Biblio

Wearable devices for fitness tracking and health monitoring have gained considerable popularity and become one of the fastest growing smart devices market. More and more companies are offering integrated health and activity monitoring solutions for fitness trackers. Recently insurances are offering their customers better conditions for health and condition monitoring. However, the extensive sensitive information collected by tracking products and accessibility by third party service providers poses vital security and privacy challenges on the employed solutions. In this paper, we present our security analysis of a representative sample of current fitness tracking products on the market. In particular, we focus on malicious user setting that aims at injecting false data into the cloud-based services leading to erroneous data analytics. We show that none of these products can provide data integrity, authenticity and confidentiality.

Servers in a network are typically assigned a static identity. Static assignment of identities is a cornerstone for adversaries in finding targets. Moving Target Defense (MTD) mutates the environment to increase unpredictability for an attacker. On another side, Software Defined Networks (SDN) facilitate a global view of a network through a central control point. The potential of SDN can not only make network management flexible and convenient, but it can also assist MTD to enhance attack surface obfuscation. In this paper, we propose an effective framework for the prevention, detection, and mitigation of flooding-based Denial of Service (DoS) attacks. Our framework includes a light-weight SDN assisted MTD strategy for network reconnaissance protection and an efficient approach for tackling DoS attacks using Software Defined-Internet Exchange Point (SD-IXP). To assess the effectiveness of the MTD strategy and DoS mitigation scheme, we set two different experiments. Our results confirm the effectiveness of our framework. With the MTD strategy in place, at maximum, barely 16% reconnaissance attempts were successful while the DoS attacks were accurately detected with false alarm rate as low as 7.1%.

Protecting data confidentiality and integrity has become increasingly important in modern software. Sometimes, access control mechanisms come short and solutions on the application-level are needed. An approach can rely on enforcing information security using some features provided by certain programming languages. Several different solutions addressing this problem have been presented in literature, and entire new languages or libraries have been built from scratch. Some of them use type systems to let the compiler check for vulnerable code. In this way we are able to rule out those implementations which do not meet a certain security requirement. In this paper we use Haskell's type system to enforce three key properties of information security: non-interference and flexible declassification policies, strict input validation, and secure computations on untainted and trusted values. We present a functional lightweight library for applications with data integrity and confidentiality issues. Our contribute relies on a compile time enforcing of the aforementioned properties. Our library is wholly generalized and might be adapted for satisfying almost every security requirement.

The wide-spreading mobile malware has become a dreadful issue in the increasingly popular mobile networks. Most of the mobile malware relies on network interface to coordinate operations, steal users' private information, and launch attack activities. In this paper, we propose TextDroid, an effective and automated malware detection method combining natural language processing and machine learning. TextDroid can extract distinguishable features (n-gram sequences) to characterize malware samples. A malware detection model is then developed to detect mobile malware using a Support Vector Machine (SVM) classifier. The trained SVM model presents a superior performance on two different data sets, with the malware detection rate reaching 96.36% in the test set and 76.99% in an app set captured in the wild, respectively. In addition, we also design a flow header visualization method to visualize the highlighted texts generated during the apps' network interactions, which assists security researchers in understanding the apps' complex network activities.