MLCapsule: Guarded Offline Deployment of Machine Learning as a Service

With the widespread use of machine learning (ML) techniques, ML as a service has become increasingly popular. In this setting, an ML model resides on a server and users can query it with their data via an API. However, if the user's input is sensitive, sending it to the server is undesirable and sometimes even legally not possible. Equally, the service provider does not want to share the model by sending it to the client for protecting its intellectual property and pay-per-query business model. In this paper, we propose MLCapsule, a guarded offline deployment of machine learning as a service. MLCapsule executes the model locally on the user's side and therefore the data never leaves the client. Meanwhile, MLCapsule offers the service provider the same level of control and security of its model as the commonly used server-side execution. In addition, MLCapsule is applicable to offline applications that require local execution. Beyond protecting against direct model access, we couple the secure offline deployment with defenses against advanced attacks on machine learning models such as model stealing, reverse engineering, and membership inference

Securing the Next Generation Web

With the ever-increasing digitalization of society, the need for secure systems is growing. While some security features, like HTTPS, are popular, securing web applications, and the clients we use to interact with them remains difficult.To secure web applications we focus on both the client-side and server-side. For the client-side, mainly web browsers, we analyze how new security features might solve a problem but introduce new ones. We show this by performing a systematic analysis of the new Content Security Policy (CSP)\ua0 directive navigate-to. In our research, we find that it does introduce new vulnerabilities, to which we recommend countermeasures. We also create AutoNav, a tool capable of automatically suggesting navigation policies for this directive. Finding server-side vulnerabilities in a black-box setting where\ua0 there is no access to the source code is challenging. To improve this, we develop novel black-box methods for automatically finding vulnerabilities. We\ua0 accomplish this by identifying key challenges in web scanning and combining the best of previous methods. Additionally, we leverage SMT solvers to\ua0 further improve the coverage and vulnerability detection rate of scanners.In addition to browsers, browser extensions also play an important role in the web ecosystem. These small programs, e.g. AdBlockers and password\ua0 managers, have powerful APIs and access to sensitive user data like browsing history. By systematically analyzing the extension ecosystem we find new\ua0 static and dynamic methods for detecting both malicious and vulnerable extensions. In addition, we develop a method for detecting malicious extensions\ua0 solely based on the meta-data of downloads over time. We analyze new attack vectors introduced by Google’s new vehicle OS, Android Automotive. This\ua0 is based on Android with the addition of vehicle APIs. Our analysis results in new attacks pertaining to safety, privacy, and availability. Furthermore, we\ua0 create AutoTame, which is designed to analyze third-party apps for vehicles for the vulnerabilities we found

Applications in security and evasions in machine learning : a survey

In recent years, machine learning (ML) has become an important part to yield security and privacy in various applications. ML is used to address serious issues such as real-time attack detection, data leakage vulnerability assessments and many more. ML extensively supports the demanding requirements of the current scenario of security and privacy across a range of areas such as real-time decision-making, big data processing, reduced cycle time for learning, cost-efficiency and error-free processing. Therefore, in this paper, we review the state of the art approaches where ML is applicable more effectively to fulfill current real-world requirements in security. We examine different security applications' perspectives where ML models play an essential role and compare, with different possible dimensions, their accuracy results. By analyzing ML algorithms in security application it provides a blueprint for an interdisciplinary research area. Even with the use of current sophisticated technology and tools, attackers can evade the ML models by committing adversarial attacks. Therefore, requirements rise to assess the vulnerability in the ML models to cope up with the adversarial attacks at the time of development. Accordingly, as a supplement to this point, we also analyze the different types of adversarial attacks on the ML models. To give proper visualization of security properties, we have represented the threat model and defense strategies against adversarial attack methods. Moreover, we illustrate the adversarial attacks based on the attackers' knowledge about the model and addressed the point of the model at which possible attacks may be committed. Finally, we also investigate different types of properties of the adversarial attacks

Security Testing: A Survey

Identifying vulnerabilities and ensuring security functionality by security testing is a widely applied measure to evaluate and improve the security of software. Due to the openness of modern software-based systems, applying appropriate security testing techniques is of growing importance and essential to perform effective and efficient security testing. Therefore, an overview of actual security testing techniques is of high value both for researchers to evaluate and refine the techniques and for practitioners to apply and disseminate them. This chapter fulfills this need and provides an overview of recent security testing techniques. For this purpose, it first summarize the required background of testing and security engineering. Then, basics and recent developments of security testing techniques applied during the secure software development lifecycle, i.e., model-based security testing, code-based testing and static analysis, penetration testing and dynamic analysis, as well as security regression testing are discussed. Finally, the security testing techniques are illustrated by adopting them for an example three-tiered web-based business application