A Multiple Instance Learning Problem Approach Model to Anomaly Network Intrusion Detection

Weon, Ill-Young; Song, Doo Heon; Ko, Sung-Bum; Lee, Chang‐Hoon

doi:10.3745/jips.2005.1.1.014

Cited by 3 publications

(1 citation statement)

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“…It is useful to handle packets as continuous streams in order to identify different sorts of attacks and decrease repeated alarms on the same attack. In [84], a new method is put forward for handling groups of related packets, whereas existing anomaly detection based on machine learning treats a packet as a fundamental unit. The proposed technique is in accordance with a BAG unit and a BAG creation algorithm that organizes packets.…”

Section: Intrusion Detectionmentioning

confidence: 99%

A Comprehensive Review on Multiple Instance Learning

Fatima,

Ali,

Kim

2023

Electronics

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Multiple-instance learning has become popular over recent years due to its use in some special scenarios. It is basically a type of weakly supervised learning where the learning dataset contains bags of instances instead of a single feature vector. Each bag is associated with a single label. This type of learning is flexible and a natural fit for multiple real-world problems. MIL has been employed to deal with a number of challenges, including object detection and identification tasks, content-based image retrieval, and computer-aided diagnosis. Medical image analysis and drug activity prediction have been the main uses of MIL in biomedical research. Many Algorithms based on MIL have been put forth over the years. In this paper, we will discuss MIL, the background of MIL and its application in multiple domains, some MIL-based methods, challenges, and lastly, the conclusions and prospects.

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Section: Intrusion Detectionmentioning

confidence: 99%