Cluster based Intrusion Detection System for Mobile Ad-hoc Network

Gopalakrishnan, S.; Rajesh, A.

doi:10.1109/iconstem.2019.8918871

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…Mobile Ad-hoc networks are distributed and made up of different individual devices that can communicate with one another. Attacks on the network can take many different forms due to its scattered nature and weak infrastructure [26,27].…”

Section: Literaturementioning

confidence: 99%

Distributed Denial-of-Service (DDoS) Attack Detection using 1D Convolution Neural Network (CNN) and Decision Tree Model

Bhargavi Goparaju,

Bandla Sreenivasa Rao

2023

ARASET

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The major problem of internet security is a Distributed Denial-of-Service (DDoS) attack, which can’t be detected easily. This attack is said to have occurred when lots of service requests are simultaneously received at a server on the internet. This makes the server too busy to provide normal services for others. The Distributed Denial of Service (DDoS) attacks nature on large networks on the Internet demanding to develop the effective detection and response methods. The deployment of these technique should perform not only at the network core but also at the edge. A DDoS attack detection framework is presented based on transfer learning model consisting of 1D Convolution Neural Network (CNN) and decision tree classifier. The 1D CNN model utilizes for features extraction from the input network traffic data. This operation also reduces the dimension of the data thereby removing the redundancy in the data. These features are given to the decision tree model for classification. The proposed framework identified the DDoS attacks with good accuracy. This system could identify attacks in real-time and provide network security.

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Section: Literaturementioning

confidence: 99%

Distributed Denial-of-Service (DDoS) Attack Detection using 1D Convolution Neural Network (CNN) and Decision Tree Model

Bhargavi Goparaju,

Bandla Sreenivasa Rao

2023

ARASET

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“…The detection in wireless ad hoc networks can be performed by each host node or by a cluster head. Some works, such as in [10][11][12], proposed cluster-based schemes and improved the performance; although, the burden is placed on the cluster head. Cluster-based schemes have issues such as the following:…”

Section: Introductionmentioning

confidence: 99%

Learning from Routing Information for Detecting Routing Misbehavior in Ad Hoc Networks

Basomingera

Choi

2020

Sensors

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Owing to ad hoc wireless networks’ properties, the implementation of complex security systems with higher computing resources seems troublesome in most situations. Therefore, the usage of anomaly or intrusion detection systems has attracted considerable attention. The detection systems are implemented either as host-based, run by each node; or as cluster/network-based, run by cluster head. These two implementations exhibit benefits and drawbacks, such as when cluster-based is used alone, it faces maintaining protection when nodes delay to elect or replace a cluster head. Despite different heuristic approaches that have been proposed, there is still room for improvement. This work proposes a detection system that can run either as host- or as cluster-based to detect routing misbehavior attacks. The detection runs on a dataset built using the proposed routing-information-sharing algorithms. The detection system learns from shared routing information and uses supervised learning, when previous network status or an exploratory network is available, to train the model, or it uses unsupervised learning. The testbed is extended to evaluate the effects of mobility and network size. The simulation results show promising performance even against limiting factors.

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