Mobile devices aggregate various types of data from sensitive corporate documents to personal content. While users desire to access this content on a single device via a unified user experience and through any mobile app, protecting this data is challenging. Even though different data types have different security and privacy needs, mobile operating systems include only a few, if any, functionalities for fine-grained data protection. We present SWIRLS, an Android-based mobile OS that provides a policy-based information-flow data protection abstraction for mobile apps to support BYOD (bring-your-own-device) use cases. SWIRLS attaches security policies to individual pieces of data and enforces these policies as the data flows through the device. Unlike current BYOD solutions like VMs that create duplication overload, SWIRLS provides a single environment to access content from different security contexts using the same applications while monitoring for malicious data leakage. SWIRLS leverages a two-level hybrid information flow tracking (IFT) mechanism to track both intra-application flows and a higher level IFT based on processes for application isolation. Our evaluation presents BYOD data protection use-cases such as limiting document sharing, preventing leakage based on document classification and security policies based on geo-fencing. SWIRLS only imposes a low battery consumption and performance overhead.

Mobile and IoT operating systems–and their ensuing software updates–are usually distributed as binary files. Given that these binary files are commonly closed source, users or businesses who want to assess the security of the software need to rely on reverse engineering. Further, verifying the correct application of the latest software patches in a given binary is an open problem. The regular application of software patches is a central pillar for improving mobile and IoT device security. This requires developers, integrators, and vendors to propagate patches to all affected devices in a timely and coordinated fashion. In practice, vendors follow different and sometimes improper security update agendas for both mobile and IoT products. Moreover, previous studies revealed the existence of a hidden patch gap: several vendors falsely reported that they patched vulnerabilities. Therefore, techniques to verify whether vulnerabilities have been patched or not in a given binary are essential. Deep learning approaches have shown to be promising for static binary analyses with respect to inferring binary similarity as well as vulnerability detection. However, these approaches fail to capture the dynamic behavior of these systems, and, as a result, they may inundate the analysis with false positives when performing vulnerability discovery in the wild. In particular, they cannot capture the fine-grained characteristics necessary to distinguish whether a vulnerability has been patched or not. In this paper, we present PATCHECKO, a vulnerability and patch presence detection framework for executable binaries. PATCHECKO relies on a hybrid, cross-platform binary code similarity analysis that combines deep learning-based static binary analysis with dynamic binary analysis. PATCHECKO does not require access to the source code of the target binary nor that of vulnerable functions. We evaluate PATCHECKO on the most recent Google Pixel 2 smartphone and the Android Things IoT firmware images, within which 25 known CVE vulnerabilities have been previously reported and patched. Our deep learning model shows a vulnerability detection accuracy of over 93%. We further prune the candidates found by the deep learning stage–which includes false positives–via dynamic binary analysis. Consequently, PATCHECKO successfully identifies the correct matches among the candidate functions in the top 3 ranked outcomes 100% of the time. Furthermore, PATCHECKO's differential engine distinguishes between functions that are still vulnerable and those that are patched with an accuracy of 96%.

Trustworthy and usable healthcare requires not only effective disease diagnostic procedures to ensure delivery of rapid and accurate outcomes, but also lightweight user privacy-preserving capabilities for resource-limited medical sensing devices. In this paper, we present MedSen, a portable, inexpensive and secure smartphone-based biomarker1 detection sensor to provide users with easy-to-use real-time disease diagnostic capabilities without the need for in-person clinical visits. To minimize the deployment cost and size without sacrificing the diagnostic accuracy, security and time requirement, MedSen operates as a dongle to the user's smartphone and leverages the smartphone's computational capabilities for its real-time data processing. From the security viewpoint, MedSen introduces a new hardware-level trusted sensing framework, built in the sensor, to encrypt measured analog signals related to cell counting in the patient's blood sample, at the data acquisition point. To protect the user privacy, MedSen's in-sensor encryption scheme conceals the user's private information before sending them out for cloud-based medical diagnostics analysis. The analysis outcomes are sent back to Med-Sen for decryption and user notifications. Additionally, MedSen introduces cyto-coded passwords to authenticate the user to the cloud server without the need for explicit screen password entry. Each user's password constitutes a predetermined number of synthetic beads with different dielectric characteristics. MedSen mixes the password beads with the user's blood before submitting the data for diagnostics analysis. The cloud server authenticates the user based on the statistics and characteristics of the beads with the blood sample, and links the user's identity to the encrypted analysis outcomes. We have implemented a real-world working prototype of MedSen through bio-sensor fabrication and smartphone app (Android) implementations. Our results show that MedSen can reliably classify different users based on their cyto-coded passwords with high accuracy. MedSen's built-in analog signal encryption guarantees the user's privacy by considering the smartphone and cloud server possibly untrusted (curious but honest). MedSen's end-to-end time requirement for disease diagnostics is approximately 0.2 seconds on average.

Mobile devices are increasingly becoming a melting pot of different types of data ranging from sensitive corporate documents to commercial media to personal content produced and shared via online social networks. While it is desirable for such diverse content to be accessible from the same device via a unified user experience and through a rich plethora of mobile apps, ensuring that this data remains protected has become challenging. Even though different data types have very different security and privacy needs and accidental instances of data leakage are common, today's mobile operating systems include few, if any, facilities for fine-grained data protection and isolation. In this paper, we present SWIRLS, an Android-based mobile OS that provides a rich policy-based information-flow data protection abstraction for mobile apps to support BYOD (bring-your-own-device) use cases. SWIRLS allows security and privacy policies to be attached to individual pieces of data contained in signed and encrypted capsules, and enforces these policies as the data flows through the device. Unlike current BYOD solutions like VMs and containers that create duplication and cognitive overload, SWIRLS provides a single environment that allows users to access content belonging to different security contexts using the same applications without fear of inadverdant or malicious data leakage. SWIRLS also unburdens app developers from having to worry about security policies, and provides APIs through which they can create seamless multi-security-context user interfaces. To implement it's abstractions, SWIRLS develops a cryptographically protected capsule distribution and installation scheme, enhances Taintdroid-based taint-tracking mechanisms to support efficient kernel and user-space security policy enforcement, implements techniques for persisting security context along with data, and provides transparent security-context switching mechanisms. Using our Android-based prototype (>25K LOC), we show a number of data protection use-cases such as isolation of personal and work data, limiting document sharing and preventing leakage based on document classification, and security policies based on geo-and time-fencing. Our experiments show that SWIRLS imposes a very minimal overhead in both battery consumption and performance.

Preserving the availability and integrity of security-critical computer systems in a fast-spreading sophisticated intrusions environment, requires advance algorithms, accurate and efficient intrusion diagnosis, along side with root-cause analysis techniques. In this paper we introduce FloTracker that is an online log-free host-based root-cause analysis detection engine, with instantaneous forensics capabilities. FloTracker presents security administrators as well as automated response systems, with immediate forensics information. For instance, it will identify a system's entry point of intrusion as soon as a critical security incident occurs, e.g., a sensitive system file modification is detected within the target system. To this end, FloTracker automatically defines an access control policy set (possibly with no access restriction) for the target system that facilitates real-time backtracking of an intrusion, given a detection point. Our experimental results on a real-world SE-Linux test-bed showed that the FloTracker could efficiently update the system's configuration thus modifications will not affect the functionalities of the system, yet providing a log-free and instantaneous root-cause analysis capability.

This paper describes an empirical research study to characterize attackers and attacks against targets of opportunity. A honey net infrastructure was built and deployed over 167 days that leveraged three different honey pot configurations and a SSH-based authentication proxy to attract and follow attackers over several weeks. A total of 211 attack sessions were recorded and evidence was collected at each stage of the attack sequence: from discovery to intrusion and exploitation of rogue software. This study makes two important contributions: 1) we introduce a new approach to measure attacker skills, and 2) we leverage keystroke profile analysis to differentiate attackers beyond their IP address of origin.