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

A Systematic Review of the State of Cyber-Security in Water Systems

Critical infrastructure systems are evolving from isolated bespoke systems to those that use general-purpose computing hosts, IoT sensors, edge computing, wireless networks and artificial intelligence. Although this move improves sensing and control capacity and gives better integration with business requirements, it also increases the scope for attack from malicious entities that intend to conduct industrial espionage and sabotage against these systems. In this paper, we review the state of the cyber-security research that is focused on improving the security of the water supply and wastewater collection and treatment systems that form part of the critical national infrastructure. We cover the publication statistics of the research in this area, the aspects of security being addressed, and future work required to achieve better cyber-security for water systems

Methods for finding the sources of leakage in cache-timing attacks and removing the profiling phase

Cryptographic algorithms are widely used in daily life in order to ensure data confidentiality and privacy. These algorithms are extensively analyzed by scientists against a theoretical deficiency. However, these theoretically verified algorithms could still posses security risks if they are not cautiously implemented. Side-channel analysis can infer the secret key by using the information leakage due to implementation flaws. One of the most studied side-channel attack is the Bernstein’s cache-timing attack. This attack owes its reputation to its ability to succeed without a spy process, which is needed to create intentional cache contentions in other cache attacks. However, the exact leakage sources of the Bernstein’s attack remained uncertain to a large extent. Moreover, the need for an identical target system to perform its profiling phase makes the attack unrealistic for real world computing platforms. In this dissertation we address these two problems. Firstly, we propose a methodology to reveal the exact sources of the information leakage. The iv proposed methodology makes use of hardware performance counters to count the number of cache misses, to which the code blocks in the program are subject. Our methodology can help the developers analyze their implementations and fix their code in the early phases of the development. Secondly, we present an approach to extract simplified cache timing-behavior models analytically and propose to use these generated models instead of a profiling phase. The fact that the attack can be accomplished without a profiling phase will lead the attack to be considered a more realistic threat than the attack originally proposed by Bernstein. We believe that, this improved version of the attack will encourage the cryptographic system designers to take further precautions against the attack

Automating Software Development for Mobile Computing Platforms

Mobile devices such as smartphones and tablets have become ubiquitous in today\u27s computing landscape. These devices have ushered in entirely new populations of users, and mobile operating systems are now outpacing more traditional desktop systems in terms of market share. The applications that run on these mobile devices (often referred to as apps ) have become a primary means of computing for millions of users and, as such, have garnered immense developer interest. These apps allow for unique, personal software experiences through touch-based UIs and a complex assortment of sensors. However, designing and implementing high quality mobile apps can be a difficult process. This is primarily due to challenges unique to mobile development including change-prone APIs and platform fragmentation, just to name a few. in this dissertation we develop techniques that aid developers in overcoming these challenges by automating and improving current software design and testing practices for mobile apps. More specifically, we first introduce a technique, called Gvt, that improves the quality of graphical user interfaces (GUIs) for mobile apps by automatically detecting instances where a GUI was not implemented to its intended specifications. Gvt does this by constructing hierarchal models of mobile GUIs from metadata associated with both graphical mock-ups (i.e., created by designers using photo-editing software) and running instances of the GUI from the corresponding implementation. Second, we develop an approach that completely automates prototyping of GUIs for mobile apps. This approach, called ReDraw, is able to transform an image of a mobile app GUI into runnable code by detecting discrete GUI-components using computer vision techniques, classifying these components into proper functional categories (e.g., button, dropdown menu) using a Convolutional Neural Network (CNN), and assembling these components into realistic code. Finally, we design a novel approach for automated testing of mobile apps, called CrashScope, that explores a given android app using systematic input generation with the intrinsic goal of triggering crashes. The GUI-based input generation engine is driven by a combination of static and dynamic analyses that create a model of an app\u27s GUI and targets common, empirically derived root causes of crashes in android apps. We illustrate that the techniques presented in this dissertation represent significant advancements in mobile development processes through a series of empirical investigations, user studies, and industrial case studies that demonstrate the effectiveness of these approaches and the benefit they provide developers