Network Security using Linux Intrusion Detection System

Anitha*, A. Arul

doi:10.7815/ijorcs.21.2011.012

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Attacks are discovered by keeping track of data from source and destination IP addresses, ports, protocol specifics, header specifics, etc. [9]. Attacks can be divided into two categories: passive and active, depending on their nature.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Machine Learning Model for Efficient Botnet Attack Detection in IoT Environment

Ali,

Shahroz,

Mushtaq

et al. 2024

IEEE Access

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Cyber attacks are growing with the rapid development and wide use of internet technology. Botnet attack emerged as one of the most harmful attacks. Botnet identification is becoming challenging due to the numerous attack vectors and the ongoing evolution of viruses. As the Internet of Things (IoT) technology is developing rapidly, many network devices have been subject to botnet attacks leading to substantial losses in different sectors. Botnets pose serious risks to network security and deep learning models have shown potential for efficiently identifying botnet activity from network traffic data. In this research, a botnet identification system is proposed based on the stacking of artificial neural network (ANN), convolutional neural network (CNN), long short-term memory (LSTM), and recurrent neural network (RNN) (ACLR). The experiments are conducted by employing both the individual models, as well as, the proposed ACLR model for performance comparison. The UNSW-NB15 dataset is used for botnet attacks and contains nine different attack types including 'Normal', 'Generic', 'Exploits', 'Fuzzers', 'DoS', 'Reconnaissance', 'Analysis', 'Backdoor', 'Shell code' and 'Worms'. Experimental results indicate the proposed ACLR model gains 0.9698 testing accuracy showing that it is successful in capturing the intricate patterns and characteristics of botnet attacks. The proposed ACLR model's k values (3, 5, 7, and 10) for a K-fold cross-validation accuracy score is 0.9749 indicating that the model's robustness and generalizability are demonstrated by k = 5. In addition, the proposed model detects botnets with a high receiver operating characteristic area under the curve (ROC-AUC) of 0.9934 and a precision-recall area under the curve (PR-AUC) of 0.9950. Performance comparison with existing state-of-the-art models further corroborates the superior performance of the proposed approach. The results of this research can be helpful against evolving threats and enhance cyber security procedures.

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Section: Related Workmentioning

confidence: 99%

Hybrid Machine Learning Model for Efficient Botnet Attack Detection in IoT Environment

Ali,

Shahroz,

Mushtaq

et al. 2024

IEEE Access

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“…alerting the administrators to take necessary actions to prevent the destructive impacts of the attacks [4].…”

mentioning

confidence: 99%

ANNIDS: Artificial Neural Network based Intrusion Detection System for Internet of Things

Anitha*¹,

Arockiam²

2019

IJITEE

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 Abstract: Internet of Things (IoT) makes everything in the real world to get connected. The resource constrained characteristics and the different types of technology and protocols tend to the IoT be more vulnerable than the conventional networks. Intrusion Detection System (IDS) is a tool which monitors analyzes and detects the abnormalities in the network activities. Machine Learning techniques are implemented with the Intrusion detection systems to enhance the performance of IDS. Various studies on IoT reveals that Artificial Neural Network (ANN) provides better accuracy and detection rate than other approaches. In this paper, an Artificial Neural Network based IDS (ANNIDS) technique based on Multilayer Perceptron (MLP) is proposed to detect the attacks initiated by the Destination Oriented Direct Acyclic Graph Information Solicitation (DIS) attack and Version attack in IoT environment. Contiki O.S/Cooja Simulator 3.0 is used for the IoT simulation.

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Network Security using Linux Intrusion Detection System

Abstract: Abstract

Cited by 2 publications

References 8 publications

Hybrid Machine Learning Model for Efficient Botnet Attack Detection in IoT Environment

Hybrid Machine Learning Model for Efficient Botnet Attack Detection in IoT Environment

ANNIDS: Artificial Neural Network based Intrusion Detection System for Internet of Things

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