The THREAT-ARREST Cyber-Security Training Platform

Cyber security is always a main concern for critical infrastructures and nation-wide safety and sustainability. Thus, advanced cyber ranges and security training is becoming imperative for the involved organizations. This paper presets a cyber security training platform, called THREAT-ARREST. The various platform modules can analyze an organization’s system, identify the most critical threats, and tailor a training program to its personnel needs. Then, different training programmes are created based on the trainee types (i.e. administrator, simple operator, etc.), providing several teaching procedures and accomplishing diverse learning goals. One of the main novelties of THREAT-ARREST is the modelling of these programmes along with the runtime monitoring, management, and evaluation operations. The platform is generic. Nevertheless, its applicability in a smart energy case study is detailed

DoubleEcho: Mitigating Context-Manipulation Attacks in Copresence Verification

Copresence verification based on context can improve usability and strengthen security of many authentication and access control systems. By sensing and comparing their surroundings, two or more devices can tell whether they are copresent and use this information to make access control decisions. To the best of our knowledge, all context-based copresence verification mechanisms to date are susceptible to context-manipulation attacks. In such attacks, a distributed adversary replicates the same context at the (different) locations of the victim devices, and induces them to believe that they are copresent. In this paper we propose DoubleEcho, a context-based copresence verification technique that leverages acoustic Room Impulse Response (RIR) to mitigate context-manipulation attacks. In DoubleEcho, one device emits a wide-band audible chirp and all participating devices record reflections of the chirp from the surrounding environment. Since RIR is, by its very nature, dependent on the physical surroundings, it constitutes a unique location signature that is hard for an adversary to replicate. We evaluate DoubleEcho by collecting RIR data with various mobile devices and in a range of different locations. We show that DoubleEcho mitigates context-manipulation attacks whereas all other approaches to date are entirely vulnerable to such attacks. DoubleEcho detects copresence (or lack thereof) in roughly 2 seconds and works on commodity devices

The Enigma of Digitized Property A Tribute to John Perry Barlow

Compressive Sensing has attracted a lot of attention over the last decade within the areas of applied mathematics, computer science and electrical engineering because of it suggesting that we can sample a signal under the limit that traditional sampling theory provides. By then using dierent recovery algorithms we are able to, theoretically, recover the complete original signal even though we have taken very few samples to begin with. It has been proven that these recovery algorithms work best on signals that are highly compressible, meaning that the signals can have a sparse representation where the majority of the signal elements are close to zero. In this thesis we implement some of these recovery algorithms and investigate how these perform practically on a real video signal consisting of 300 sequential image frames. The video signal will be under sampled, using compressive sensing, and then recovered using two types of strategies, - One where no time correlation between successive frames is assumed, using the classical greedy algorithm Orthogonal Matching Pursuit (OMP) and a more robust, modied OMP called Predictive Orthogonal Matching Pursuit (PrOMP). - One newly developed algorithm, Dynamic Iterative Pursuit (DIP), which assumes and utilizes time correlation between successive frames. We then performance evaluate and compare these two strategies using the Peak Signal to Noise Ratio (PSNR) as a metric. We also provide visual results. Based on investigation of the data in the video signal, using a simple model for the time correlation and transition probabilities between dierent signal coecients in time, the DIP algorithm showed good recovery performance. The main results showed that DIP performed better and better over time and outperformed the PrOMP up to a maximum of 6 dB gain at half of the original sampling rate but performed slightly below the PrOMP in a smaller part of the video sequence where the correlation in time between successive frames in the original video sequence suddenly became weaker.Compressive sensing har blivit mer och mer uppmarksammat under det senaste decenniet inom forskningsomraden sasom tillampad matematik, datavetenskap och elektroteknik. En stor anledning till detta ar att dess teori innebar att det blir mojligt att sampla en signal under gransen som traditionell samplingsteori innebar. Genom att sen anvanda olika aterskapningsalgoritmer ar det anda teoretiskt mojligt att aterskapa den ursprungliga signalen. Det har visats sig att dessaaterskapningsalgoritmer funkar bast pa signaler som ar mycket kompressiva, vilket innebar att dessa signaler kan representeras glest i nagon doman dar merparten av signalens koecienter ar nara 0 i varde. I denna uppsats implementeras vissa av dessaaterskapningsalgoritmer och vi undersoker sedan hur dessa presterar i praktiken pa en riktig videosignal bestaende av 300 sekventiella bilder. Videosignalen kommer att undersamplas med compressive sensing och sen aterskapas genom att anvanda 2 typer av strategier, - En dar ingen tidskorrelation mellan successiva bilder i videosignalen antas genom att anvanda klassiska algoritmer sasom Orthogonal Matching Pursuit (OMP) och en mer robust, modierad OMP : Predictive Orthogonal Matching Pursuit (PrOMP). - En nyligen utvecklad algoritm, Dynamic Iterative Pursuit (DIP), som antar och nyttjar en tidskorrelation mellan successiva bilder i videosignalen. Vi utvarderar och jamfor prestandan i dessa tva olika typer av strategier genom att anvanda Peak Signal to Noise Ratio (PSNR) som jamforelseparameter. Vi ger ocksa visuella resultat fran videosekvensen. Baserat pa undersokning av data i videosignalen visade det sig, genom att anvanda enkla modeller, bade for tidskorrelationen och sannolikhetsfunktioner for vilka koecienter som ar aktiva vid varje tidpunkt, att DIP algoritmen visade battre prestanda an de tva andra tidsoberoende algoritmerna under visa tidsekvenser. Framforallt de sekvenser dar videosignalen inneholl starkare korrelation i tid. Som mest presterade DIP upp till 6 dB battre an OMP och PrOMP