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March 2019

Verifying security protocols using BAN logic

March 21 @ 8:30 am - 10:00 am
Facultatea de Matematica si Informatica, sala 202, Strada Academiei nr 14
Bucuresti, Romania
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Speaker: Alexandru Dragomir (University of Bucharest) Abstract: Epistemic logics - logics aimed at reasoning about knowledge and belief - are widely considered to be suitable for modelling, analyzing and predicting vulnerabilities of security protocols. One of the first and most discussed logical approaches to the problem of verifying security protocols is the one proposed in BAN logic (Burrows, Abadi & Needham 1989), a many-sorted epistemic logic used for its intuitive and compelling set of inference rules devised for reasoning about…

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April 2019

Verifying security protocols using BAN logic – Part 2

April 4 @ 8:30 am - 9:00 am
Facultatea de Matematica si Informatica, sala 202, Strada Academiei nr 14
Bucuresti, Romania
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Speaker: Alexandru Dragomir (University of Bucharest) Abstract: Epistemic logics - logics aimed at reasoning about knowledge and belief - are widely considered to be suitable for modelling, analyzing and predicting vulnerabilities of security protocols. One of the first and most discussed logical approaches to the problem of verifying security protocols is the one proposed in BAN logic (Burrows, Abadi & Needham 1989), a many-sorted epistemic logic used for its intuitive and compelling set of inference rules devised for reasoning about…

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How to find bugs in your (x86) code: Applications that use RIVER

April 4 @ 9:00 am - 10:00 am
Facultatea de Matematica si Informatica, sala 202, Strada Academiei nr 14
Bucuresti, Romania
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Speaker: Bogdan Ghimiș (University of Bucharest) Abstract: From a security perspective, discovering bugs before shipping a product is crucial. This presentation will be about RIVER, a tool that can help us to inspect x86 binary code. This lecture will encompass two papers describing methods of finding problematic inputs: a genetic algorithm and a method using taint analysis. The genetic algorithm is used in conjunction with Apache Spark - an engine used for big data and distributed computing - to determine…

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Anti-Malware Machine Learning

April 18 @ 8:30 am - 10:00 am
Facultatea de Matematica si Informatica, sala 202, Strada Academiei nr 14
Bucuresti, Romania
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Speaker: Andra Băltoiu (University of Bucharest) Abstract: In a previous seminar, we introduced Dictionary Learning (DL), a machine learning method capable of handling the requirements of IoT-related tasks, motivated by its reduced computational complexity, theoretical guarantees and its applicability to continuous retraining contexts. We now discuss the task of training different machine learning and DL models in order to identify malware and study the adaptability of the resulting models to new types of malware.

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May 2019

Dynamic Code Analysis

May 30 @ 5:00 pm - 6:00 pm

Speaker:  Radu Velea (BitDefender) Abstract: Static code analysis methods have the advantage of providing deterministic and reliable results. Malware has evolved beyond the point where simple pattern matching algorithms or signatures can provide adequate levels of protection. To respond to new threats we have to look at other hidden aspects such as execution behavior and fight evasive techniques by performing dynamic code analysis. This presentation discusses how to do this using runtime emulation and describes the existing challenges for the…

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July 2019

Anomaly Detection Reading Group: Deep OC-SVM

July 25 @ 10:00 am - 11:30 pm
Facultatea de Matematica si Informatica, sala 202, Strada Academiei nr 14
Bucuresti, Romania
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Speaker: Andrei Pătrașcu (University of Bucharest) Abstract: Recent empirical results confirm that one-class (OC) classification methods remain among the most important learning strategies for anomaly detection. In this seminar, we will technically describe in detail multiple basic OC schemes such as OC-SVM and SVDD and their deep variants, in order to identify room of improvements or generalization directions towards the graph anomaly detection context.

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August 2019

Anomaly Detection Reading Group: Deep RPCA

August 1 @ 10:00 am - 11:30 pm
Facultatea de Matematica si Informatica, sala 202, Strada Academiei nr 14
Bucuresti, Romania
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Speaker: Andrei Pătrașcu (University of Bucharest) Abstract: We continue our adventure by investigating existing results with Robust Principal Component Analysis (RPCA) and its adaptation to existing deep neural networks. Required reading: ZHOU, Chong; PAFFENROTH, Randy C. Anomaly detection with robust deep autoencoders. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2017. p. 665-674. CANDÈS, Emmanuel J., et al. Robust principal component analysis?. Journal of the ACM (JACM), 2011, 58.3: 11.

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Anomaly Detection Reading Group: Graph Classification

August 8 @ 10:00 am - 11:30 pm
Facultatea de Matematica si Informatica, sala Google

Speaker: Andra Băltoiu (University of Bucharest) Abstract: We continue our investigation on the task of detecting outliers in networks, by looking at the concept of signal variation on a graph. Required reading: A. Sandryhaila and J. M. F. Moura, "Classification via regularization on graphs," 2013 IEEE Global Conference on Signal and Information Processing, Austin, TX, 2013, pp. 495-498. S. Chen, A. Sandryhaila, J. M. F. Moura and J. Kovačević, "Signal Recovery on Graphs: Variation Minimization," in IEEE Transactions on Signal…

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Anomaly Detection Reading Group: Distributed Online AD

August 21 @ 10:00 am - 11:30 pm
Facultatea de Matematica si Informatica, sala Google

Speaker: Paul Irofti (University of Bucharest) Abstract: We continue our investigation on the task of detecting outliers in networks when dealing with big-data and investigate existing online and distributed solutions. Required reading: Miao, Xuedan, et al. "Distributed online one-class support vector machine for anomaly detection over networks." IEEE transactions on cybernetics 49.4 (2018): 1475-1488. Liu, Zhaoting, Ying Liu, and Chunguang Li. "Distributed sparse recursive least-squares over networks." IEEE Transactions on Signal Processing 62.6 (2014): 1386-1395.

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Anomaly Detection Reading Group: Gaussian Mixture Models

August 30 @ 10:00 am - 11:30 pm
Facultatea de Matematica si Informatica, sala 202, Strada Academiei nr 14
Bucuresti, Romania
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Speaker: Andrei Pătrașcu (University of Bucharest) Abstract: We continue our adventure by investigating existing results using Gaussian Mixture Models (GMM) for anomaly detection and their adaptation to existing deep neural networks. Required reading: Zong, Bo, et al. "Deep autoencoding gaussian mixture model for unsupervised anomaly detection." (2018). Chapter 11 from Deisenroth, Marc Peter, A. Aldo Faisal, and Cheng Soon Ong. "Mathematics for Machine Learning." (2018).

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