• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.3093-3115
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• 2020

Network anomaly detection system plays an essential role in detecting network anomaly and ensuring network security. Anomaly detection system based machine learning has become an increasingly popular solution. However, due to the unbalance and high-dimension characteristics of network traffic, the existing methods unable to achieve the excellent performance of high accuracy and low false alarm rate. To address this problem, a new network anomaly detection method based on data balancing and recursive feature addition is proposed. Firstly, data balancing algorithm based on improved KNN outlier detection is designed to select part respective data on each category. Combination optimization about parameters of improved KNN outlier detection is implemented by genetic algorithm. Next, recursive feature addition algorithm based on correlation analysis is proposed to select effective features, in which a cross contingency test is utilized to analyze correlation and obtain a features subset with a strong correlation. Then, random forests model is as the classification model to detection anomaly. Finally, the proposed algorithm is evaluated on benchmark datasets KDD Cup 1999 and UNSW_NB15. The result illustrates the proposed strategies enhance accuracy and recall, and decrease the false alarm rate. Compared with other algorithms, this algorithm still achieves significant effects, especially recall in the small category.

• Journal of the Korea Society for Simulation
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• v.20 no.1
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• pp.107-116
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• 2011

Recently, femtocell has been concerned as one of effective solutions to relieve shadow region and provide high quality services to users in indoor environments. Even though femtocell offers various benefits to cellular operators and users, many technical issues, such as interference coordination, network synchronization, self-configuration, self-optimization, and so on, should be solved to deploy the femtocell in current network. In this paper, we develop a simulator for evaluating performance of long term evolution (LTE) femtocell systems under various interference scenarios. The simulator consists of a main-module and five sub-modules. The main-module connects and manages five sub-modules which have the functionality managing user mobility, packet scheduling, call admission control, traffic generation, and modulation and coding scheme (MCS). To provide user convenience, the simulator adopts graphical user interface (GUI) which can observes simulation results in real time. We expect that this simulator can contribute to developing effective femtocell systems by supporting a tool for analyzing the effect of interference between macrocell and femtocell.