arXiv subjects

Aikaterini Mitrokotsa

Publications and source records attributed to Aikaterini Mitrokotsa.

8 recordsLinked to original sources

arXiv2016

Bayesian Differential Privacy through Posterior Sampling

Differential privacy formalises privacy-preserving mechanisms that provide access to a database. We pose the question of whether Bayesian inference itself can be used directly to provide private access to data, with no modification. The answer is affirmative: under certain conditions on the prior, sampling from the posterior distribution can be used to achieve a desired level of privacy and utility. To do so, we generalise differential privacy to arbitrary dataset metrics, outcome spaces and distribution families. This allows us to also deal with non-i.i.d or non-tabular datasets. We prove bounds on the sensitivity of the posterior to the data, which gives a measure of robustness. We also show how to use posterior sampling to provide differentially private responses to queries, within a decision-theoretic framework. Finally, we provide bounds on the utility and on the distinguishability of datasets. The latter are complemented by a novel use of Le Cam's method to obtain lower bounds. All our general results hold for arbitrary database metrics, including those for the common definition of differential privacy. For specific choices of the metric, we give a number of examples satisfying our assumptions.

stat.ML↗

arXiv2013

Near-Optimal Blacklisting

Many applications involve agents sharing a resource, such as networks or services. When agents are honest, the system functions well and there is a net profit. Unfortunately, some agents may be malicious, but it may be hard to detect them. We consider the intrusion response problem of how to permanently blacklist agents, in order to maximise expected profit. This is not trivial, as blacklisting may erroneously expel honest agents. Conversely, while we gain information by allowing an agent to remain, we may incur a cost due to malicious behaviour. We present an efficient algorithm (HIPER) for making near-optimal decisions for this problem. Additionally, we derive three algorithms by reducing the problem to a Markov decision process (MDP). Theoretically, we show that HIPER is near-optimal. Experimentally, its performance is close to that of the full MDP solution, when the (stronger) requirements of the latter are met.

cs.CR↗

arXiv2010

Expected loss analysis of thresholded authentication protocols in noisy conditions

A number of authentication protocols have been proposed recently, where at least some part of the authentication is performed during a phase, lasting $n$ rounds, with no error correction. This requires assigning an acceptable threshold for the number of detected errors. This paper describes a framework enabling an expected loss analysis for all the protocols in this family. Furthermore, computationally simple methods to obtain nearly optimal value of the threshold, as well as for the number of rounds is suggested. Finally, a method to adaptively select both the number of rounds and the threshold is proposed.

cs.CR↗

arXiv2010

Shedding Light on RFID Distance Bounding Protocols and Terrorist Fraud Attacks

The vast majority of RFID authentication protocols assume the proximity between readers and tags due to the limited range of the radio channel. However, in real scenarios an intruder can be located between the prover (tag) and the verifier (reader) and trick this last one into thinking that the prover is in close proximity. This attack is generally known as a relay attack in which scope distance fraud, mafia fraud and terrorist attacks are included. Distance bounding protocols represent a promising countermeasure to hinder relay attacks. Several protocols have been proposed during the last years but vulnerabilities of major or minor relevance have been identified in most of them. In 2008, Kim et al. [1] proposed a new distance bounding protocol with the objective of being the best in terms of security, privacy, tag computational overhead and fault tolerance. In this paper, we analyze this protocol and we present a passive full disclosure attack, which allows an adversary to discover the long-term secret key of the tag. The presented attack is very relevant, since no security objectives are met in Kim et al.'s protocol. Then, design guidelines are introduced with the aim of facilitating protocol designers the stimulating task of designing secure and efficient schemes against relay attacks. Finally a new protocol, named Hitomi and inspired by [1], is designed conforming the guidelines proposed previously.

cs.CR↗

arXiv2009

Statistical Decision Making for Authentication and Intrusion Detection

User authentication and intrusion detection differ from standard classification problems in that while we have data generated from legitimate users, impostor or intrusion data is scarce or non-existent. We review existing techniques for dealing with this problem and propose a novel alternative based on a principled statistical decision-making view point. We examine the technique on a toy problem and validate it on complex real-world data from an RFID based access control system. The results indicate that it can significantly outperform the classical world model approach. The method could be more generally useful in other decision-making scenarios where there is a lack of adversary data.

stat.ML↗

arXiv2008

Intrusion Detection Using Cost-Sensitive Classification

Intrusion Detection is an invaluable part of computer networks defense. An important consideration is the fact that raising false alarms carries a significantly lower cost than not detecting at- tacks. For this reason, we examine how cost-sensitive classification methods can be used in Intrusion Detection systems. The performance of the approach is evaluated under different experimental conditions, cost matrices and different classification models, in terms of expected cost, as well as detection and false alarm rates. We find that even under unfavourable conditions, cost-sensitive classification can improve performance significantly, if only slightly.

cs.CR↗

arXiv2008

Intrusion Detection in Mobile Ad Hoc Networks Using Classification Algorithms

In this paper we present the design and evaluation of intrusion detection models for MANETs using supervised classification algorithms. Specifically, we evaluate the performance of the MultiLayer Perceptron (MLP), the Linear classifier, the Gaussian Mixture Model (GMM), the Naive Bayes classifier and the Support Vector Machine (SVM). The performance of the classification algorithms is evaluated under different traffic conditions and mobility patterns for the Black Hole, Forging, Packet Dropping, and Flooding attacks. The results indicate that Support Vector Machines exhibit high accuracy for almost all simulated attacks and that Packet Dropping is the hardest attack to detect.

cs.CR↗

arXiv2008

Towards an Effective Intrusion Response Engine Combined with Intrusion Detection in Ad Hoc Networks

In this paper, we present an effective intrusion response engine combined with intrusion detection in ad hoc networks. The intrusion response engine is composed of a secure communication module, a local and a global response module. Its function is based on an innovative tree-based key agreement protocol while the intrusion detection engine is based on a class of neural networks called eSOM. The proposed intrusion response model and the tree-based protocol, it is based on, are analyzed concerning key secrecy while the intrusion detection engine is evaluated for MANET under different traffic conditions and mobility patterns. The results show a high detection rate for packet dropping attacks.

cs.CR↗