Browsing by Author "Rezapour, Amir"

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A Robust Algorithm for Predicting Attacks Using Collaborative Security Logs
(2020-05-01)
Rezapour, Amir
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Tzeng, Wen-Guey
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資訊工程學系
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Department of Computer Science
As networks become ubiquitous in our daily lives, users rely more on networks for exchanging data and communication. However, numerous new and sophisticated attacks that endanger security of users have been reported. In practice, blacklisting illicit sources has been a fundamental defense strategy in recent years. In this paper, we propose a predictor that is based on the observations from a centralized log-sharing infrastructure. Our observations include the direct relation between attackers and victims, victim similarities, and attacker correlations. We compile a customized blacklist for each Dshield.org contributor using a weighted function of direct and indirect relations between victims and attackers. This list not only offers a significantly higher prediction ratio, but also includes source addresses with potentially higher threats. We evaluate our predictor using two months of malicious activities acquired from Dshield.org. The experimental results demonstrate a significant improvement over previous algorithms.
Monsieur Poirot: Detecting Botnets Using Re-Identification Algorithm and Nontrivial Feature Selection Technique
(2018-01-01)
Lee, Wei-Min
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Rezapour, Amir
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Tzeng, Wen-Guey
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資訊工程學系
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Department of Computer Science
- Modern botnets are progressively migrating to P2P network to resist against take-down attempts. In addition, new botnets use randomization in their behavior to evade detection. In this paper, we propose a new method for detecting stealthy P2P bots. We formulate the problem as a re-identification problem. This opens the possibility of powerful instantiations of detection algorithms to address the botnet detection problem. We also use a nontrivial feature selection technique to discover the best feature pairs for conducting comparison between two flows. We use real-world botnet data to evaluate the performance of Monsieur Poirot and compare it with existing flow-based algorithms. Monsieur Poirot is robust towards injection of noise in the communication patterns. The experimental results show that Monsieur Poirot is able to identify P2P bots with an average TPR of 98.65% and an average FPR of 0.21%.
Privacy-preserving ridge regression on distributed data
(2018-07-01)
Chen, Yi-Ruei
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Rezapour, Amir
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Tzeng, Wen-Guey
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交大名義發表
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National Chiao Tung University
Ridge regression is a statistical method for modeling a linear relationship between a dependent variable and some explanatory values. It is a building-block that plays a major role in many learning algorithms such as recommendation systems. However, in many applications such as e-health, explanatory values contains private information owned by different patients that are not willing to share them, unless data privacy is guaranteed. In this paper, we propose a protocol for conducting privacy-preserving ridge regression (PPRR) over high-dimensional data. In our protocol, each user submits its data in an encrypted form to an evaluator and the evaluator computes a linear model of all users' data without learning their contents. The core encryption method is equipped with homomorphic properties to enable the evaluator to perform ridge regression over encrypted data. We implement our protocol and demonstrate that it is suitable for dealing with high-dimensional data distributed among millions of users. We also compare our protocol with the state-of-the-art solutions in terms of both computation and communication costs. The results show that our protocol outperforms most existing approaches based on secure multi-party computation, garbled circuit, fully homomorphic encryption, secret-sharing, and hybrid methods. (C) 2018 Elsevier Inc. All rights reserved.
The Forward-Backward String: a New Robust Feature for Botnet Detection
(2017-01-01)
Su, Yuan-Hsiang
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Rezapour, Amir
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Tzeng, Wen-Guey
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資訊工程學系
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Department of Computer Science
We introduce the forward-backward string as a new feature which is robust against variation over payload length, the inter-arrival time of packets, and the number of packets within a flow. It represents an abstract activity of a host within a flow. The forward-backward string is packet-oriented and does not rely on payload size, the content of header and the interarrival time of packets. We use real-world botnet data to evaluate the performance of our new feature with some existing works. The experimental results show that the forward-backward string boosts the accuracy of existing works up to 5%. We further examine the robustness of the new feature against packet and flow level noise. The forward-backward string not only increases the accuracy but also enhances the robustness of the prior works.
Unique Signature with Short Output from CDH Assumption
(2015)
Shen, Shiuan-Tzuo
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Rezapour, Amir
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Tzeng, Wen-Guey
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資訊工程學系
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Department of Computer Science
We give a simple and efficient construction of unique signature on groups equipped with bilinear map. In contrast to prior works, our proof of security is based on computational Diffie-Hellman problem in the random oracle model. Meanwhile, the resulting signature consists of only one group element. Due to its simplicity, security and efficiency, our scheme is suitable for those situations that require to overcome communication bottlenecks. Moreover, the unique signature is a building block for designing chosen-ciphertext secure cryptosystems and verifiable random functions, which have found many interesting applications in cryptographic protocol design.