Trafik Ağlarında Anomali Tespiti

Year 2018, , 132 - 138, 30.09.2018

Özlem Örnek , Seval Vatan Serpil Sarıoğlu Ahmet Yazıcı

https://doi.org/10.31796/ogummf.440285

Cited By: 2

Abstract

Trafik ağlarının gittikçe büyümesiyle beraber izlenmesi ve kontrolünün sağlanması da zorlaşmıştır. Bu izlemenin sağlanmasında da anomali tespiti çok önemli bir yere sahiptir. Trafik ağlarında anomali tespiti yaklaşımları ile olaylar erkenden tespit edilerek hızlı müdahale imkanı sağlanır. Bu ise zaman ve maliyet tasarrufu sağlar. Literatürde farklı alanlardaki problemler için anomali tespitinde sınıflandırma, kümeleme, istatistiksel vb. yaklaşımlar bulunmaktadır. Bu alanda destek vektör makineleri, bayes ağları, bulanık mantık, genetik algoritmalar vb. birçok yöntem anomali tespiti için kullanılmaktadır. Bu çalışmada karar ağacı algoritması ile trafik ağlarında anomali tespiti için bir yöntem önerilmiştir. Önerilen yöntem Britanya Kolumbiyası’na ait trafik veri seti kullanılarak test edilmiştir. Yapılan testlerde trafik ağındaki bazı anormal olayların önerilen yöntem ile tespit edilebileceği görülmüştür. 

Keywords

Anomali tespiti, karar ağaçları, sınıflandırma algoritmaları, trafik ağları

Anomaly Detection in Traffic Networks

Abstract

Along with the growing traffic networks, the control of traffic networks has become increasingly difficult. The detection of anomaly is very important in ensuring this monitoring. With anomaly detection approaches in traffic networks, events are detected early and quick intervention is provided. This saves time and money. There are classification, clustering, statistical, etc. approaches in anomaly detection for problems in the literature. Support vector machines, Bayesian networks, fuzzy logic, genetic algorithms, etc. are main approaches that used to detect anomalies. In this study, a decision tree algorithm is proposed to detect anomaly in traffic networks. The proposed method has been tested using the British Colombia traffic data set. It is seen that some abnormal events in the traffic network can be detected by using the proposed method. 

Keywords

Anomaly detection, decision trees, classification algorithms, traffic networks

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