An advanced method for detection of botnet traffic using intrusion detection system

Koli, Manoj S.; Chavan, Manik K.

doi:10.1109/icicct.2017.7975246

Cited by 11 publications

(2 citation statements)

References 9 publications

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“…According to the behavior of some effective detection methods using a machine-learning algorithm in this article. The main concern of this article is to build a model using the Bayes category ML algorithm to predict and prevent various types of intrusion attacks that create a breakdown of wireless sensor network applications [4][5][6][7][8]. This is deployed by monitoring various parameters of the wireless sensor Network and the output based on their weights Volume 14, Number 2, 2023 and concentrations and energy consumed at various nodes.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection Model using Naive Bayes ML Algorithm for WSN Intrusion Detection System

Jeevaraj

Karthik

Vijayan

et al. 2023

Int. j. electr. comput. eng. syst. (Online)

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Intrusion detection models using machine-learning algorithms are used for Intrusion prediction and prevention purposes. Wireless sensor network has a possibility of being attacked by various kinds of threats that will de-promote the performance of any network. These WSN are also affected by the sensor networks that send wrong information because of some environmental causes in- built disturbances misaligned management of the sensors in creating intrusion to the wireless sensor networks. Even though signified routing protocols cannot assure the required security in wireless sensor networks. The idea system provides a key solution for this kind of problem that arises in the network and predicts the abnormal behavior of the sensor nodes as well. But built model by the proposed system various approaches in detecting these kinds of intrusions in any wireless sensor networks in the past few years. The proposed system methodology gives a phenomenon control over the wireless sensor network in detecting the inclusions in its early stages itself. The Data set pre-processing is done by a method of applying the minimum number of features for intrusion detection systems using a machine learning algorithm. The main scope of this article is to improve the prediction of intrusion in a wireless sensor network using AI- based algorithms. This also includes the finest feature selection methodologies to increase the performance of the built model using the selected classifier, which is the Bayes category algorithm. Performance accuracy in the prediction of different attacks in wireless sensor networks is attained at nearly 95.8% for six selected attributes, a Precision level of 0.958, and the receiver operating characteristics or the area under the curve is equal to 0.989.

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Section: Introductionmentioning

confidence: 99%

Feature Selection Model using Naive Bayes ML Algorithm for WSN Intrusion Detection System

Jeevaraj

Karthik

Vijayan

et al. 2023

Int. j. electr. comput. eng. syst. (Online)

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“…An IoT botnet is a group of hacked computers, smart appliances, and Internetconnected devices that have been co-opted for illicit purposes. The most effective countermeasure against such types of botnet is provided by the Intrusion detection system (IDS) [3].…”

Section: Introductionmentioning

confidence: 99%

IoT-Botnet Detection using Long Short-Term Memory Recurrent Neural Network

Costa¹,

Dessai²,

Gaonkar³

et al. 2020

IJERT

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IoT (Internet of Things) provides unique identifiers and the facility to convey data across a network without requiring human-to-computer or human-to-human interaction. It is one of the most fast-evolving technologies nowadays. The positive influence of the IoT toward governments, citizens, and businesses is being significant. IoT comes with significant security concerns that need to be addressed. One of the serious security threats in network security is IoT-Bot. In past years many techniques are being practiced to detect IoT-Bot in a network. This paper explains the detection of IoT-botnet using a deep learning-based LSTM RNN (Long Short-Term Memory Recurrent Neural Network) model. The accuracy of this model is then compared with the SVM (Support Vector Machine), LR (Linear Regression), and KNN (K-Nearest Neighbors) model. UNSW-NB15 dataset is used for training and testing the model. The dataset contains 9 types of attack categories. The accuracy of this proposed model is very high. This can further be extended to work in real-time botnet detention. Wireshark can be used to collect real-time network traffic and detect IoT-Bot in a network.

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A Survey on Different Network Intrusion Detection Systems and CounterMeasure

Rajput

Thakkar

2019

Advances in Intelligent Systems and Computing

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An advanced method for detection of botnet traffic using intrusion detection system

Cited by 11 publications

References 9 publications

Feature Selection Model using Naive Bayes ML Algorithm for WSN Intrusion Detection System

Feature Selection Model using Naive Bayes ML Algorithm for WSN Intrusion Detection System

IoT-Botnet Detection using Long Short-Term Memory Recurrent Neural Network

A Survey on Different Network Intrusion Detection Systems and CounterMeasure

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