透過您的圖書館登入
IP:3.141.200.180
  • 期刊

An Intrusion Prediction Technique Based on Co-evolutionary Immune System for Network Security (CoCo-IDP)

並列摘要


Forecasting the unknown and detecting the known threats1 and targeted attacks^2 are the most concern of network security especially in large scale environment. We have presented an intrusion^3 detection and prediction system using cooperative co-evolutionary immune system for distributed data networks. This is an intelligent technique based on genetic algorithm and co-evolutionary immune system where the detectors can discriminate the existing incidents^4 and predicting the new incidents in a distributed environment. We have prepared a prototype of CoCo-IDP^5 in a Jini platform running grid computing^6 in distributed systems. Evaluation results show that, the CoCo-IDP can adaptively converge for the best answer and can detect or predict the incidents in a selected boundary. Moreover, the system generates the flexible detectors with diversity in a variable threshold. In comparison with pure Immune System (IS), the obtained results show that the proposed system has simpler rules, more powerful detection and prediction capabilities with high accuracy metric. We have compared the probability of detection and false accuracy rate in KDD^7 database with several well known methods for proof and validation of our results.

被引用紀錄


陳怡婷(2012)。群播環境下的雙層網路編碼〔碩士論文,國立臺灣師範大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0021-1610201315270861

延伸閱讀