced mechanism is presented against these wormhole attacks in a MANET. Existing methods use Quality of Service (QoS) for entire network to detect attacks. Our method makes use of the packet delivery ratio and round trip time for each node, and it also detects active and passive attacks. Thus, the complete identification of wormhole attack is possible using the proposed method.

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