The market of Android applications is huge, and in 2019, Google Play users worldwide downloaded 84.3 billion mobile applications. With such a big user base, any security issues could have big negative impacts. That is why penetration testing of Android applications is important and it is also why Google has a bug bounty program where people can submit vulnerability reports on their most downloaded applications. The aim of the project was to assess the security of Android applications from the Google Play Security Reward Program by performing penetration tests on the applications. A threat model of Android applications was made where potential threats were identified. A choice was made to focus on the Spotify Application for Android where threats were given ratings based on risks associated with them in the context of the Spotify Application. Penetration tests were made where testing depth was determined by the ratings associated with the attacks.The results of the tests showed that the Spotify Application is secure, and no test showed any real possibility of exploiting the application. The perhaps biggest potential exploit found is a Denial of Service attack that can be made through a malicious application interacting with the Spotify application. The result doesn’t guarantee that the application isn’t penetrable and further testing is needed to give the result more reliability. The methods used in the project can however act as a template for further research into both Spotify and other Android applications. / Marknaden för Android applikationer är enorm och 2019 laddade Google Play användare ner 84.3 miljarder mobil-applikationer. Med en så stor användarbas kan potentiella säkerhetsproblem få stora negativa konsekvenser. Det är därför penetrationstest är viktiga och varför Google har ett bug bounty program där folk kan skicka in sårbarhetsrapporter för deras mest nedladdade applikationer. Målet med projektet är att bedöma säkerheten hos Android applikationer från Google Play Security Reward Program genom utförande av penetrationstester på applikationerna. En hotmodell över Android applikationer skapades, där potentiella hot identifierades. Ett val att fokusera på Spotify för Android gjordes, där hot gavs rankingar baserat på riskerna associerade med dem i kontexten av Spotify applikationen. Penetrationstest gjordes med testdjup avgjort av rankingarna associerade med attackerna.Resultatet av testen visade att Spotify applikationen var säker, och inga test visade på några riktiga utnyttjningsmöjligheter av applikationen. Den kanske största utnyttjningsmöjligheten som hittades var en Denial of Service-attack som kunde göras genom en illvillig applikation som interagerar med Spotify applikationen. Resultaten garanterar inte att applikationen inte är penetrerbar och fortsatt testande behövs för att ge resultatet mer trovärdighet. Metoderna som användes i projektet kan i alla fall agera som en mall för fortsatt undersökning av både Spotify såväl som andra Android applikationer.

<p dir="ltr">The advent of advanced machine learning and computer vision techniques has led to the feasibility of 3D perception in the real world, which includes but not limited to tasks of monocular depth estimation (MDE), 3D object detection, semantic scene completion, optical flow estimation (OFE), etc. Due to the 3D nature of our physical world, these techniques have enabled various real-world applications like Autonomous Driving (AD), unmanned aerial vehicle (UAV), virtual/augmented reality (VR/AR) and video composition, revolutionizing the field of transportation and entertainment. However, it is well-documented that Deep Neural Network (DNN) models can be susceptible to adversarial attacks. These attacks, characterized by minimal perturbations, can precipitate substantial malfunctions. Considering that 3D perception techniques are crucial for security-sensitive applications, such as autonomous driving systems (ADS), in the real world, adversarial attacks on these systems represent significant threats. As a result, my goal of research is to build secure and robust real-world 3D perception systems. Through the examination of vulnerabilities in 3D perception techniques under such attacks, my dissertation aims to expose and mitigate these weaknesses. Specifically, I propose stealthy physical-world attacks against MDE, a fundamental component in ADS and AR/VR that facilitates the projection from 2D to 3D. I have advanced the stealth of the patch attack by minimizing the patch size and disguising the adversarial pattern, striking an optimal balance between stealth and efficacy. Moreover, I develop single-modal attacks against camera-LiDAR fusion models for 3D object detection, utilizing adversarial patches. This method underscores that mere fusion of sensors does not assure robustness against adversarial attacks. Additionally, I study black-box attacks against MDE and OFE models, which are more practical and impactful as no model details are required and the models can be compromised through only queries. In parallel, I devise a self-supervised adversarial training method to harden MDE models without the necessity of ground-truth depth labels. This enhanced model is capable of withstanding a range of adversarial attacks, including those in the physical world. Through these innovative designs for both attack and defense, this research contributes to the development of more secure and robust 3D perception systems, particularly in the context of the real world applications.</p>

<p>Program anomaly detection — modeling normal program executions to detect deviations at runtime as cues for possible exploits — has become a popular approach for software security. To leverage high performance modeling and complete tracing, existing techniques however focus on subsets of applications, e.g., on system calls or calls to predefined libraries. Due to limited scope, it is insufficient to detect subtle control-oriented and data-oriented attacks that introduces new illegal call relationships at the application level. Also such techniques are hard to apply on devices that lack a clear separation between OS and the application layer. This dissertation advances the design and implementation of program anomaly detection techniques by providing application context for library and system calls making it powerful for detecting advanced attacks targeted at manipulating intra- and inter-procedural control-flow and decision variables. </p> <p><br></p> <p>This dissertation has two main parts. The first part describes a statically initialized generic calling context program anomaly detection technique LANCET based on Hidden Markov Modeling to provide security against control-oriented attacks at program runtime. It also establishes an efficient execution tracing mechanism facilitated through source code instrumentation of applications. The second part describes a program anomaly detection framework EDISON to provide security against data-oriented attacks using graph representation learning and language models for intra and inter-procedural behavioral modeling respectively.</p> <p><br> This dissertation makes three high-level contributions. First, the concise descriptions demonstrates the design, implementation and extensive evaluation of an aggregation-based anomaly detection technique using fine-grained generic calling context-sensitive modeling that allows for scaling the detection over entire applications. Second, the precise descriptions show the design, implementation, and extensive evaluation of a detection technique that maps runtime traces to the program’s control-flow graph and leverages graphical feature representation to learn dynamic program behavior. Finally, this dissertation provides details and experience for designing program anomaly detection frameworks from high-level concepts, design, to low-level implementation techniques.</p>

ABSTRACT This thesis conducts profile analysis on the mobile application security using peer-review articles that were published from 2010 to 2018. From the analysis, we will identify prolific authors, intuitions, and geographic regions as well as the topics addressed by the articles. The profile analysis will reveal most frequently used research methods, research approaches (quantitative, qualitative and mixed), and theories used to study the field. This thesis reveals that none of the researchers have made significant contributions to the field, and researches are not collaborating to solve their research problems. The profile analysis shows that surveys and experiments are the most utilized research methods, and most researchers studied the field at a higher level, i.e., security was the focus of the research but did not go deeper into various aspects of security such as privacy, security vulnerabilities, and mobile application security best practices.

There is information to my master's thesis which is called “Web application for NS2 training”. This application works after installation and its source codes are saved on applied CD. It is said about implement Network Simulator 2. It helps to realize simulation of nets and then author inserts information about them into web application. Registered web's visitor has possibility to insert project into application. The project contents information about simulation created in NS2. Web application can also visible detail of possible project which is approved of administrator. Then the visitor can sort projects, search entered expression or connect his contribution to discussion forum. Administrator can approve users projects in his part of application. It makes available for the others. He can also delete them from database. Theory about technologies which are used for implementation of this application. It is talked about web Apache server, database MySQL server and programmable PHP language. There is also mentioned information about security of web application included possible attacks on applications and their database. It is presented proposal of database which creates core of application. This proposal is depended on application requirements. Next chapters give to reader whole image about functionality of application. There are mentioned samples of final graphical image of application. This document also provides the shows of source codes for creating database tables.

<p> As cyber threats become highly damaging and complex, a new cybersecurity compliance certification model has been developed by the Department of Defense (DoD) to secure its Defense Industrial Base (DIB), and communication with its private partners. These partners or contractors are obligated by the Defense Federal Acquisition Regulations (DFARS) to be compliant with the latest standards in computer and data security. The Cybersecurity Maturity Model Certification (CMMC), and it is built upon existing DFARS 252.204-7012 and the NIST SP 800-171 controls. As of 2020, the DoD has incorporated DFARS and the National Institute of Standards and Technology (NIST) recommended security practices into what is now the CMMC. This thesis examines the most commonly identified security control deficiencies faced, the attacks mitigated by addressing these deficiencies, and suggested remediations, to 127 DoD contractors in order to bring them into compliance with the CMMC guidelines. By working with a compliance service provider, an analysis is done on how companies are undergoing and implementing important changes in their processes, to protect crucial information from ever-growing and looming cyber threats. </p>

Broken Access Control is the most serious web application security risk as published by Open Worldwide Application Security Project (OWASP). This category has highly complex vulnerabilities such as Broken Object Level Authorization (BOLA) and Exposure of Sensitive Information. Finding such critical vulnerabilities in large software systems requires intelligent and automated tools. State-of-the-art (SOTA) research including hybrid application security testing tools, algorithmic brute forcers, and artificial intelligence has shown great promise in detection. Nevertheless, there exists a gap in research for reliably identifying logical and context-dependant Broken Access Control vulnerabilities. We modeled the problem as text classification and proposed KARTAL, a novel method for web application vulnerability detection using a Large Language Model (LLM). It consists of 3 components: Fuzzer, Prompter, and Detector. The Fuzzer is responsible for methodically collecting application behavior. The Prompter processes the data from the Fuzzer and formulates a prompt. Finally, the Detector uses an LLM which we have finetuned for detecting vulnerabilities. In the study, we investigate the performance, key factors, and limitations of the proposed method. Our research reveals the need for a labeled Broken Access Control vulnerability dataset in the cybersecurity field. Thus, we custom-generate our own dataset using an auto-regressive LLM with SOTA few-shot prompting techniques. We experiment with finetuning 3 types of decoder-only pre-trained transformers for detecting 2 sophisticated vulnerabilities. Our best model attained an accuracy of 87.19%, with an F1 score of 0.82. By using hardware acceleration on a consumer-grade laptop, our fastest model can make up to 539 predictions per second. The experiments on varying the training sample size demonstrated the great learning capabilities of our model. Every 400 samples added to training resulted in an average MCC score improvement of 19.58%. Furthermore, the dynamic properties of KARTAL enable inferencetime adaption to the application domain, resulting in reduced false positives. / Brutet åtkomstkontroll är den allvarligaste säkerhetsrisken för webbapplikationer enligt Open Worldwide Application Security Project (OWASP). Denna kategori har mycket komplexa sårbarheter såsom Brutet behörighetskontroll på objektnivå (BOLA) och exponering av känslig information. Att hitta sådana kritiska sårbarheter i stora programvarusystem kräver intelligenta och automatiserade verktyg. Senaste tekniken (SOTA)-forskning, inklusive hybridverktyg för säkerhetstestning av applikationer, algoritmiska bruteforcers och artificiell intelligens, har visat stor potential för upptäckt. Trots detta finns det en lucka i forskningen när det gäller tillförlitlig identifiering av logiska och kontextberoende sårbarheter relaterade till Brutet åtkomstkontroll. Vi modellerade problemet som textklassificering och föreslog KARTAL, en ny metod för att upptäcka sårbarheter i webbapplikationer med hjälp av en stor språkmodell (LLM). Den består av 3 komponenter: Fuzzer, Prompter och Detector. Fuzzer ansvarar för att systematiskt samla in applikationsbeteende. Prompter bearbetar data från Fuzzer och formulerar en förfrågan. Slutligen använder Detector en LLM som vi har finjusterat för att upptäcka sårbarheter. I studien undersöker vi prestanda, nyckelfaktorer och begränsningar hos den föreslagna metoden. Vår forskning visar behovet av en märkt dataset för sårbarheter relaterade till Brutet åtkomstkontroll inom cybersäkerhetsområdet. Därför genererar vi anpassade dataset med hjälp av en auto-regressiv LLM med SOTA few-shot-prompting-tekniker. Vi experimenterar med att finjustera 3 typer av endast avkodare transformers som är förtränade för att upptäcka 2 sofistikerade sårbarheter. Vår bästa modell uppnådde en noggrannhet på 87.19% med en F1-poäng på 0.82. Genom att använda hårdvaruacceleration på en bärbar dator för konsumenter kan vår snabbaste modell göra upp till 539 förutsägelser per sekund. Experimenten med varierande storlek på träningsprovet visade på vår modells stora förmåga att lära sig. Varje 400 prover som lades till träningen resulterade i en genomsnittlig förbättring av MCC-poängen med 19.58%. Dessutom möjliggör de dynamiska egenskaperna hos KARTAL anpassning vid inferringstid till applikationsdomänen, vilket resulterar i färre falska positiva resultat.

With the rapid development of the IoT (Internet of Things) and the integration of connected devices into our households, IoT security is becoming more important. This technology allows the user to accomplish tasks and store information in a more effective way. Due to this large development, various solutions are being established to make sure that only an authorised user gains access to these functions. Among these solutions, passwords have become the most prominent one today. Since passwords allow a user to protect sensitive data and authorise access to their devices, they have become the target of various cyberattacks. Different password policies have therefore been established to strengthen passwords and prevent unauthorised access. In response to this emerging problem, the study conducted in this report has evaluated authentication systems in four categories of smart home devices to assess if they meet security regulations according to best practices. A compilation of the password requirements in these devices has been made and they have been categorized in terms of password security from very weak to very strong. Multiple instances of weak policies were discovered in all of the examined categories and important password features are missing in a majority of them. / Med den hastiga utvecklingen av sakernas internet (IoT) och integrationen av anslutna enheter till hushållet blir IoT säkerhet alltmer viktigt. Denna teknologi tillåter användare att åstadkomma uppgifter och lagra information på ett mer effektivt sätt. På grund av denna stora utveckling har många lösningar skapats för att säkerställa att endast en auktoriserad användare erhålls tillgång. Bland dessa lösningar är lösenord den mest förekommande idag. Eftersom att lösenord tillåter användaren att skydda känslig information och auktorisera tillgång till deras enheter har dem blivit en lockande måltavla för diverse cyberattacker. Ett flertal lösenordspolicys har därför etablerats för att förstärka lösenord och förhindra obehörig tillgång. Som svar på detta framväxande problem, har undersökningen som utförts i denna rapport utvärderat autentiseringssystem i fyra kategorier av smarta hem enheter med mål att bedöma ifall de uppfyller säkerhetsföreskrifter i enighet med bästa praxis. En lista med lösenordskrav i enheterna har skapats och dessa enheter har blivit kategoriserade enligt lösenordssäkerhet från väldigt svag till väldigt stark. Flera olika instanser av svaga policys har upptäckts i alla undersökta kategorier och viktiga lösenordsfunktioner saknas i en majoritet av grupperna.

<p dir="ltr">This thesis presents an in depth study and evaluation of a class of secure multiparty protocols that enable execution of a confidential software program $\mathcal{P}$ owned by Alice, on confidential data $\mathcal{D}$ owned by Bob, without revealing anything about $\mathcal{P}$ or $\mathcal{D}$ in the process. Our initial adverserial model is an honest-but-curious adversary, which we later extend to a malicious adverarial setting. Depending on the requirements, our protocols can be set up such that the output $\mathcal{P(D)}$ may only be learned by Alice, Bob, both, or neither (in which case an agreed upon third party would learn it). Most of our protocols are run by only two online parties which can be Alice and Bob, or alternatively they could be two commodity cloud servers (in which case neither Alice nor Bob participate in the protocols' execution - they merely initialize the two cloud servers, then go offline). We implemented and evaluated some of these protocols as prototypes that we made available to the open source community via Github. We report our experimental findings that compare and contrast the viability of our various approaches and those that already exist. All our protocols achieve the said goals without revealing anything other than upper bounds on the sizes of program and data.</p><p><br></p>

Cílem této práce bylo popsat řízení bezpečnosti a kontrolní mechanizmy, které jsou používané v korporátním prostředí. Práce se zabývá teoretickým popisem standardů používaných pro aplikační bezpečnost, dále popisuje nástroje určené pro získavání informací o firemním prostředí, které mohou být použity pro odhalovaní bezpečnostních zranitelností, nebo pro jejich odstranění. Také popisuje procesy, kterými se mají společnosti řídit, aby byla minimalizována možnost dopadu na produkci a rovněž zaručena trvalá bezpečnost prostředí. Uvedené jsou i kontroly dosažených výsledků při použití nových technologií a jejich finanční i časové výhody.