arXiv subjects

Carlos Sarraute

Publications and source records attributed to Carlos Sarraute.

50 records · Page 3Linked to original sources

arXiv2013

An Oblivious Password Cracking Server

Building a password cracking server that preserves the privacy of the queries made to the server is a problem that has not yet been solved. Such a server could acquire practical relevance in the future: for instance, the tables used to crack the passwords could be calculated, stored and hosted in cloud-computing services, and could be queried from devices with limited computing power. In this paper we present a method to preserve the confidentiality of a password cracker---wherein the tables used to crack the passwords are stored by a third party---by combining Hellman tables and Private Information Retrieval (PIR) protocols. We provide the technical details of this method, analyze its complexity, and show the experimental results obtained with our implementation.

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arXiv2013

Automated Attack Planning

Penetration Testing is a methodology for assessing network security, by generating and executing possible attacks. Doing so automatically allows for regular and systematic testing. A key question then is how to automatically generate the attacks. A natural way to address this issue is as an attack planning problem. In this thesis, we are concerned with the specific context of regular automated pentesting, and use the term "attack planning" in that sense. The following three research directions are investigated. First, we introduce a conceptual model of computer network attacks, based on an analysis of the penetration testing practices. We study how this attack model can be represented in the PDDL language. Then we describe an implementation that integrates a classical planner with a penetration testing tool. This allows us to automatically generate attack paths for real world pentesting scenarios, and to validate these attacks by executing them. Secondly, we present efficient probabilistic planning algorithms, specifically designed for this problem, that achieve industrial-scale runtime performance (able to solve scenarios with several hundred hosts and exploits). These algorithms take into account the probability of success of the actions and their expected cost (for example in terms of execution time, or network traffic generated). Finally, we take a different direction: instead of trying to improve the efficiency of the solutions developed, we focus on improving the model of the attacker. We model the attack planning problem in terms of partially observable Markov decision processes (POMDP). This grounds penetration testing in a well-researched formalism. POMDPs allow the modelling of information gathering as an integral part of the problem, thus providing for the first time a means to intelligently mix scanning actions with actual exploits.

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arXiv2013

Les POMDP font de meilleurs hackers: Tenir compte de l'incertitude dans les tests de penetration

Penetration Testing is a methodology for assessing network security, by generating and executing possible hacking attacks. Doing so automatically allows for regular and systematic testing. A key question is how to generate the attacks. This is naturally formulated as planning under uncertainty, i.e., under incomplete knowledge about the network configuration. Previous work uses classical planning, and requires costly pre-processes reducing this uncertainty by extensive application of scanning methods. By contrast, we herein model the attack planning problem in terms of partially observable Markov decision processes (POMDP). This allows to reason about the knowledge available, and to intelligently employ scanning actions as part of the attack. As one would expect, this accurate solution does not scale. We devise a method that relies on POMDPs to find good attacks on individual machines, which are then composed into an attack on the network as a whole. This decomposition exploits network structure to the extent possible, making targeted approximations (only) where needed. Evaluating this method on a suitably adapted industrial test suite, we demonstrate its effectiveness in both runtime and solution quality.

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arXiv2013

Attack Planning in the Real World

Assessing network security is a complex and difficult task. Attack graphs have been proposed as a tool to help network administrators understand the potential weaknesses of their network. However, a problem has not yet been addressed by previous work on this subject; namely, how to actually execute and validate the attack paths resulting from the analysis of the attack graph. In this paper we present a complete PDDL representation of an attack model, and an implementation that integrates a planner into a penetration testing tool. This allows to automatically generate attack paths for penetration testing scenarios, and to validate these attacks by executing the corresponding actions -including exploits- against the real target network. We present an algorithm for transforming the information present in the penetration testing tool to the planning domain, and show how the scalability issues of attack graphs can be solved using current planners. We include an analysis of the performance of our solution, showing how our model scales to medium-sized networks and the number of actions available in current penetration testing tools.

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arXiv2013

Penetration Testing == POMDP Solving?

Penetration Testing is a methodology for assessing network security, by generating and executing possible attacks. Doing so automatically allows for regular and systematic testing without a prohibitive amount of human labor. A key question then is how to generate the attacks. This is naturally formulated as a planning problem. Previous work (Lucangeli et al. 2010) used classical planning and hence ignores all the incomplete knowledge that characterizes hacking. More recent work (Sarraute et al. 2011) makes strong independence assumptions for the sake of scaling, and lacks a clear formal concept of what the attack planning problem actually is. Herein, we model that problem in terms of partially observable Markov decision processes (POMDP). This grounds penetration testing in a well-researched formalism, highlighting important aspects of this problem's nature. POMDPs allow to model information gathering as an integral part of the problem, thus providing for the first time a means to intelligently mix scanning actions with actual exploits.

cs.AI↗

arXiv2013

An Algorithm to Find Optimal Attack Paths in Nondeterministic Scenarios

As penetration testing frameworks have evolved and have become more complex, the problem of controlling automatically the pentesting tool has become an important question. This can be naturally addressed as an attack planning problem. Previous approaches to this problem were based on modeling the actions and assets in the PDDL language, and using off-the-shelf AI tools to generate attack plans. These approaches however are limited. In particular, the planning is classical (the actions are deterministic) and thus not able to handle the uncertainty involved in this form of attack planning. We herein contribute a planning model that does capture the uncertainty about the results of the actions, which is modeled as a probability of success of each action. We present efficient planning algorithms, specifically designed for this problem, that achieve industrial-scale runtime performance (able to solve scenarios with several hundred hosts and exploits). These algorithms take into account the probability of success of the actions and their expected cost (for example in terms of execution time, or network traffic generated). We thus show that probabilistic attack planning can be solved efficiently for the scenarios that arise when assessing the security of large networks. Two "primitives" are presented, which are used as building blocks in a framework separating the overall problem into two levels of abstraction. We also present the experimental results obtained with our implementation, and conclude with some ideas for further work.

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arXiv2013

Aplicacion de las Redes Neuronales al Reconocimiento de Sistemas Operativos

In this work we present a family of neural networks, the multi-layer perceptron networks, and some of the algorithms used to train those networks (we hope that with enough details and precision as to satisfy a mathematical public). Then we study how to use those networks to solve a problem that arises from the field of information security: the remote identification of Operating Systems (part of the information gathering steps of the penetration testing methodology). This is the contribution of this work: it is an application of classic Artificial Intelligence techniques to a classification problem that gave better results than the classic techniques used to solve it.

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arXiv2010

Outrepasser les limites des techniques classiques de Prise d'Empreintes grace aux Reseaux de Neurones

We present an application of Artificial Intelligence techniques to the field of Information Security. The problem of remote Operating System (OS) Detection, also called OS Fingerprinting, is a crucial step of the penetration testing process, since the attacker (hacker or security professional) needs to know the OS of the target host in order to choose the exploits that he will use. OS Detection is accomplished by passively sniffing network packets and actively sending test packets to the target host, to study specific variations in the host responses revealing information about its operating system. The first fingerprinting implementations were based on the analysis of differences between TCP/IP stack implementations. The next generation focused the analysis on application layer data such as the DCE RPC endpoint information. Even though more information was analyzed, some variation of the "best fit" algorithm was still used to interpret this new information. Our new approach involves an analysis of the composition of the information collected during the OS identification process to identify key elements and their relations. To implement this approach, we have developed tools using Neural Networks and techniques from the field of Statistics. These tools have been successfully integrated in a commercial software (Core Impact).

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arXiv2010

Advanced Software Protection Now

Software digital rights management is a pressing need for the software development industry which remains, as no practical solutions have been acclamaimed succesful by the industry. We introduce a novel software-protection method, fully implemented with today's technologies, that provides traitor tracing and license enforcement and requires no additional hardware nor inter-connectivity. Our work benefits from the use of secure triggers, a cryptographic primitive that is secure assuming the existence of an ind-cpa secure block cipher. Using our framework, developers may insert license checks and fingerprints, and obfuscate the code using secure triggers. As a result, this rises the cost that software analysis tools have detect and modify protection mechanisms. Thus rising the complexity of cracking this system.

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arXiv2010

Simulation of Computer Network Attacks

In this work we present a prototype for simulating computer network attacks. Our objective is to simulate large networks (thousands of hosts, with applications and vulnerabilities) while remaining realistic from the attacker's point of view. The foundation for the simulator is a model of computer intrusions, based on the analysis of real world attacks. In particular we show how to interpret vulnerabilities and exploits as communication channels. This conceptual model gives a tool to describe the theater of operations, targets, actions and assets involved in multistep network attacks. We conclude with applications of the attack simulator.

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arXiv2010

An attack on MySQL's login protocol

The MySQL challenge-and-response authentication protocol is proved insecure. We show how can an eavesdropper impersonate a valid user after witnessing only a few executions of this protocol. The algorithm of the underlying attack is presented. Finally we comment about implementations and statistical results.

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arXiv2010

Building Computer Network Attacks

In this work we start walking the path to a new perspective for viewing cyberwarfare scenarios, by introducing conceptual tools (a formal model) to evaluate the costs of an attack, to describe the theater of operations, targets, missions, actions, plans and assets involved in cyberwarfare attacks. We also describe two applications of this model: autonomous planning leading to automated penetration tests, and attack simulations, allowing a system administrator to evaluate the vulnerabilities of his network.

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arXiv2010

Using Neural Networks to improve classical Operating System Fingerprinting techniques

We present remote Operating System detection as an inference problem: given a set of observations (the target host responses to a set of tests), we want to infer the OS type which most probably generated these observations. Classical techniques used to perform this analysis present several limitations. To improve the analysis, we have developed tools using neural networks and Statistics tools. We present two working modules: one which uses DCE-RPC endpoints to distinguish Windows versions, and another which uses Nmap signatures to distinguish different version of Windows, Linux, Solaris, OpenBSD, FreeBSD and NetBSD systems. We explain the details of the topology and inner workings of the neural networks used, and the fine tuning of their parameters. Finally we show positive experimental results.

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arXiv2010

Simulating Cyber-Attacks for Fun and Profit

We introduce a new simulation platform called Insight, created to design and simulate cyber-attacks against large arbitrary target scenarios. Insight has surprisingly low hardware and configuration requirements, while making the simulation a realistic experience from the attacker's standpoint. The scenarios include a crowd of simulated actors: network devices, hardware devices, software applications, protocols, users, etc. A novel characteristic of this tool is to simulate vulnerabilities (including 0-days) and exploits, allowing an attacker to compromise machines and use them as pivoting stones to continue the attack. A user can test and modify complex scenarios, with several interconnected networks, where the attacker has no initial connectivity with the objective of the attack. We give a concise description of this new technology, and its possible uses in the security research field, such as pentesting training, study of the impact of 0-days vulnerabilities, evaluation of security countermeasures, and risk assessment tool.

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