Roni Mateless | Tel Aviv University (original) (raw)
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Papers by Roni Mateless
Perform. Evaluation, 2021
In this paper we present a novel algorithm and efficient data structure for anomaly detection bas... more In this paper we present a novel algorithm and efficient data structure for anomaly detection based on temporal data. Time-series data are represented by a sequence of symbolic time intervals, describing increasing and decreasing trends, in a compact way using gradient temporal abstraction technique. Then we identify unusual subsequences in the resulting sequence using dynamic data structure based on the geometric observations supporting polylogarithmic update and query times. Moreover, we introduce a new parameter to control the pairwise difference between the corresponding symbols in addition to a distance metric between the subsequences. Experimental results on a public DNS network traffic dataset show the superiority of our approach compared to the baselines.
Lecture Notes in Computer Science, 2019
In this paper we propose a novel approach to identify anomalies in DNS traffic. The traffic time-... more In this paper we propose a novel approach to identify anomalies in DNS traffic. The traffic time-points data is transformed to a string, which is used by new fast approximate string matching algorithm to detect anomalies. Our approach is generic in its nature and allows fast adaptation to different types of traffic. We evaluate the approach on a large public dataset of DNS traffic based on 10 days, discovering more than order of magnitude DNS attacks in comparison to auto-regression as a baseline. Moreover, the additional comparison has been made including other common regressors such as Linear Regression, Lasso, Random Forest and KNN, all of them showing the superiority of our approach.
IEEE Transactions on Network and Service Management
Perform. Evaluation, 2021
In this paper we present a novel algorithm and efficient data structure for anomaly detection bas... more In this paper we present a novel algorithm and efficient data structure for anomaly detection based on temporal data. Time-series data are represented by a sequence of symbolic time intervals, describing increasing and decreasing trends, in a compact way using gradient temporal abstraction technique. Then we identify unusual subsequences in the resulting sequence using dynamic data structure based on the geometric observations supporting polylogarithmic update and query times. Moreover, we introduce a new parameter to control the pairwise difference between the corresponding symbols in addition to a distance metric between the subsequences. Experimental results on a public DNS network traffic dataset show the superiority of our approach compared to the baselines.
Lecture Notes in Computer Science, 2019
In this paper we propose a novel approach to identify anomalies in DNS traffic. The traffic time-... more In this paper we propose a novel approach to identify anomalies in DNS traffic. The traffic time-points data is transformed to a string, which is used by new fast approximate string matching algorithm to detect anomalies. Our approach is generic in its nature and allows fast adaptation to different types of traffic. We evaluate the approach on a large public dataset of DNS traffic based on 10 days, discovering more than order of magnitude DNS attacks in comparison to auto-regression as a baseline. Moreover, the additional comparison has been made including other common regressors such as Linear Regression, Lasso, Random Forest and KNN, all of them showing the superiority of our approach.
IEEE Transactions on Network and Service Management