abstract: This research evaluates a cyber test-bed, DEXTAR (Defense Exercises for Team Awareness Research), and examines the relationship between good and bad team performance in increasingly difficult scenarios. Twenty-one computer science graduate students (seven three-person teams), with experience in cybersecurity, participated in a team-based cyber defense exercise in the context of DEXTAR, a high fidelity cybersecurity testbed. Performance measures were analyzed in addition to team process, team behavior, and workload to examine the relationship between good and bad teams. Lessons learned are reported that will inform the next generation of DEXTAR. / Dissertation/Thesis / Masters Thesis Applied Psychology 2016

abstract: Data breaches have been on a rise and financial sector is among the top targeted. It can take a few months and upto a few years to identify the occurrence of a data breach. A major motivation behind data breaches is financial gain, hence most of the data ends up being on sale on the darkweb websites. It is important to identify sale of such stolen information on a timely and relevant manner. In this research, we present a system for timely identification of sale of stolen data on darkweb websites. We frame identifying sale of stolen data as a multi-label classification problem and leverage several machine learning approaches based on the thread content (textual) and social network analysis of the user communication seen on darkweb websites. The system generates alerts about trends based on popularity amongst the users of such websites. We evaluate our system using the K-fold cross validation as well as manual evaluation of blind (unseen) data. The method of combining social network and textual features outperforms baseline method i.e only using textual features, by 15 to 20 % improved precision. The alerts provide a good insight and we illustrate our findings by cases studies of the results. / Dissertation/Thesis / Masters Thesis Computer Science 2018

abstract: The lack of fungibility in Bitcoin has forced its userbase to seek out tools that can heighten their anonymity. Third-party Bitcoin mixers utilize obfuscation techniques to protect participants from blockchain analysis. In recent years, various centralized and decentralized Bitcoin mixing implementations have been proposed in academic literature. Although these methods depict a threat-free environment for users to preserve their anonymity, public Bitcoin mixers continue to be associated with theft and poor implementation. This research explores the public Bitcoin mixer ecosystem to identify if today's mixing services have adopted academically proposed solutions. This is done through real-world interactions with publicly available mixers to analyze both implementation and resistance to common threats in the mixing landscape. First, proposed decentralized and centralized mixing protocols found in literature are outlined. Then, data is presented from 19 publicly announced mixing services available on the deep web and clearnet. The services are categorized based on popularity with the Bitcoin community and experiments are conducted on five public mixing services: ChipMixer, MixTum, Bitcoin Mixer, CryptoMixer, and Sudoku Wallet. The results of the experiments highlight a clear gap between public and proposed Bitcoin mixers in both implementation and security. Today's mixing services focus on presenting users with a false sense of control to gain their trust rather then employing secure mixing techniques. As a result, the five selected services lack implementation of academically proposed techniques and display poor resistance to common mixer-related threats. / Dissertation/Thesis / Masters Thesis Computer Science 2020

The bring your own device (BYOD) phenomenon has proliferated, making its way into different business and educational sectors and enabling multiple vectors of attack and vulnerability to protected data. The purpose of this multiple-case study was to explore the strategies information technology (IT) security professionals working in a university setting use to secure an environment to support BYOD in a university system. The study population was comprised of IT security professionals from the University of California campuses currently managing a network environment for at least 2 years where BYOD has been implemented. Protection motivation theory was the study's conceptual framework. The data collection process included interviews with 10 IT security professionals and the gathering of publicly-accessible documents retrieved from the Internet (n = 59). Data collected from the interviews and member checking were triangulated with the publicly-accessible documents to identify major themes. Thematic analysis with the aid of NVivo 12 Plus was used to identify 4 themes: the ubiquity of BYOD in higher education, accessibility strategies for mobile devices, the effectiveness of BYOD strategies that minimize risk, and IT security professionals' tasks include identifying and implementing network security strategies. The study's implications for positive social change include increasing the number of users informed about cybersecurity and comfortable with defending their networks against foreign and domestic threats to information security and privacy. These changes may mitigate and reduce the spread of malware and viruses and improve overall cybersecurity in BYOD-enabled organizations.

Successful attacks on critical infrastructure have increased in occurrence and sophistication. Many cybersecurity strategies incorporate conventional best practices but often do not consider organizational circumstances and nonstandard critical infrastructure protection needs. The purpose of this qualitative multiple case study was to explore cybersecurity strategies used by information technology (IT) managers and compliance officers to mitigate cyber threats to critical infrastructure. The population for this study comprised IT managers and compliance officers of 4 case organizations in the Pacific Northwest United States. The routine activity theory developed by criminologist Cohen and Felson in 1979 was used as the conceptual framework. Data collection consisted of interviews with 2 IT managers, 3 compliance officers, and 25 documents related to cybersecurity and associated policy governance. A software tool was used in a thematic analysis approach against the data collected from the interviews and documentation. Data triangulation revealed 4 major themes: a robust workforce training program is crucial, make infrastructure resiliency a priority, importance of security awareness, and importance of organizational leadership support and investment. This study revealed key strategies that may help improve cybersecurity strategies used by IT and compliance professionals, which can mitigate successful attacks against critical infrastructure. The study findings will contribute to positive social change through an exploration and contextual analysis of cybersecurity strategy with situational awareness of IT practices to enhance cyber threat mitigation and inform business processes.

Biometrics is the study of physical or behavioral traits that establishes the identity of a person. Forensics, physical security and cyber security are some of the main fields that use biometrics. Unlike traditional authentication systems—such as password based—biometrics cannot be lost, forgotten or shared. This is possible because biometrics establishes the identity of a person based on a physiological/behavioural characteristic rather than what the person possess or remembers. Biometrics has two modes of operation: identification and authentication. Identification finds the identity of a person among a group of persons. Authentication determines if the claimed identity of a person is truthful. Biometric person authentication is an alternative to passwords or graphical patterns. It prevents shoulder surfing attacks, i.e., people watching from a short distance. Nevertheless, biometric traits of conventional authentication techniques like fingerprints, face—and to some extend iris—are easy to capture and duplicate. This denotes a security risk for modern and future applications such as digital twins, where an attacker can copy and duplicate a biometric trait in order to spoof a biometric system. Researchers have proposed ECG as biometric authentication to solve this problem. ECG authentication conceals the biometric traits and reduces the risk of an attack by duplication of the biometric trait. However, current ECG authentication solutions require 10 or more seconds of an ECG signal in order to have accurate results. The accuracy is directly proportional to the ECG signal time-length for authentication. This is inconvenient to implement ECG authentication in an end-user product because a user cannot wait 10 or more seconds to gain access in a secure manner to their device. This thesis addresses the problem of spoofing by proposing an accurate and secure ECG biometric authentication system with relatively short ECG signal length for authentication. The system consists of an ECG acquisition from lead I (two electrodes), signal processing approaches for filtration and R-peak detection, a feature extractor and an authentication process. To evaluate this system, we developed a method to calculate the Equal Error Rate—EER—with non-normal distributed data. In the authentication process, we propose an approach based on Support Vector Machine—SVM—and achieve 4.5% EER with 4 seconds of ECG signal length for authentication. This approach opens the door for a deeper understanding of the signal and hence we enhanced it by applying a hybrid approach of Convolutional Neural Networks—CNN—combined with SVM. The purpose of this hybrid approach is to improve accuracy by automatically detect and extract features with Deep Learning—in this case CNN—and then take the output into a one-class SVM classifier—Authentication; which proved to outperform accuracy for one-class ECG classification. This hybrid approach reduces the EER to 2.84% with 4 seconds of ECG signal length for authentication. Furthermore, we investigated the combination of two different biometrics techniques and we improved the accuracy to 0.46% EER, while maintaining a short ECG signal length for authentication of 4 seconds. We fuse Fingerprint with ECG at the decision level. Decision level fusion requires information that is available from any biometric technique. Fusion at different levels—such as feature level fusion—requires information about features that are incompatible or hidden. Fingerprint minutiae are composed of information that differs from ECG peaks and valleys. Therefore fusion at the feature level is not possible unless the fusion algorithm provides a compatible conversion scheme. Proprietary biometric hardware does not provide information about the features or the algorithms; therefore, features are hidden and not accessible for feature level fusion; however, the result is always available for a decision level fusion.

La creciente competitividad del mercado, genera una dificultad cada vez mayor en las organizaciones para alcanzar el éxito en sus proyectos. Tal hecho busca priorizar criterios económicos, tiempo, costo, calidad y alcance, ocasionando falta de controles que resultan en brechas de seguridad en la compañía. De esa forma se deja en segundo plano procedimientos de seguridad como por ejemplo el testeo de aplicaciones web. Estas poseen vulnerabilidades que podrían proporcionar los medios para que usuarios finales maliciosos violen mecanismos de protección de un sistema y obtengan acceso a información privada o recursos de la empresa. Los pronósticos referentes a la violación de datos indican que la industria de salud será el blanco más buscado para los ataques cibernéticos en 2017 ya que el alto valor de los registros de salud electrónicos (EHRs) llama cada vez más la atención de los cibercriminales. Dichos registros representan una fuente de ganancias mayor a la que si se accediera a información de tarjetas o cuentas bancarias. El presente proyecto propone un modelo de madurez de seguridad de aplicaciones web ante ciberataques para clínicas de nivel 2 bajo la norma técnica del MINSA, orientada a mostrar las debilidades de las aplicaciones web y las mejoras que se puedan realizar en aspectos de seguridad. El proyecto permitió la implementación de mejoras por parte de las empresas clientes en sus plataformas web mediante la recomendación propuesta por la guía de mejora luego de haber realizado el pentesting propuesto. / Bearing in mind that the projections made for the area of information security point to an increase in attacks on the health sector, added to the lack or little diffusion of security maturity models that allow organizations to know the status of their website in terms of security and that the existing models lack a post-evaluation monitoring, it is necessary to propose a model of security maturity of web applications against cyber-attacks, oriented to the health sector, which is simple to apply. The maturity model proposes to offer the user a portfolio of tools that asks them to apply tests and obtain their results, interpret them and place them at a level of maturity before cyberattacks, then proposing controls to improve the security of the web. This model will be based on the International Professional Practice Framework methodology and will include the main vulnerabilities published by the Open Web Application Security Project to propose attacks that identify the weakness of the evaluated web system, so that the client company has the possibility to reinforce its weaknesses. Guides will also be proposed to select strategies to improve critical points from a security perspective. Because of the validation, it was found that, of the 14 tests applied, five were approved, positioning the web at level 3 of maturity, which means that there are validations in the structure of the web; however, they are partial or inefficient. / Tesis

京都大学 / 0048 / 新制・課程博士 / 博士(地球環境学) / 甲第21937号 / 地環博第183号 / 新制||地環||36(附属図書館) / 京都大学大学院地球環境学舎地球環境学専攻 / (主査)教授 宇佐美 誠, 教授 佐野 亘, 准教授 森 晶寿 / 学位規則第4条第1項該当 / Doctor of Global Environmental Studies / Kyoto University / DFAM

The aim of this diploma thesis is to analyze an instance of the GlobeImposter ransomware extracted from an affected device. The first part outlines various types of malware and ransomware and includes a description of encryption mechanisms and key distribution systems. It also discusses possible approaches of static and dynamic analysis of malware samples and requirements for test environments. The practical part describes the source of the malware sample, the physical and virtual test environment and the results of the static and dynamic analysis of the GlobeImposter ransomware. The final part discusses the results and the possibility of implementing a decryptor for the analyzed GlobeImposter ransomware.

In recent years, there has been a significant increase in insider threats within organisations and these have caused massive losses and damages. Due to the fact that email communications are a crucial part of the modern-day working environment, many insider threats exist within organisations’ email infrastructure. It is a well-known fact that employees not only dispatch ‘business-as-usual’ emails, but also emails that are completely unrelated to company business, perhaps even involving malicious activity and unethical behaviour. Such insider threat activities are mostly caused by employees who have legitimate access to their organisation’s resources, servers, and non-public data. However, these same employees abuse their privileges for personal gain or even to inflict malicious damage on the employer. The problem is that the high volume and velocity of email communication make it virtually impossible to minimise the risk of insider threat activities, by using techniques such as filtering and rule-based systems. The research presented in this dissertation suggests strategies to minimise the risk of insider threat via email systems by employing a machine-learning-based approach. This is done by studying and creating categories of malicious behaviours posed by insiders, and mapping these to phrases that would appear in email communications. Furthermore, a large email dataset is classified according to behavioural characteristics of employees. Machine learning algorithms are employed to identify commonly occurring insider threats and to group the occurrences according to insider threat classifications. / Dissertation (MSc (Computer Science))--University of Pretoria, 2020. / Computer Science / MSc (Computer Science) / Unrestricted