The amount of traffic data collected by automatic number plate reading systems constantly incrseases. It is therefore important, for law enforcement agencies, to find convenient techniques and tools to analyze such data. In this paper we propose a scalable and fully automated procedure leveraging the Apache Accumulo technology that allows an effective importing and processing of traffic data. We discuss preliminary results obtained by using our application for the analysis of a dataset containing real traffic data provided by the Italian National Police. We believe the results described here can pave the way to further interesting research on the matter.

This paper describes the efforts, pitfalls, and successes of applying unsupervised classification techniques to analyze the Trap-2017 dataset. Guided by the informative perspective on the nature of the dataset obtained through a set of specifically-written perl/bash scripts, we devised an automated clustering tool implemented in python upon openly-available scientific libraries. By applying our tool on the original raw data it is possibile to infer a set of trending behaviors for vehicles travelling over a route, yielding an instrument to classify both routes and plates. Our results show that addressing the main goal of the Trap-2017 initiative (``to identify itineraries that could imply a criminal intent'') is feasible even in the presence of an unlabelled and noisy dataset, provided that the unique characteristics of the problem are carefully considered. Albeit several optimizations for the tool are still under investigation, we believe that it may already pave the way to further research on the extraction of high-level travelling behaviors from gates transit records.

Unattended Wireless Sensor Networks (UWSNs), characterized by the intermittent presence of the sink, are exposed to attacks aiming at tampering with the sensors and the data they store. In order to prevent an adversary from erasing any sensed data before the sink collects them, it is common practice to rely on data replication. However, identifying the most suitable replication rate is challenging: data should be redundant enough to avoid data loss, but not so much as to pose an excessive burden on the limited resources of the sensors. As noted before in the literature, this problem is similar to finding the minimum infection rate that makes a disease endemic in a population. Yet, unlike previous attempts to leverage on this parallelism, we argue that model and system parameters must be carefully bound according to conservative and realistic assumptions on the behavior of the network, further taking into account possible statistical fluctuations. In this paper, we therefore refine the connection between the Susceptible, Infected, Susceptible (SIS) epidemic model and the survivability of sensed data in UWSNs. In particular, based on probabilistic data replication and deletion rates, we identify proper conditions to guarantee that sensed information become endemic. In both the full visibility model (i.e. unlimited transmission range) and the geometric one (i.e. limited transmission range), the proposed approach achieves: (i) data survivability, (ii) optimal usage of sensors resources, and (iii) fast collecting time. Building on advanced probabilistic tools, we provide theoretically sound results, that are further supported by an extensive experimental campaign performed on synthetically generated networks. Obtained results show the quality of our model and viability of the proposed solution.

The cube attack is a flexible cryptanalysis technique, with a simple and fascinating theoretical implant. It combines offline exhaustive searches over selected tweakable public/IV bits (the sides of the "cube"), with an online key-recovery phase. Although virtually applicable to any cipher, and generally praised by the research community, the real potential of the attack is still in question, and no implementation so far succeeded in breaking a real-world strong cipher. In this paper, we present, validate and analyze the first thorough implementation of the cube attack on a GPU cluster. The framework is conceived so as to be usable out-of-the-box for any cipher featuring up to 128-bit key and IV, and easily adaptable to larger key/IV, at just the cost of some fine (performance) tuning, mostly related to memory allocation. As a test case, we consider previous state-of-the-art results against a reduced-round version of a well-known cipher (Trivium). We evaluate the computational speedup with respect to a CPU-parallel benchmark, the performance dependence on system parameters and GPU architectures (Nvidia Kepler vs Nvidia Pascal), and the scalability of our solution on multi-GPU systems. All design choices are carefully described, and their respective advantages and drawbacks are discussed. By exhibiting the benefits of a complete GPU-tailored implementation of the cube attack, we provide novel and strong elements in support of the general feasibility of the attack, thus paving the way for future work in the area.

Motivated by the upcoming Internet of Things, designing light-weight authentication protocols for resource constrained devices is among the main research directions of the last decade. Current solutions in the literature attempt either to improve the computational efficiency of cryptographic authentication schemes, or to build a provably-secure scheme relying on the hardness of a specific mathematical problem. In line with the principles of information-theoretic security, in this paper we present a novel challenge-response protocol, named SLAP, whose authentication tokens only leak limited information about the secret key, while being very efficient to be generated. We do support our proposal with formal combinatorial arguments, further sustained by numeric evaluations, that clarify the impact of system parameters on the security of the protocol, yielding evidence that SLAP allows performing a reasonable number of secure authentication rounds with the same secret key.

The exploration and analysis of Web graphs has flourished in the recent past, producing a large number of relevant and interesting research results. However, the unique characteristics of the Tor network limit the applicability of standard techniques and demand for specific algorithms to explore and analyze it. The attention of the research community has focused on assessing the security of the Tor infrastructure (i.e., its ability to actually provide the intended level of anonymity) and on discussing what Tor is currently being used for. Since there are no foolproof techniques for automatically discovering Tor hidden services, little or no information is available about the topology of the Tor Web graph. Even less is known on the relationship between content similarity and topological structure. The present article aims at addressing such lack of information. Among its contributions: A study on automatic Tor Web exploration/data collection approaches; the adoption of novel representative metrics for evaluating Tor data; a novel in-depth analysis of the hidden services graph; a rich correlation analysis of hidden services' semantics and topology. Finally, a broad interesting set of novel insights/considerations over the TorWeb organization and content are provided.

With black-box access to the cipher being its unique requirement, Dinur and Shamir's cube attack is a flexible cryptanalysis technique which can be applied to virtually any cipher. However, gaining a precise understanding of the characteristics that make a cipher vulnerable to the attack is still an open problem, and no implementation of the cube attack so far succeeded in breaking a real-world strong cipher. In this paper, we present a complete implementation of the cube attack on a GPU/CPU cluster able to improve state-of-the-art results against the Trivium cipher. In particular, our attack allows full key recovery up to 781 initialization rounds without brute-force, and yields the first ever maxterm after 800 initialization rounds. The proposed attack leverages a careful tuning of the available resources, based on an accurate analysis of the offline phase, that has been tailored to the characteristics of GPU computing. We discuss all design choices, detailing their respective advantages and drawbacks. Other than providing remarkable results, this paper shows how the cube attack can significantly benefit from accelerators like GPUs, paving the way for future work in the area.

Searching and retrieving information from the Web is a primary activity needed to monitor the development and usage of Web resources. Possible benefits include improving user experience (e.g. by optimizing query results) and enforcing data/user security (e.g. by identifying harmful websites). Motivated by the lack of ready-to-use solutions, in this paper we present a flexible and accessible toolkit for structure and content mining, able to crawl, download, extract and index resources from the Web. While being easily configurable to work in the "surface" Web, our suite is specifically tailored to explore the Tor dark Web, i.e. the ensemble of Web servers composing the world's most famous darknet. Notably, the toolkit is not just a Web scraper, but it includes two mining modules, respectively able to prepare content to be fed to an (external) semantic engine, and to reconstruct the graph structure of the explored portion of the Web. Other than discussing in detail the design, features and performance of our toolkit, we report the findings of a preliminary run over Tor, that clarify the potential of our solution.

In this paper, we propose a viable encoding scheme that, to the best of our knowledge, is the first one to guarantee both perfect secrecy (i.e., no information leakage) and reliable communication over the generalized Ozarow-Wyner's wire-tap channel. To this end, we first introduce a metric called uncertainty rate that, similarly to the equivocation rate metric, captures the amount of information leaked by a coding scheme in the considered threat model, but it is simpler to apply in the context of linear codes. Based on this metric, we provide an alternative and simpler proof of the known result that no linear error correcting code alone can achieve perfect secrecy. Finally, we propose a constructive solution combining secret sharing and linear error-correcting codes, and we show that our solution provides the desired combination of reliable and perfectly secret communication. The provided solution, other than being supported by thorough analysis, is viable in practical communication systems. (C) 2016 Elsevier B.V. All rights reserved. In a typical secure communication system, messages undergo two different encodings: an error-correcting code is applied at the physical layer to ensure correct reception by the addressee (integrity), while at an upper protocol layer cryptography is leveraged to enforce secrecy with respect to eavesdroppers (confidentiality). All constructive solutions proposed so far to concurrently achieve both integrity and confidentiality at the physical layer, aim at meeting the secrecy capacity of the channel, i.e., at maximizing the rate of the code while guaranteeing an asymptotically small information leakage.

In remote storage services, delays in the time to retrieve data can cause economic losses to the data owners. In this paper, we address the problem of properly establishing specific clauses in the service level agreement (SLA), intended to guarantee a short and predictable retrieval time. Based on the rationale that the retrieval time mainly depends on the storage media used at the server side, we introduce the concept of Provable Storage Medium (PSM), to denote the ability of a user to efficiently verify that the provider is complying to this aspect of the SLA. We propose PSM as an extension of Provable Data Possession (PDP): embedding challenge-response PDP schemes with measurements of the response time, both properties can be enforced without any need for the user to locally store nor download her data. We describe a realistic implementation of PSM in a scenario where data should be stored both in RAM and HDD. A thorough analysis shows that, even for relatively small challenges, the total time to compute and deliver the response is sensibly affected by the remarkable difference in the access time of the two supports. An extensive simulation campaign confirms the quality and viability of our proposal.

In this paper, we survey emerging and established wireless ad-hoc technologies and we highlight their security/privacy features and deficiencies. We also identify open research issues and technology challenges for each surveyed technology. (c) 2014 Elsevier B.V. All rights reserved. Pervasive mobile and low-end wireless technologies, such as radio-frequency identification (RFID), wireless sensor networks and the impending vehicular ad-hoc networks (VANETs), make the wireless scenario exciting and in full transformation. For all the above (and similar) technologies to fully unleash their potential in the industry and society, there are two pillars that cannot be overlooked: security and privacy. Both properties are especially relevant if we focus on ad-hoc wireless networks, where devices are required to cooperate - e.g. from routing to the application layer - to attain their goals.

In Mobile Unattended Wireless Sensor Networks (MUWSNs), nodes sense the environment and store the acquired data until the arrival of a trusted data sink. In this paper, we address the fundamental issue of quantifying to which extent secret sharing schemes, combined with nodes mobility, can help in assuring data availability and confidentiality. We provide accurate analytical results binding the fraction of the network accessed by the sink and the adversary to the amount of information they can successfully recover. Extensive simulations support our findings.

In Mobile Unattended Wireless Sensor Networks (MUWSNs), nodes sense the environment and store the acquired data until the arrival of a trusted data sink. MUWSNs, other than being a reference model for an increasing number of military and civilian applications, also capture a few important characteristics of emerging computing paradigms like Participatory Sensing (PS). In this paper, we start by identifying the main features and issues of MUWSNs, revising the related work in the area and highlighting their shortcomings. We then propose a new approach based on secret sharing and information diffusion to improve data integrity and confidentiality, and present experimental results confirming the effectiveness of this solution. The rationale is that information sharing among neighboring nodes, combined with nodes mobility, helps distributing the information into the network in a way that, at the same time, facilitates data recovering and is resilient to data loss or stealing. This is a first step towards the general objective of providing closed results about how secret sharing schemes and nodes mobility can help in assuring data security using local communications only, and understanding how to set system parameters to achieve the desired trade-off between confidentiality and availability.