CBR based reactive behavior learning for the memory-prediction framework

Herrero-Reder, I.; Urdiales, Cristina; Peula, J.M.; Hernández, Francisco

doi:10.1016/j.neucom.2016.10.075

Cited by 9 publications

(2 citation statements)

References 24 publications

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“…A novel framework to reveal the human neocortex function was proposed by Hawkins et al [19,20]. Furthermore, as more experiments were conducted, it was found that grid cells, such as neurons, may also be present in the neocortex [21,22]. The memory-prediction framework has been implemented in various fields, including object identification [23,24], medicine [25], online learning [26,27], real-time network traffic anomaly detection [28][29][30][31], and network forensics [32].…”

Section: Related Workmentioning

confidence: 99%

User Behavior Traffic Analysis Using a Simplified Memory-Prediction Framework

Budiarto¹,

Alqarni²,

Alzahrani³

et al. 2022

Computers, Materials &Amp; Continua

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As nearly half of the incidents in enterprise security have been triggered by insiders, it is important to deploy a more intelligent defense system to assist enterprises in pinpointing and resolving the incidents caused by insiders or malicious software (malware) in real-time. Failing to do so may cause a serious loss of reputation as well as business. At the same time, modern network traffic has dynamic patterns, high complexity, and large volumes that make it more difficult to detect malware early. The ability to learn tasks sequentially is crucial to the development of artificial intelligence. Existing neurogenetic computation models with deep-learning techniques are able to detect complex patterns; however, the models have limitations, including catastrophic forgetfulness, and require intensive computational resources. As defense systems using deep-learning models require more time to learn new traffic patterns, they cannot perform fully online (on-the-fly) learning. Hence, an intelligent attack/malware detection system with on-the-fly learning capability is required. For this paper, a memory-prediction framework was adopted, and a simplified single cell assembled sequential hierarchical memory (s.SCASHM) model instead of the hierarchical temporal memory (HTM) model is proposed to speed up learning convergence to achieve onthe-fly learning. The s.SCASHM consists of a Single Neuronal Cell (SNC) model and a simplified Sequential Hierarchical Superset (SHS) platform. The s.SCASHM is implemented as the prediction engine of a user behavior analysis tool to detect insider attacks/anomalies. The experimental results show that the proposed memory model can predict users' traffic behavior with accuracy level ranging from 72% to 83% while performing on-the-fly learning.

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Section: Related Workmentioning

confidence: 99%

User Behavior Traffic Analysis Using a Simplified Memory-Prediction Framework

Budiarto¹,

Alqarni²,

Alzahrani³

et al. 2022

Computers, Materials &Amp; Continua

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“…The generalization ability of the method was verified by training and evaluating two independent datasets. To simulate the memory process of the human brain, Herrero [23] introduced a bottom-up CBR-learning framework and trained a group of cooperative/competitive reaction behaviors of the Aibo robot in the RoboCup environment to test the effectiveness of the proposed framework.…”

Section: Literature Reviewmentioning

confidence: 99%

Case-Based Reasoning for Hidden Property Analysis of Judgment Debtors

Zhang

Zhou

et al. 2021

Mathematics

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Many judgment debtors try to evade, confront, and delay law enforcement using concealing and transferring their property to resist law enforcement in China. The act of hiding property seriously affects people’s legitimate rights and interests and China’s legal authority. Therefore, it is essential to find an effective method of analyzing whether a judgment debtor hides property. Aiming at the hidden property analysis problem, we propose a case-based reasoning method for the judgment debtor’s hidden property analysis. In the hidden property analysis process, we present the attributes of the enforcement case by crisp symbols, crisp numbers, interval numbers, and fuzzy linguistic variables and develop a hybrid similarity measure between the historical enforcement case and the target enforcement case. The results show that the recommendations obtained with the information and knowledge of similar historical cases are consistent with judicial practice, which can reduce the work pressure of law enforcement officers and improve the efficiency of handling enforcement cases.

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An Integrated Approach to Predicting the Influence of Reputation Mechanisms on Q&A Communities

Yang

Mao

2023

Lecture Notes in Computer Science

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CBR based reactive behavior learning for the memory-prediction framework

Cited by 9 publications

References 24 publications

User Behavior Traffic Analysis Using a Simplified Memory-Prediction Framework

User Behavior Traffic Analysis Using a Simplified Memory-Prediction Framework

Case-Based Reasoning for Hidden Property Analysis of Judgment Debtors

An Integrated Approach to Predicting the Influence of Reputation Mechanisms on Q&A Communities

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