Intrusion Detection Using Incremental Learning from Streaming Imbalanced Data

Bhosale, Dipali; Ade, Roshani

doi:10.5121/ijmpict.2015.6102

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…Currently, the procedure suitable for intrusion detection that is similar to real life application conditions is incremental learning because it can learn from large-scale dynamic stream data and build up a knowledge base over time to benefit future learning and decision-making processes [29]. While, intrusion on incoming and outgoing network traffic is essential to have statistical calculation of constant timely changes to make a decision at a certain moment ensuring if it is a detected intrusion [30]. The study [31] presented Weight ILDA (WILDA) to function with online hand-written Chinese character recognition.…”

Section: Relate Studiesmentioning

confidence: 99%

A New Incremental Decision Tree Learning for Cyber Security based on ILDA and Mahalanobis Distance

Jaiyen¹,

Sornsuwit²

2019

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A cyber-attack detection is currently essential for computer network protection. The fundamentals of protection are to detect cyber-attack effectively with the ability to combat it in various ways and with constant data learning such as internet traffic. With these functions, each cyber-attack can be memorized and protected effectively any time. This research will present procedures for a cyber-attack detection system Incremental Decision Tree Learning (IDTL) that use the principle through Incremental Linear Discriminant Analysis (ILDA) together with Mahalanobis distance for classification of the hierarchical tree by reducing data features that enhance classification of a variety of malicious data. The proposed model can learn a new incoming datum without involving the previous learned data and discard this datum after being learned. The results of the experiments revealed that the proposed method can improve classification accuracy as compare with other methods. They showed the highest accuracy when compared to other methods. If comparing with the effectiveness of each class, it was found that the proposed method can classify both intrusion datasets and other datasets efficiently.

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Section: Relate Studiesmentioning

confidence: 99%

A New Incremental Decision Tree Learning for Cyber Security based on ILDA and Mahalanobis Distance

Jaiyen¹,

Sornsuwit²

2019

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“…Therefore, Incremental learning approaches [10], [11] have gained significant attention in the field of intrusion detection due to their ability to learn and adapt continuously without the need for a complete and ready-to-go dataset [12]. These approaches allow models to be trained and launched with limited initial data and can continue to learn and update as new data becomes available.…”

mentioning

confidence: 99%

An Incremental Majority Voting Approach for Intrusion Detection System Based on Machine Learning

Abdulboriy,

Shin

2024

IEEE Access

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With the rapid growth of digitalization and the increasing volume of data, the cybersecurity threat landscape is expanding at an alarming rate. Intrusion Detection Systems (IDS) have been widely employed in conjunction with firewalls to safeguard networks. However, traditional IDS systems operate in a static manner, rendering them vulnerable to obsolescence and necessitating costly retraining efforts. As a result, the demand for dynamic models capable of handling continuous streams of network traffic has surged as they can learn from the incoming traffic without the need to old data and costly retraining. In response to this challenge, we have implemented an enhanced approach: an incremental majority voting IDS system, which utilizes existing tools and techniques to improve the robustness and adaptability of intrusion detection By leveraging the collective decision-making power of multiple machine learning models such as: KNN classifier, Softmax Regressor and Adaptive Random Forest classifier, our system aims to improve the accuracy, especially reducing false alarm rates, and effectiveness of intrusion detection in real-time scenarios. Through this research, we have managed to obtain a model which scores 96.43% of accuracy as well as giving 100% precision for majority type of attacks. By successfully handling the imbalanced nature of streaming data, our adopted model shows promising potential as a high-performing solution for IDS and can be considered one of the robust IDS models capable of dealing with real-world imbalanced datasets.

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