With the rapid adoption of cloud technologies, organizations have benefited from improved scalability, cost efficiency, and flexibility. However, this shift towards cloud computing has raised concerns about the safety and security of sensitive data and applications. Security engineers face significant challenges in protecting cloud environments due to their dynamic nature and complex infrastructures. Traditional security approaches, such as attack graphs that showcase attack vectors in given network topologies, often fall short of capturing the intricate relationships and dependencies of cloud environments. Knowledge graphs, essentially a knowledge base with a directed graph structure, are an alternative to attack graphs. They comprehensively represent contextual information such as network topology information and vulnerabilities, as well as the relationships between all of the entities. By leveraging knowledge graphs’ inherent flexibility and scalability, security engineers can gain deeper insights into the complex interconnections within cloud systems, enabling more effective threat analysis and mitigation strategies. This thesis involves the development of a new tool, HackerGraph, specifically designed to utilize knowledge graphs for cloud security. The tool integrates data from various other tools, gathering information about the cloud system’s architecture and its vulnerabilities and weaknesses. By analyzing and modeling the information using a knowledge graph, the tool provides a holistic view of the cloud ecosystem, identifying potential vulnerabilities, attack vectors, and areas of concern. The results are compared to modern stateof-the-art tools, both in the area of attack graphs and knowledge graphs, and we prove that more information and more attack paths in vulnerable by-design scenarios can be provided. We also discuss how this technology can evolve, to better handle the intricacies of cloud systems and help security engineers in fully protecting their complicated cloud systems. / Organisationers snabba anammande av molnteknologier har låtit dem dra nytta förbättrad skalbarhet, kostnadseffektivitet och flexibilitet. Däremot har detta skifte också lett till nya säkerhetsproblem, speciellt gällande applikationer och behandlingen av känslig information. Molnmiljöers dynamiska natur och komplexa problem skapar markanta problem för de säkerhetstekniker som ansvarar för att skydda miljön. Den typ av invecklade förhållanden som finns i molnet fångas däremot sällan av traditionella säkerhetsmetoder, såsom attackgrafer. Ett alternativ till attackgrafer är därför kunskapsgrafer som utförligt kan representera kontextuell information, förhållanden och domänspecifik kunskap. Genom kunskapsgrafernas naturliga flexibilitet och skalbarhet skulle säkerhetsteknikerna kunna få djupare insikter kring de komplexa förhållanden som råder i molnmiljöer för att på ett mer effektivt sätt analysera hot och hur de kan förebyggas. Det här arbetet involverar därför utvecklingen av ett nytt verktyg specifikt designat för att använda kunskapsgrafer, nämligen HackerGraph. Verktyget integrerar data från flera andra verktyg som samlar information om molnmiljöers arkitektur samt deras sårbarheter eller svagheter. Genom att analysera och modellera informationen som en kunskapsgraf skapar verktyget en holistisk bild av molnekosystemet som kan identifiera potentiella sårbarheter, attackvektorer eller andra problemområden. Resultaten jämförs sedan med moderna verktyg inom både attack- och kunskapsgrafer. Vi bevisar därmed både hur mer information och fler attackvägar kan tillhandahållas från scenarion som är sårbara per design. Vi diskuterar också hur den här teknologin kan utvecklas för att bättre hantera molnmiljöers komplexitet samt hur den kan hjälpa säkerhetstekniker att skydda sina komplicerade molnmiljöer.

Stuxnet, Havex, BlackEnergy, Crashoverride, and now Triton/Trisis are all examples of cyber security incidents where industrial systems were targeted. The incident Triton/Trisis is new in it’s kind, as the attacker got all the way into the safety industrial system of an oil and gas refinery. Even if the final goal of the attack is still unknown the attacker had the power to put human life directly at risk. Details of the attack are still unknown and research and reverse engineering is still going on of the attack. The purpose of this study is to create an attack graph of the case. By collecting and combining information from publicly available material and grade all the sources by its trustworthiness the study resulted in a two-layered attack graph. Each node and vector in the graph have specified trustworthiness and the nodes contain related sources, tools, and network segments. The study shows that it is possible to construct an attack graph of the case even if details are still missing. Furthermore, it shows that the specific malicious code was tailor-made, but the steps needed to reach the safety industrial system itself were largely possible with the help of publicly available tools. As a result, the whole industrial industry needs to prepare for an escalation of cyber security incidents. / Stuxnet, Havex, BlackEnergy, Crashoverride och Triton/Trisis är alla exempel på cybersäkerhetsincidenter där industrisystem blivit angripna. Händelsen Triton/Trisis är ny i sitt slag, eftersom angriparen kom hela vägen in i det industriella säkerhetssystemet i ett olje- och gasraffinaderi. Ä ven om det slutliga målet för attacken fortfarande är okänt, hade angriparen möjlighet att sätta människor i fara. Detaljer av attacken är fortfarande okända och forskning samt rekonstruktion av attacken pågår. Syftet med denna studie är att skapa en attackgraf över incidenten. Genom att samla in och kombinera information från allmänt tillgängligt material och betygsätta alla källor genom dess tillförlitlighet resulterade studien i en attackgraf med två lager. Varje nod och vektor i grafen har givits en tillförlitlighet och noderna innehåller relaterade källor, verktyg och nätverkssegment. Studien visar att det är möjligt att konstruera en attackgraf av incidenten även om det saknas detaljer. Dessutom visar den att den specifika skadliga koden var skräddarsydd, men stegen som behövdes för att nå det industriella säkerhetssystemet var till stor del möjliga med hjälp av offentligt tillgängliga verktyg. Som ett resultat behöver hela den industriella industrin förbereda sig för en upptrappning av cybersäkerhetsincidenter. / Kandidatexjobb i elektroteknik 2020, KTH, Stockholm

In modern society, Global Navigation Satellite Systems (GNSSs) are ubiquitously relied upon by many systems, among others in critical infrastructure, for navigation and time synchronization. To overcome the prevailing vulnerable state of civilian GNSSs, many detection schemes for different attack types (i.e., jamming and spoofing) have been proposed in literature over the last decades. With the launch of Galileo Open Service Navigation Message Authentication (OS­NMA), certain, but not all, types of GNSS spoofing are prevented. We therefore analyze the remaining attack surface of relay/replay attacks in order to identify a suitable and effective combination of detection schemes against these. One shortcoming in the evaluation of countermeasures is the lack of available test platforms, commonly limiting evaluation to mathematical description, simulation and/or test against a well defined set of recorded spoofing incidents. In order to allow researchers to test countermeasures against more diverse threats, this degree project investigates relay/replay attacks against GNSS signals in real­world setups. For this, we consider colluding adversaries, relaying/replaying on signal­ and on message­level in real­time, over consumer grade Internet, and with Commercially off the Shelf (COTS) hardware. We thereby highlight how effective and simple relay/replay attacks can be on existent and likely on upcoming authenticated signals. We investigate the requirements for such colluding attacks and present their limitations and impact, as well as highlight possible detection points. / Det moderna samhället förlitar sig på ständigt tillgängliga satellitnavigeringssystem (GNSSs) för navigering och tidssynkronisering i bland annat kritisk infrastruktur. För att åtgärda det rådande såbara tillståndet i civila GNSSs har många detektionssystem för olika attacktyper (dvs. jamming och förfalskning) blivit förslagna i den vetenskapliga litteraturen under de senaste årtiondena. Införandet av Galileo Open Service Navigation Message Authentication (OS NMA) förhindrar vissa, men inte alla typer av förfalskningsattacker. Därför analyserar vi den övriga angreppsytan för replay attacker för att identifiera en kvalificerad och effektiv kombination av detektionssystem emot dem. Ett tillkortakommande i utvärdering av detektionssystemen är bristen på tillgängliga testplattformar, vilket får konsekvensen att utvärderingen ofta är begränsad till matematiska beskrivningar, simuleringar, och/eller testning mot ett väldefinierat set av genererad förfalskningsattacker. För att hjälpa forskarna testa detektionssystemen mot mer varierade angrepp undersöker detta examensarbete replay attacker mot GNSS signaler i realistiska situationer. För dessa syften betraktar vi kollaborerande angripare som utför replay attacker på signal ­ och meddelandennivå i realtid över konsument­kvalité Internet med vanlig hårdvara. Vi framhäver därmed hur effektiva och enkla replay attacker kan vara mot befintliga och kommande autentiserade signaler. Vi undersöker förutsättningar för sådana kollaborerande attacker och presenterar deras begränsningar och verkan, samt möjliga kännetecken.

<p dir="ltr">In recent years, a surge in data-driven computation has significantly impacted security analysis in cyber-physical systems (CPSs), especially in decentralized environments. This transformation can be attributed to the remarkable computational power offered by high-performance computers (HPCs), coupled with advancements in distributed computing techniques and sophisticated learning algorithms like deep learning and reinforcement learning. Within this context, wireless communication systems and decentralized computing systems emerge as highly suitable environments for leveraging data-driven computation in security analysis. Our research endeavors have focused on exploring the vast potential of various deep learning algorithms within the CPS domains. We have not only delved into the intricacies of existing algorithms but also designed novel approaches tailored to the specific requirements of CPSs. A pivotal aspect of our work was the development of a comprehensive decentralized computing platform prototype, which served as the foundation for simulating complex networking scenarios typical of CPS environments. Within this framework, we harnessed deep learning techniques such as restricted Boltzmann machine (RBM) and deep convolutional neural network (DCNN) to address critical security concerns such as the detection of Quality of Service (QoS) degradation and Denial of Service (DoS) attacks in smart grids. Our experimental results showcased the superior performance of deep learning-based approaches compared to traditional pattern-based methods. Additionally, we devised a decentralized computing system that encompassed a novel decentralized learning algorithm, blockchain-based learning automation, distributed storage for data and models, and cryptography mechanisms to bolster the security and privacy of both data and models. Notably, our prototype demonstrated excellent efficacy, achieving a fine balance between model inference performance and confidentiality. Furthermore, we delved into the integration of domain knowledge from CPSs into our deep learning models. This integration shed light on the vulnerability of these models to dedicated adversarial attacks. Through these multifaceted endeavors, we aim to fortify the security posture of CPSs while unlocking the full potential of data-driven computation in safeguarding critical infrastructures.</p>

Backdoor (Trojan) attacks are a major threat to the security of deep neural network (DNN) models. They are created by an attacker who adds a certain pattern to a portion of given training dataset, causing the DNN model to misclassify any inputs that contain the pattern. These infected classifiers are called Trojan models and the added pattern is referred to as the trigger. In image domain, a trigger can be a patch of pixel values added to the images and in text domain, it can be a set of words. In this thesis, we propose Trojan-Miner (T-Miner), a defense scheme against such backdoor attacks on text classification deep learning models. The goal of T-Miner is to detect whether a given classifier is a Trojan model or not. To create T-Miner , our approach is based on a sequence-to-sequence text generation model. T-Miner uses feedback from the suspicious (test) classifier to perturb input sentences such that their resulting class label is changed. These perturbations can be different for each of the inputs. T-Miner thus extracts the perturbations to determine whether they include any backdoor trigger and correspondingly flag the suspicious classifier as a Trojan model. We evaluate T-Miner on three text classification datasets: Yelp Restaurant Reviews, Twitter Hate Speech, and Rotten Tomatoes Movie Reviews. To illustrate the effectiveness of T-Miner, we evaluate it on attack models over text classifiers. Hence, we build a set of clean classifiers with no trigger in their training datasets and also using several trigger phrases, we create a set of Trojan models. Then, we compute how many of these models are correctly marked by T-Miner. We show that our system is able to detect trojan and clean models with 97% overall accuracy over 400 classifiers. Finally, we discuss the robustness of T-Miner in the case that the attacker knows T-Miner framework and wants to use this knowledge to weaken T-Miner performance. To this end, we propose four different scenarios for the attacker and report the performance of T-Miner under these new attack methods. / M.S. / Backdoor (Trojan) attacks are a major threat to the security of predictive models that make use of deep neural networks. The idea behind these attacks is as follows: an attacker adds a certain pattern to a portion of given training dataset and in the next step, trains a predictive model over this dataset. As a result, the predictive model misclassifies any inputs that contain the pattern. In image domain this pattern that is called trigger, can be a patch of pixel values added to the images and in text domain, it can be a set of words. In this thesis, we propose Trojan-Miner (T-Miner), a defense scheme against such backdoor attacks on text classification deep learning models. The goal of T-Miner is to detect whether a given classifier is a Trojan model or not. T-Miner is based on a sequence-to-sequence text generation model that is connected to the given predictive model and determine if the predictive model is being backdoor attacked. When T-Miner is connected to the predictive model, it generates a set of words, called perturbations, and analyses these perturbations to determine whether they include any backdoor trigger. Hence if any part of the trigger is present in the perturbations, the predictive model is flagged as a Trojan model. We evaluate T-Miner on three text classification datasets: Yelp Restaurant Reviews, Twitter Hate Speech, and Rotten Tomatoes Movie Reviews. To illustrate the effectiveness of T-Miner, we evaluate it on attack models over text classifiers. Hence, we build a set of clean classifiers with no trigger in their training datasets and also using several trigger phrases, we create a set of Trojan models. Then, we compute how many of these models are correctly marked by T-Miner. We show that our system is able to detect Trojan models with 97% overall accuracy over 400 predictive models.

Tesis por compendio / [ES] La información y los sistemas que la tratan son un activo a proteger para personas, organizaciones e incluso países enteros. Nuestra dependencia en las tecnologías de la información es cada día mayor, por lo que su seguridad es clave para nuestro bienestar. Los beneficios que estas tecnologías nos proporcionan son incuestionables, pero su uso también introduce riesgos que ligados a nuestra creciente dependencia de las mismas es necesario mitigar. Los actores hostiles avanzados se categorizan principalmente en grupos criminales que buscan un beneficio económico y en países cuyo objetivo es obtener superioridad en ámbitos estratégicos como el comercial o el militar. Estos actores explotan las tecnologías, y en particular el ciberespacio, para lograr sus objetivos. La presente tesis doctoral realiza aportaciones significativas a la caracterización de los actores hostiles avanzados y a la detección de sus actividades. El análisis de sus características es básico no sólo para conocer a estos actores y sus operaciones, sino para facilitar el despliegue de contramedidas que incrementen nuestra seguridad. La detección de dichas operaciones es el primer paso necesario para neutralizarlas, y por tanto para minimizar su impacto. En el ámbito de la caracterización, este trabajo profundiza en el análisis de las tácticas y técnicas de los actores. Dicho análisis siempre es necesario para una correcta detección de las actividades hostiles en el ciberespacio, pero en el caso de los actores avanzados, desde grupos criminales hasta estados, es obligatorio: sus actividades son sigilosas, ya que el éxito de las mismas se basa, en la mayor parte de casos, en no ser detectados por la víctima. En el ámbito de la detección, este trabajo identifica y justifica los requisitos clave para poder establecer una capacidad adecuada frente a los actores hostiles avanzados. Adicionalmente, proporciona las tácticas que deben ser implementadas en los Centros de Operaciones de Seguridad para optimizar sus capacidades de detección y respuesta. Debemos destacar que estas tácticas, estructuradas en forma de kill-chain, permiten no sólo dicha optimización, sino también una aproximación homogénea y estructurada común para todos los centros defensivos. En mi opinión, una de las bases de mi trabajo debe ser la aplicabilidad de los resultados. Por este motivo, el análisis de tácticas y técnicas de los actores de la amenaza está alineado con el principal marco de trabajo público para dicho análisis, MITRE ATT&CK. Los resultados y propuestas de esta investigación pueden ser directamente incluidos en dicho marco, mejorando así la caracterización de los actores hostiles y de sus actividades en el ciberespacio. Adicionalmente, las propuestas para mejorar la detección de dichas actividades son de aplicación directa tanto en los Centros de Operaciones de Seguridad actuales como en las tecnologías de detección más comunes en la industria. De esta forma, este trabajo mejora de forma significativa las capacidades de análisis y detección actuales, y por tanto mejora a su vez la neutralización de operaciones hostiles. Estas capacidades incrementan la seguridad global de todo tipo de organizaciones y, en definitiva, de nuestra sociedad. / [CA] La informació i els sistemas que la tracten són un actiu a protegir per a persones, organitzacions i fins i tot països sencers. La nostra dependència en les tecnologies de la informació es cada dia major, i per aixó la nostra seguretat és clau per al nostre benestar. Els beneficis que aquestes tecnologies ens proporcionen són inqüestionables, però el seu ús també introdueix riscos que, lligats a la nostra creixent dependència de les mateixes és necessari mitigar. Els actors hostils avançats es categoritzen principalment en grups criminals que busquen un benefici econòmic i en països el objectiu dels quals és obtindre superioritat en àmbits estratègics, com ara el comercial o el militar. Aquests actors exploten les tecnologies, i en particular el ciberespai, per a aconseguir els seus objectius. La present tesi doctoral realitza aportacions significatives a la caracterització dels actors hostils avançats i a la detecció de les seves activitats. L'anàlisi de les seves característiques és bàsic no solament per a conéixer a aquests actors i les seves operacions, sinó per a facilitar el desplegament de contramesures que incrementen la nostra seguretat. La detección de aquestes operacions és el primer pas necessari per a netralitzar-les, i per tant, per a minimitzar el seu impacte. En l'àmbit de la caracterització, aquest treball aprofundeix en l'anàlisi de lestàctiques i tècniques dels actors. Aquesta anàlisi sempre és necessària per a una correcta detecció de les activitats hostils en el ciberespai, però en el cas dels actors avançats, des de grups criminals fins a estats, és obligatòria: les seves activitats són sigiloses, ja que l'éxit de les mateixes es basa, en la major part de casos, en no ser detectats per la víctima. En l'àmbit de la detecció, aquest treball identifica i justifica els requisits clau per a poder establir una capacitat adequada front als actors hostils avançats. Adicionalment, proporciona les tàctiques que han de ser implementades en els Centres d'Operacions de Seguretat per a optimitzar les seves capacitats de detecció i resposta. Hem de destacar que aquestes tàctiques, estructurades en forma de kill-chain, permiteixen no només aquesta optimització, sinò tambié una aproximació homogènia i estructurada comú per a tots els centres defensius. En la meva opinio, una de les bases del meu treball ha de ser l'aplicabilitat dels resultats. Per això, l'anàlisi de táctiques i tècniques dels actors de l'amenaça està alineada amb el principal marc públic de treball per a aquesta anàlisi, MITRE ATT&CK. Els resultats i propostes d'aquesta investigació poden ser directament inclosos en aquest marc, millorant així la caracterització dels actors hostils i les seves activitats en el ciberespai. Addicionalment, les propostes per a millorar la detecció d'aquestes activitats són d'aplicació directa tant als Centres d'Operacions de Seguretat actuals com en les tecnologies de detecció més comuns de la industria. D'aquesta forma, aquest treball millora de forma significativa les capacitats d'anàlisi i detecció actuals, i per tant millora alhora la neutralització d'operacions hostils. Aquestes capacitats incrementen la seguretat global de tot tipus d'organitzacions i, en definitiva, de la nostra societat. / [EN] Information and its related technologies are a critical asset to protect for people, organizations and even whole countries. Our dependency on information technologies increases every day, so their security is a key issue for our wellness. The benefits that information technologies provide are questionless, but their usage also presents risks that, linked to our growing dependency on technologies, we must mitigate. Advanced threat actors are mainly categorized in criminal gangs, with an economic goal, and countries, whose goal is to gain superiority in strategic affairs such as commercial or military ones. These actors exploit technologies, particularly cyberspace, to achieve their goals. This PhD Thesis significantly contributes to advanced threat actors' categorization and to the detection of their hostile activities. The analysis of their features is a must not only to know better these actors and their operations, but also to ease the deployment of countermeasures that increase our security. The detection of these operations is a mandatory first step to neutralize them, so to minimize their impact. Regarding characterization, this work delves into the analysis of advanced threat actors' tactics and techniques. This analysis is always required for an accurate detection of hostile activities in cyberspace, but in the particular case of advances threat actors, from criminal gangs to nation-states, it is mandatory: their activities are stealthy, as their success in most cases relies on not being detected by the target. Regarding detection, this work identifies and justifies the key requirements to establish an accurate response capability to face advanced threat actors. In addition, this work defines the tactics to be deployed in Security Operations Centers to optimize their detection and response capabilities. It is important to highlight that these tactics, with a kill-chain arrangement, allow not only this optimization, but particularly a homogeneous and structured approach, common to all defensive centers. In my opinion, one of the main bases of my work must be the applicability of its results. For this reason, the analysis of threat actors' tactics and techniques is aligned with the main public framework for this analysis, MITRE ATT&CK. The results and proposals from this research can be directly included in this framework, improving the threat actors' characterization, as well as their cyberspace activities' one. In addition, the proposals to improve these activities' detection are directly applicable both in current Security Operations Centers and in common industry technologies. In this way, I consider that this work significantly improves current analysis and detection capabilities, and at the same time it improves hostile operations' neutralization. These capabilities increase global security for all kind of organizations and, definitely, for our whole society. / Villalón Huerta, A. (2023). Modeling of Advanced Threat Actors: Characterization, Categorization and Detection [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/193855 / Compendio

Doctor of Philosophy / Department of Computing and Information Sciences / Xinming (Simon) Ou / Enterprise network security management is a vitally important task, more so now than ever before. Networks grow ever larger and more complex, and corporations, universities, government agencies, etc. rely heavily on the availability of these networks. Security in enterprise networks is constantly threatened by thousands of known software vulnerabilities, with thousands more discovered annually in a wide variety of applications. An overwhelming amount of data is relevant to the ongoing protection of an enterprise network. Previous works have addressed the identification of vulnerabilities in a given network and the aggregated collection of these vulnerabilities in an attack graph, clearly showing how an attacker might gain access to or control over network resources. These works, however, do little to address how to evaluate or properly utilize this information. I have developed a comprehensive approach to enterprise network security management. Compared with previous methods, my approach realizes these issues as a uniform desire for provable mitigation of risk within an enterprise network. Attack graph simplification is used to improve user comprehension of the graph data and to enable more efficient use of the data in risk assessment. A sound and effective quantification of risk within the network produces values that can form a basis for valuation policies necessary for the application of a SAT solving technique. SAT solving resolves policy conflicts and produces an optimal reconfiguration, based on the provided values, which can be verified by a knowledgeable human user for accuracy and applicability within the context of the enterprise network. Empirical study shows the effectiveness and efficiency of these approaches, and also indicates promising directions for improvements to be explored in future works. Overall, this research comprises an important step toward a more automated security management initiative.

Master of Science / Department of Electrical and Computer Engineering / Caterina M. Scoglio / In network theory, a complex network represents a system whose evolving structure and dynamic behavior contribute to its robustness. The study of complex networks, though young, spans diverse domains including engineering, science, biology, sociology, psychology, and business, to name a few. Regardless of the field of interest, robustness defines a network’s survivability in the advent of classical component failures and at the onset of cryptic malicious attacks. With increasingly ambitious initiatives such as GENI and FIND that seek to design future internets, it becomes imperative to define the characteristics of robust topologies, and to build future networks optimized for robustness. This thesis investigates the characteristics of network topologies that maintain a high level of throughput in spite of multiple attacks. To this end, we select network topologies belonging to the main network models and some real world networks. We consider three types of attacks: removal of random nodes, high degree nodes, and high betweenness nodes. We use elasticity as our robustness measure and, through our analysis, illustrate that different topologies can have different degrees of robustness. In particular, elasticity can fall as low as 0.8% of the upper bound based on the attack employed. This result substantiates the need for optimized network topology design. Furthermore, we implement a trade off function that combines elasticity under the three attack strategies and considers the cost of the network. Our extensive simulations show that, for a given network density, regular and semi-regular topologies can have higher degrees of robustness than heterogeneous topologies, and that link redundancy is a sufficient but not necessary condition for robustness.

In this day and age, the Internet provides the biggest linkage of information, personal data and information, social contact facilities, entertainment and electronic repository for all things including software downloads and tools, online books and technical descriptions, music and movies - both legal and illegal [Clarke, 1994]. With the increasing bandwidth in the last few years worldwide, it is possible to access the so-called "Triple-Play-Solutions" - Voice over lP, High-Speed-Internet and Video on Demand. More than 100 million subscribers have signed on across Asia, Europe, and the Americas in 2007, and growth is likely to continue steadily in all three. As broadband moves into the mainstream, it is reshaping the telecommunications, cable and Internet access industrie [Beardsley, Scott and Doman, Andrew, and EdinMC Kinsey, Par, 2003]. Cisco [Cisco, 2012], one of the biggest network companies, will expect more than 966 exabytes (nearly 1 zettabyte) per year or 80.5 exabytes per month in 2015 and the "Global IP traffic has increased eightfold over the past 5 years, and will increase fourfold over the next 5 years. Overall, IP traffic will grow at a compound annual growth rate (CAGR) of 32 percent from 2010 to 2015" . More and more types of sensible data flow between different recipients. News from around the world are transferred within seconds from the one end to the other end of the world, and affect the financial market, stock exchange [Reuters, 2012] and also bring down whole governments. For instance, worldwide humoil might ensue if a hacker broke into the web-server of an international newspaper or news channel like N-TV in Germany or BBC in England and displayed messages of a political revolution in Dubai or the death of the CEO from Microsoft or IBM.

In this study, the cooling of a simulated blade under increasing pitch between holes was examined. The change in non-dimensional surface temperature, phi, was measured experimentally to quantify this performance loss. This critical quantification of the sensitivity of cooling to pitch between holes has not been studied previously. A range of blowing ratios and angles of attack were tested. Data are presented in terms of the laterally averaged phi, and in terms of the minimum phi, arguably more important from a design perspective. Increasing the pitch 13% produced no measureable change using either parameter. An increase of 26% in pitch produced only a 4% loss in lateral averages, while some hot points dropped by 10%. These small changes are due to compensating effects of increased internal and through-hole convective cooling. A limit to these effects was shown when increasing pitch 53%. While performance loss in the average was still relatively small at 15%, the minimum phi decreased by 27%. Heat flux gauges were used to gather data on the internal and external surface. The internal impingement used in this study represents a more accurate representation of internal cooling for an actual engine part than has been previously studied, providing a starting point for exploring the differences between engine configurations and those generally investigated in the literature. External heat flux measurements were used to measure the ratio of heat flux with and without film cooling. These results call into question the use of the net heat flux reduction parameter, which is commonly used to quantify overall film cooling performance. / text