Business Analytics Using Predictive Algorithms

In today's data-driven business landscape, organizations strive to extract actionable insights and make informed decisions using their vast data. Business analytics, combining data analysis, statistical modeling, and predictive algorithms, is crucial for transforming raw data into meaningful information. However, there are gaps in the field, such as limited industry focus, algorithm comparison, and data quality challenges. This work aims to address these gaps by demonstrating how predictive algorithms can be applied across business domains for pattern identification, trend forecasting, and accurate predictions. The report focuses on sales forecasting and topic modeling, comparing the performance of various algorithms including Linear Regression, Random Forest Regression, XGBoost, LSTMs, and ARIMA. It emphasizes the importance of data preprocessing, feature selection, and model evaluation for reliable sales forecasts, while utilizing S-BERT, UMAP, and HDBScan unsupervised algorithms for extracting valuable insights from unstructured textual data

USER CENTRIC POLICY MANAGEMENT

Internet use, in general, and online social networking sites, in particular, are ex- periencing tremendous growth with hundreds of millions of active users. As a result, there is a tremendous amount of privacy information and content online. Protect- ing this information is a challenge. Access control policy composition is complex, laborious and tedious for the average user. Usable access control frameworks have lagged. Acceptance / use of available frameworks is low. As a result, policies are only partially configured and maintained. Or, they may be all together ignored. This leads to privacy information and content not being properly protected and potentially unknowingly made available to unintended recipients. We overcome these limitations by introducing User Centric Policy Management – a new paradigm of semi-automated tools that aid users in building, recommending and maintaining their online access control policies. We introduce six user centric policy management assistance tools: Policy Manager is a supervised learning based mech- anism that leverages user provided example policy settings to build classifiers that are the basis for auto-generated policies. Assisted Friend Grouping leverages proven clustering techniques to assist users in grouping their friends for policy management purposes. Same-As Subject Management leverages a user’s memory and opinion of their friends to set policies for other similar friends. Example Friend Selection pro- vides different techniques for aiding users in selecting friends used in the development of access control policies. Same-As Object Management leverages a user’s memory and perception of their objects for setting policies for other similar objects. iLayer is a least privilege based access control model for web and social networking sites that builds, recommends and enforces access control policies for third party developed applications. To demonstrate the effectiveness of these policy management assistance tools, we implemented a suite of prototype applications, conducted numerous experiments and completed a number of extensive user studies. The results show that User Centric Pol- icy Management is a more usable access control framework that is effective, efficient and satisfying to the user, which ultimately improves online security and privacy

SYSTEMATIC DISCOVERY OF ANDROID CUSTOMIZATION HAZARDS

The open nature of Android ecosystem has naturally laid the foundation for a highly fragmented operating system. In fact, the official AOSP versions have been aggressively customized into thousands of system images by everyone in the customization chain, such as device manufacturers, vendors, carriers, etc. If not well thought-out, the customization process could result in serious security problems. This dissertation performs a systematic investigation of Android customization’ inconsistencies with regards to security aspects at various Android layers. It brings to light new vulnerabilities, never investigated before, caused by the under-regulated and complex Android customization. It first describes a novel vulnerability Hare and proves that it is security critical and extensive affecting devices from major vendors. A new tool is proposed to detect the Hare problem and to protect affected devices. This dissertation further discovers security configuration changes through a systematic differential analysis among custom devices from different vendors and demonstrates that they could lead to severe vulnerabilities if introduced unintentionally

Studying JavaScript Security Through Static Analysis

Mit dem stetigen Wachstum des Internets wächst auch das Interesse von Angreifern. Ursprünglich sollte das Internet Menschen verbinden; gleichzeitig benutzen aber Angreifer diese Vernetzung, um Schadprogramme wirksam zu verbreiten. Insbesondere JavaScript ist zu einem beliebten Angriffsvektor geworden, da es Angreifer ermöglicht Bugs und weitere Sicherheitslücken auszunutzen, und somit die Sicherheit und Privatsphäre der Internetnutzern zu gefährden. In dieser Dissertation fokussieren wir uns auf die Erkennung solcher Bedrohungen, indem wir JavaScript Code statisch und effizient analysieren. Zunächst beschreiben wir unsere zwei Detektoren, welche Methoden des maschinellen Lernens mit statischen Features aus Syntax, Kontroll- und Datenflüssen kombinieren zur Erkennung bösartiger JavaScript Dateien. Wir evaluieren daraufhin die Verlässlichkeit solcher statischen Systeme, indem wir bösartige JavaScript Dokumente umschreiben, damit sie die syntaktische Struktur von bestehenden gutartigen Skripten reproduzieren. Zuletzt studieren wir die Sicherheit von Browser Extensions. Zu diesem Zweck modellieren wir Extensions mit einem Graph, welcher Kontroll-, Daten-, und Nachrichtenflüsse mit Pointer Analysen kombiniert, wodurch wir externe Flüsse aus und zu kritischen Extension-Funktionen erkennen können. Insgesamt wiesen wir 184 verwundbare Chrome Extensions nach, welche die Angreifer ausnutzen könnten, um beispielsweise beliebigen Code im Browser eines Opfers auszuführen.As the Internet keeps on growing, so does the interest of malicious actors. While the Internet has become widespread and popular to interconnect billions of people, this interconnectivity also simplifies the spread of malicious software. Specifically, JavaScript has become a popular attack vector, as it enables to stealthily exploit bugs and further vulnerabilities to compromise the security and privacy of Internet users. In this thesis, we approach these issues by proposing several systems to statically analyze real-world JavaScript code at scale. First, we focus on the detection of malicious JavaScript samples. To this end, we propose two learning-based pipelines, which leverage syntactic, control and data-flow based features to distinguish benign from malicious inputs. Subsequently, we evaluate the robustness of such static malicious JavaScript detectors in an adversarial setting. For this purpose, we introduce a generic camouflage attack, which consists in rewriting malicious samples to reproduce existing benign syntactic structures. Finally, we consider vulnerable browser extensions. In particular, we abstract an extension source code at a semantic level, including control, data, and message flows, and pointer analysis, to detect suspicious data flows from and toward an extension privileged context. Overall, we report on 184 Chrome extensions that attackers could exploit to, e.g., execute arbitrary code in a victim's browser

Towards a Network-based Approach for Smartphone Security

Smartphones have become an important utility that affects many aspects of our daily life. Due to their large dissemination and the tasks that are performed with them, they have also become a valuable target for criminals. Their specific capabilities and the way they are used introduce new threats in terms of information security. The research field of smartphone security has gained a lot of momentum in the past eight years. Approaches that have been presented so far focus on investigating design flaws of smartphone operating systems as well as their potential misuse by an adversary. Countermeasures are often realized based upon extensions made to the operating system itself, following a host-based design approach. However, there is a lack of network-based mechanisms that allow a secure integration of smartphones into existing IT infrastructures. This topic is especially relevant for companies whose employees use smartphones for business tasks. This thesis presents a novel, network-based approach for smartphone security called CADS: Context-related Signature and Anomaly Detection for Smartphones. It allows to determine the security status of smartphones by analyzing three aspects: (1) their current configuration in terms of installed software and available hardware, (2) their behavior and (3) the context they are currently used in. Depending on the determined security status, enforcement actions can be defined in order to allow or to deny access to services provided by the respective IT infrastructure. The approach is based upon the distributed collection and central analysis of data about smartphones. In contrast to other approaches, it explicitly supports to leverage existing security services both for analysis and enforcement purposes. A proof of concept is implemented based upon the IF-MAP protocol for network security and the Google Android platform. An evaluation verifies (1) that the CADS approach is able to detect so-called sensor sniffing attacks and (2) that reactions can be triggered based on detection results to counter ongoing attacks. Furthermore, it is demonstrated that the functionality of an existing, host-based approach that relies on modifications of the Android smartphone platform can be mimicked by the CADS approach. The advantage of CADS is that it does not need any modifications of the Android platform itself

Impact and key challenges of insider threats on organizations and critical businesses

The insider threat has consistently been identified as a key threat to organizations and governments. Understanding the nature of insider threats and the related threat landscape can help in forming mitigation strategies, including non-technical means. In this paper, we survey and highlight challenges associated with the identification and detection of insider threats in both public and private sector organizations, especially those part of a nation’s critical infrastructure. We explore the utility of the cyber kill chain to understand insider threats, as well as understanding the underpinning human behavior and psychological factors. The existing defense techniques are discussed and critically analyzed, and improvements are suggested, in line with the current state-of-the-art cyber security requirements. Finally, open problems related to the insider threat are identified and future research directions are discussed

On Enhancing Security of Password-Based Authentication

Password has been the dominant authentication scheme for more than 30 years, and it will not be easily replaced in the foreseeable future. However, password authentication has long been plagued by the dilemma between security and usability, mainly due to human memory limitations. For example, a user often chooses an easy-to-guess (weak) password since it is easier to remember. The ever increasing number of online accounts per user even exacerbates this problem. In this dissertation, we present four research projects that focus on the security of password authentication and its ecosystem. First, we observe that personal information plays a very important role when a user creates a password. Enlightened by this, we conduct a study on how users create their passwords using their personal information based on a leaked password dataset. We create a new metric---Coverage---to quantify the personal information in passwords. Armed with this knowledge, we develop a novel password cracker named Personal-PCFG (Probabilistic Context-Free Grammars) that leverages personal information for targeted password guessing. Experiments show that Personal-PCFG is much more efficient than the original PCFG in cracking passwords. The second project aims to ease the password management hassle for a user. Password managers are introduced so that users need only one password (master password) to access all their other passwords. However, the password manager induces a single point of failure and is potentially vulnerable to data breach. To address these issues, we propose BluePass, a decentralized password manager that features a dual-possession security that involves a master password and a mobile device. In addition, BluePass enables a hand-free user experience by retrieving passwords from the mobile device through Bluetooth communications. In the third project, we investigate an overlooked aspect in the password lifecycle, the password recovery procedure. We study the password recovery protocols in the Alexa top 500 websites, and report interesting findings on the de facto implementation. We observe that the backup email is the primary way for password recovery, and the email becomes a single point of failure. We assess the likelihood of an account recovery attack, analyze the security policy of major email providers, and propose a security enhancement protocol to help securing password recovery emails by two factor authentication. \newline Finally, we focus on a more fundamental level, user identity. Password-based authentication is just a one-time checking to ensure that a user is legitimate. However, a user\u27s identity could be hijacked at any step. For example, an attacker can leverage a zero-day vulnerability to take over the root privilege. Thus, tracking the user behavior is essential to examine the identity legitimacy. We develop a user tracking system based on OS-level logs inside an enterprise network, and apply a variety of techniques to generate a concise and salient user profile for identity examination