A Study of Intrusion Detection System using Machine Learning Classification Algorithm based on different feature selection approach

Illavarason, P; Sundaram, Baskaran

doi:10.1109/i-smac47947.2019.9032499

Cited by 17 publications

(7 citation statements)

References 11 publications

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“…This section recalls the works [7][8][9][10][11][12][13][14][15][16][17][18][19][20] from which inspiration was taken for the application of ML models and to make a comparison between the state-of-the-art results and the results achieved by the techniques we presented in the next sections.…”

Section: Related Workmentioning

confidence: 99%

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Analysis, Design, and Comparison of Machine-Learning Techniques for Networking Intrusion Detection

Dini

Saponara

2021

Designs

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The use of machine-learning techniques is becoming more and more frequent in solving all those problems where it is difficult to rationally interpret the process of interest. Intrusion detection in networked systems is a problem in which, although it is not fundamental to interpret the measures that one is able to obtain from a process, it is important to obtain an answer from a classification algorithm if the network traffic is characterized by anomalies (and hence, there is a high probability of an intrusion) or not. Due to the increased adoption of SW-defined autonomous systems that are distributed and interconnected, the probability of a cyber attack is increased, as well as its consequence in terms of system reliability, availability, and even safety. In this work, we present the application of different machine-learning models to the problem of anomaly classification in the context of local area network (LAN) traffic analysis. In particular, we present the application of a K-nearest neighbors (KNN) and of an artificial neural network (ANN) to realize an algorithm for intrusion detection systems (IDS). The dataset used in this work is representative of the communication traffic in common LAN networks in military application in particular typical US Air Force LAN. This work presents a training phase of the different models based on a multidimensional-scaling preprocessing procedure, based on different metrics, to provide higher performance and generalization with respect to model prediction capability. The obtained results of KNN and ANN classifiers are compared with respect to a commonly used index of performance for classifiers evaluation.

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Section: Related Workmentioning

confidence: 99%

“…In [10], a very detailed investigation is reported for observing several issues on the intrusive performance by using the ML classification. Here, an ML-classification algorithm is used for detecting the several categories of attacks.…”

Section: Related Workmentioning

confidence: 99%

Analysis, Design, and Comparison of Machine-Learning Techniques for Networking Intrusion Detection

Dini

Saponara

2021

Designs

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“…Machine learning is used for autonomous real-time anomaly detection in this work. Usage of machine learning classifiers for intrusion and anomaly detection for network security is thoroughly researched in the past decade [27], [28]. The classifiers studied in this paper include:…”

Section: Machine Learning Techniques For Autonomous Anomaly Detectionmentioning

confidence: 99%

“…DT, KNN and GNB classifiers are preferred due to their suitability for intrusion and anomaly detection [28], [29]. RF and Stacking classifiers are also included in this paper because they perform relatively well on the IoTID20 data as presented in [26].…”

Section: Machine Learning Techniques For Autonomous Anomaly Detectionmentioning

confidence: 99%

SHAPEIoT: Secure Handshake Protocol for Autonomous IoT Device Discovery and Blacklisting using Physical Unclonable Functions and Machine Learning

Baykara¹,

Şafak²,

Kalkan³

2021

Advances in Machine Learning

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This paper proposes a new lightweight handshake protocol implemented on top of the Constrained Application Protocol (CoAP) that can be used in device discovery and ensuring the IoT network security by autonomously managing devices of any computational complexity using whitelisting and blacklisting. A Physical Unclonable Function (PUF) is utilized for the session key generation in the proposed handshake protocol. The CoAP server performs real-time device discovery using the proposed handshake protocol, and anomaly detection using machinelearning algorithms to ensure the security of the IoT network. To the best of our knowledge, the presented PUF-based handshake protocol is the first to performs blacklisting and whitelisting. Whitelisted IoT devices not displaying anomalous behavior can join and remain in the IoT network. IoT devices that display anomalous behavior are autonomously blacklisted by the CoAP server and are either disallowed from joining the IoT network or are removed from the IoT network. Simulation results show that amongst the five machine learning algorithms studied, the stacking classifier displays the highest overall anomaly detection accuracy of 99.98%. Based on the results of the network simulation performed, the CoAP server is capable of blacklisting malicious IoT devices within the network with perfect accuracy.

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“…A KY(Buck-boost)Converter To Analysing The Hybrid System Generating Higher Output [10][11].This Paper Presents An Advanced Consolation Of Hybrid System So That Demand For More Power Generation Could Be Fulfilled [12]. To Optimize The Energy utilization According the power availability and load profile For this applications the intelligent Controller Determines the utilization of Multisource Energy System [13]- [15].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Solar and Windmill Using Real Time Simulation Tool

Pushpavalli¹,

Abirami²,

Sivagami³

et al. 2021

Proceedings of the Fist International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICA

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This work is concerned with modelling and modification of a wind power production method into a solar-wind composite mode along with analysis and simulation of the proposed method. The suggested Wind turbine is modelled, and parameters such as rotor diameter, wind speed, pitch angle, etc are taken into count. The PV module is then modeled based on general parameters such as solar irradiation and temperature, etc. A PVsyst simulation is performed for the solar PV grid and stand alone system. Further homer grid software is used to perform simulation on the hybrid system and the results were observed. The hybrid system has an output 1.5kw or 1500w which consists of wind turbines (500kw) and PV solar system of 6 panels of 165w each producing the total peak output of 1kw or 1000w. Autocontollers and converters are used along with a battery of 155w for storage. Conclusions point that the load absorbs power from both solar and wind schemes. However, there is the fluctuation of power supply in some driving conditions, such as modification of solar radiation and wind velocity

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A Study of Intrusion Detection System using Machine Learning Classification Algorithm based on different feature selection approach

Cited by 17 publications

References 11 publications

Analysis, Design, and Comparison of Machine-Learning Techniques for Networking Intrusion Detection

Analysis, Design, and Comparison of Machine-Learning Techniques for Networking Intrusion Detection

SHAPEIoT: Secure Handshake Protocol for Autonomous IoT Device Discovery and Blacklisting using Physical Unclonable Functions and Machine Learning

Analysis of Solar and Windmill Using Real Time Simulation Tool

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