Relevance Features Selection for Intrusion Detection

Olusola, Adetunmbi Adebayo; Adeola, Oladele S.; Daramola, Oladunni

doi:10.1007/978-1-4614-0373-9_31

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…Olusola et al [21] proposed a new feature reduction technique for the KDD '99 dataset. The paper emphasized that selecting more relevant features leads to faster and more appropriate results for network attack detection.…”

Section: Literature Review Of Feature Selection Methods For Intrusion...mentioning

confidence: 99%

A Novel Feature Selection Approach to Classify Intrusion Attacks in Network Communications

Ozkan-Okay,

Samet,

Aslan

et al. 2023

Applied Sciences

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The fast development of communication technologies and computer systems brings several challenges from a security point of view. The increasing number of IoT devices as well as other computing devices make network communications more challenging. The number, sophistication, and severity of network-related attacks are growing rapidly. There are a variety of different attacks including remote-to-user (R2L), user-to-remote (U2R), denial of service (DoS), distributed DDoS, and probing. Firewalls, antivirus scanners, intrusion detection systems (IDSs), and intrusion prevention systems (IPSs) are widely used to prevent and stop cyber-related attacks. Especially, IDPSs are used to stop and prevent intrusions on communication networks. However, traditional IDSs are no longer effective in detecting complicated cyber attacks from normal network traffic. Because of this, new promising techniques, which specifically utilize data mining, machine learning, and deep learning, need to be proposed in order to distinguish intrusions from normal network traffic. To effectively recognize intrusions, the feature generation, feature selection, and learning processes must be performed delicately before the classification stage. In this study, a new feature selection method called FSAP (Feature Selection Approach) is proposed. In addition, a hybrid attack detection model called SABADT (Signature- and Anomaly-Based Attack Detection Technique) is suggested, which utilizes different classification metrics to recognize attacks. The proposed general method FSACM (Feature Selection and Attack Classification Method) is tested on KDD ’99, UNSW-NB15, and CIC-IDS2017 datasets. According to the experiment results, the proposed method outperformed the state-of-the-art methods in the literature in terms of detection, accuracy, and false-alarm rates.

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Section: Literature Review Of Feature Selection Methods For Intrusion...mentioning

confidence: 99%

A Novel Feature Selection Approach to Classify Intrusion Attacks in Network Communications

Ozkan-Okay,

Samet,

Aslan

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

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“…Adeola S.Oladele. And Daramola O.Abosede [9] presented the relevance of each feature in KDD '99 intrusion detection dataset to the detection of each class. Rough set degree of dependency and dependency ratio of each class were employed to determine the most discriminating features for each class.…”

Section: Related Workmentioning

confidence: 99%

Intrusion detection system by improved preprocessing methods and Naïve Bayes classifier using NSL-KDD 99 Dataset

Deshmukh¹,

Ghorpade²,

Padiya³

2014

2014 International Conference on Electronics and Communication Systems (ICECS)

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Today Network is one of the very important parts of life and a lot of essential activities are performed using network. Network security plays critical role in real life situations. This paper presents a Data Mining method in which various preprocessing methods are involved such as Normalization, Discretization and Feature selection. With the help of these methods the data is preprocessed and required features are selected. Here NaIve Bayes classifier is used in supervised learning method which classifies various network events for the KDD cup'99 Dataset. This dataset is the most commonly used dataset for Intrusion Detection.

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“…These were gathered in two parts: an offline evaluation using network traffic and audit logs collected on a simulation network, and a real-time evaluation through Air Force Research Laboratory (AFRL). • The KDD'99 dataset [24] remains the widely accepted standard for machine learning in networking, despite its limitations. Composed of Ethernet transmissions captured for identifying known malware activity, it was released for use in developing intrusion detection systems and has been used in network research, either in its original form or the improved NSL-KDD version.…”

Section: A Chronological Survey Of Network Traffic Generatorsmentioning

confidence: 99%

PAC-GPT: A Novel Approach to Generating Synthetic Network Traffic With GPT-3

Kholgh,

Kostakos

2023

IEEE Access

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The application of machine learning models, particularly in cybersecurity, has surged significantly in the past few years. However, the effectiveness of these models is predominantly tethered to the quality and breadth of the training data they ingest. The scarcity of realistic datasets within the cybersecurity field constitutes a considerable challenge to the development of industry-grade tools intended for real-world application scenarios. Specifically, current datasets are either significantly outdated or fall short on both qualitative and quantitative fronts, primarily because many organizations exhibit reluctance in data sharing, stemming from privacy concerns or the potential threat to trade secrets. To address this challenge, the paper introduces PAC-GPT, a novel framework to generate reliable synthetic data for machine learning methods based on Open AI's Generative Pre-trained Transformer 3 (GPT-3). The core components of this framework are two modules, namely a Flow Generator, which is responsible for capturing and regenerating patterns in a series of network packets, and Packet Generator, which can generate individual network packets given the network flow. We also propose a packet generator based on LLM chaining and then proceed to assess, compare, and evaluate its performance using metrics such as loss, accuracy and success rate, concluding that transformers are a suitable approach for synthetic packet generation with minimal fine-tuning performed. Lastly, a streamlined command line interface (CLI) tool has been devised to facilitate the seamless access of this innovative data generation strategy by professionals from various disciplines.

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Relevance Features Selection for Intrusion Detection

Cited by 5 publications

References 10 publications

A Novel Feature Selection Approach to Classify Intrusion Attacks in Network Communications

A Novel Feature Selection Approach to Classify Intrusion Attacks in Network Communications

Intrusion detection system by improved preprocessing methods and Naïve Bayes classifier using NSL-KDD 99 Dataset

PAC-GPT: A Novel Approach to Generating Synthetic Network Traffic With GPT-3

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Relevance Features Selection for Intrusion Detection

Cited by 5 publications

References 10 publications

A Novel Feature Selection Approach to Classify Intrusion Attacks in Network Communications

A Novel Feature Selection Approach to Classify Intrusion Attacks in Network Communications

Intrusion detection system by improved preprocessing methods and Na&#x00EF;ve Bayes classifier using NSL-KDD 99 Dataset

PAC-GPT: A Novel Approach to Generating Synthetic Network Traffic With GPT-3

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Product

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Intrusion detection system by improved preprocessing methods and Naïve Bayes classifier using NSL-KDD 99 Dataset