With the rapid growth in smartphone usage, preventing leakage of personal information and privacy has become a challenging task. One major consequence of such leakage is impersonation. This type of illegal usage is nearly impossible to prevent as existing preventive mechanisms (e.g., passcode and fingerprinting), are not capable of continuously monitoring usage and determining whether the user is authorized. Once unauthorized users can defeat the initial protection mechanisms, they would have full access to the devices including using stored passwords to access high-value websites. We present Kollector, a new framework to detect impersonation based on a multi-view bagging deep learning approach to capture sequential tapping information on the smart-phone's keyboard. We construct a sequential-tapping biometrics model to continuously authenticate the user while typing. We empirically evaluated our system using real-world phone usage sessions from 26 users over eight weeks. We then compared our model against commonly used shallow machine techniques and find that our system performs better than other approaches and can achieve an 8.42 percent equal error rate, a 94.24 percent accuracy and a 94.41 percent H-mean using only the accelerometer and only five keyboard taps. We also experiment with using only three keyboard taps and find that the system still yields high accuracy while giving additional opportunities to make more decisions that can result in more accurate final decisions.

In December 2015, Juniper Networks announced multiple security vulnerabilities stemming from unauthorized code in ScreenOS, the operating system for their NetScreen VPN routers. The more sophisticated of these vulnerabilities was a passive VPN decryption capability, enabled by a change to one of the elliptic curve points used by the Dual EC pseudorandom number generator. In this paper, we describe the results of a full independent analysis of the ScreenOS randomness and VPN key establishment protocol subsystems, which we carried out in response to this incident. While Dual EC is known to be insecure against an attacker who can choose the elliptic curve parameters, Juniper had claimed in 2013 that ScreenOS included countermeasures against this type of attack. We find that, contrary to Juniper's public statements, the ScreenOS VPN implementation has been vulnerable since 2008 to passive exploitation by an attacker who selects the Dual EC curve point. This vulnerability arises due to apparent flaws in Juniper's countermeasures as well as a cluster of changes that were all introduced concurrently with the inclusion of Dual EC in a single 2008 release. We demonstrate the vulnerability on a real NetScreen device by modifying the firmware to install our own parameters, and we show that it is possible to passively decrypt an individual VPN session in isolation without observing any other network traffic. We investigate the possibility of passively fingerprinting ScreenOS implementations in the wild. This incident is an important example of how guidelines for random number generation, engineering, and validation can fail in practice.

Recent trends in aviation have led many general aviation pilots to adopt the use of iPads (or other tablets) in the cockpit. While initially used to display static charts and documents, uses have expanded to include live data such as weather and traffic information that is used to make flight decisions. Because the tablet and any connected devices are not a part of the onboard systems, they are not currently subject to the software reliability standards applied to avionics. In this paper, we create a risk model for electronic threats against mobile cockpit information systems and evaluate three such systems popular with general aviation pilots today: The Appareo Stratus 2 receiver with the ForeFlight app, the Garmin GDL~39 receiver with the Garmin Pilot app, and the SageTech Clarity CL01 with the WingX Pro7 app. We found all three to be vulnerable, allowing an attacker to manipulate information presented to the pilot, which in some scenarios would lead to catastrophic outcomes. Finally, we provide recommendations for securing such systems.

In recent years, researchers have proposed systems for running trusted code on an untrusted operating system. Protection mechanisms deployed by such systems keep a malicious kernel from directly manipulating a trusted application's state. Under such systems, the application and kernel are, conceptually, peers, and the system call API defines an RPC interface between them.

We show that on both the x86 and ARM architectures it is possible to mount return-oriented programming attacks without using return instructions. Our attacks instead make use of certain instruction sequences that behave like a return, which occur with sufficient frequency in large libraries on (x86) Linux and (ARM) Android to allow creation of Turing-complete gadget sets.

Modern automobiles are no longer mere mechanical devices; they are pervasively monitored and controlled by dozens of digital computers coordinated via internal vehicular networks. While this transformation has driven major advancements in efficiency and safety, it has also introduced a range of new potential risks. In this paper we experimentally evaluate these issues on a modern automobile and demonstrate the fragility of the underlying system structure. We demonstrate that an attacker who is able to infiltrate virtually any Electronic Control Unit (ECU) can leverage this ability to completely circumvent a broad array of safety-critical systems. Over a range of experiments, both in the lab and in road tests, we demonstrate the ability to adversarially control a wide range of automotive functions and completely ignore driver input\dash including disabling the brakes, selectively braking individual wheels on demand, stopping the engine, and so on. We find that it is possible to bypass rudimentary network security protections within the car, such as maliciously bridging between our car's two internal subnets. We also present composite attacks that leverage individual weaknesses, including an attack that embeds malicious code in a car's telematics unit and that will completely erase any evidence of its presence after a crash. Looking forward, we discuss the complex challenges in addressing these vulnerabilities while considering the existing automotive ecosystem.