Machine Learning based Intrusion Detection Framework using Recursive Feature Elimination Method

S, Jenif D Souza W; Parvathavarthini, B.; Arunmozhi, S.

doi:10.1109/icscan49426.2020.9262282

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…The RFE process identified several key features highly relevant to network intrusion detection. These features align with our expectations and prior research [18], [20], and [25], confirming the importance of specific traffic characteristics in detecting malicious activities. The combination of IF, OC-SVM and ANOVA F-test not only improved the model's performance but also reduced the complexity of the model by eliminating redundant and irrelevant features.…”

Section: E Discussion Of Resultssupporting

confidence: 90%

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An Empirical Internet Protocol Network Intrusion Detection using Isolation Forest and One-Class Support Vector Machines

Fuhnwi,

Adedoyin,

Agbaje

2023

IJACSA

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With the increasing reliance on web-based applications and services, network intrusion detection has become a critical aspect of maintaining the security and integrity of computer networks. This study empirically investigates internet protocol network intrusion detection using two machine learning techniques: Isolation Forest (IF) and One-Class Support Vector Machines (OC-SVM), combined with ANOVA F-test feature selection. This paper presents an empirical study comparing the effectiveness of two machine learning algorithms, Isolation Forest (IF) and One-Class Support Vector Machines (OC-SVM), with ANOVA F-test feature selection in detecting network intrusions using web services. The study used the NSL-KDD dataset, encompassing hypertext transfer protocol (HTTP), simple mail transfer protocol (SMTP), and file transfer protocol (FTP) web services attacks and normal traffic patterns, to comprehensively evaluate the algorithms. The performance of the algorithms is evaluated based on several metrics, such as the F1-score, detection rate (recall), precision, false alarm rate (FAR), and Area Under the Receiver Operating Characteristic (AUCROC) curve. Additionally, the study investigates the impact of different hyperparameters on the performance of both algorithms. Our empirical results demonstrate that while both IF and OC-SVM exhibit high efficacy in detecting network intrusion attacks using web services of type HTTP, SMTP, and FTP, the One-Class Support Vector Machines outperform the Isolation Forest in terms of F1-score (SMTP), detection rate(HTTP, SMTP, and FTP), AUCROC, and a consistent low false alarm rate (HTTP). We used the t-test to determine that OCSVM statistically outperforms IF on DR and FAR.

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Section: E Discussion Of Resultssupporting

confidence: 90%

“…Empirical results show that the proposed model achieved a low false alarm rate and a high recall. Similarly, [18], [19], [20], and [21] applied machine learning techniques for network intrusion detection systems.…”

Section: Related Workmentioning

confidence: 99%

An Empirical Internet Protocol Network Intrusion Detection using Isolation Forest and One-Class Support Vector Machines

Fuhnwi,

Adedoyin,

Agbaje

2023

IJACSA

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“…To explain the logic behind RFE, every column in the dataset needs to be iterated at least one time. The feature that is proved to be the least important is deleted after each round of RFE, and the procedure should then be recurred until the features remaining are equal to the boundary case that was established by RFE before [9,10]. After gaining a fundamental understanding of RFE and contrasting it with RFC, the final ten features that are satisfied by machine learning models, along with Figure 1, are bulleted as follows: IPV4_SRC_ADDR,IPV4_DST_ADDR, L7_PROTO, IN_BYTES, OUT_BYTES, FLOW_DURATION_MILLISECONDS, DURATION_IN, MAX_IP_PKT_LEN, DST_TO_SRC_AVG_THROUGHPUT, LABEL…”

Section: Recursive Feature Elimination (Rfe)mentioning

confidence: 99%

Enhancing network security through machine learning: A study on intrusion detection system using supervised algorithms

Huang,

Li,

Zhang

2023

ACE

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The topic of Intrusion Detection System (IDS) has become a highly debated issue in cybersecurity, generating intense discussions among experts in the field. IDS can be broadly categorized into two types: signature-based and anomaly-based. Signature-based IDS employ a collection of known network attacks to identify the precise attack the network is experiencing, while anomaly-based IDS employ machine learning models to detect anomalies present in the network traffic that could indicate a potential attack. In this study, we concentrate on anomaly-based IDS, evaluating the effectiveness of three supervised learning algorithms - Decision Tree (DT), Naive Bayes (NB), and K-Nearest Neighbor (KNN) - to determine the most suitable algorithm for each dataset based on its source. We conducted tests to evaluate each algorithm's performance and choose the best one for each dataset. Our findings show that anomaly-based IDS is highly effective in enhancing network security, providing valuable insights for organizations looking to improve their security measures.

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Ensemble Based-Cross Project Defect Prediction

Jindal

Ahmad

Aditya

2021

Smart Innovation, Systems and Technologies

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Machine Learning based Intrusion Detection Framework using Recursive Feature Elimination Method

Cited by 5 publications

References 6 publications

An Empirical Internet Protocol Network Intrusion Detection using Isolation Forest and One-Class Support Vector Machines

An Empirical Internet Protocol Network Intrusion Detection using Isolation Forest and One-Class Support Vector Machines

Enhancing network security through machine learning: A study on intrusion detection system using supervised algorithms

Ensemble Based-Cross Project Defect Prediction

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