Research And Implementation Of Anomaly Detection System Based On The Improved Local Outlier Detection Algorithm

With the increasing demand of computer network services and thefast technology development, the architecture of network becomeincreasingly complex and the network traffic daily rapidly grows. In such alarge and complex model, many kinds of problems emerge, such asexcessive network traffic load exceeds the server buffer cause a systemcrash, malicious users using many methods to attack and invade servers,etc. Different anomalies would bring different losses, such as reducingsystem performance, system crash, etc. Therefore, in order to ensure thestability, security and the efficiency of a system, we need to avoid theoccurrence of the anomaly events. Even if an anomaly event occurs, weneed to detect it in time and take appropriate measures.In this paper, we find two problems in the local outlier factor anomalydetection algorithm. The first problem is that the factor is not reasonablewhen there is quasi-linear relationship between two dimensions. A solutionwhich is using Mahalanobis distance instead of traditional Euclideandistance is proposed to solve the problem. And linear transformation isused to make the time complexity of the new algorithm is the same asbefore. The second problem is that when there are two clusters withdifferent density adjacent in multidimensional space, there are many falsepositives in the data points which are on the edge of the adjacent. Localinfluential set which is using the nearest neighbors and reverse nearestneighbors is proposed to solve this problem. Compared to the tradition LOF algorithm, the improved LOF algorithm’s detection result is betterwhile the running time is almost the same.