Ioannis Agadakos

Ioannis Agadakos, Manuel Egele, William Robertson

Designing and implementing secure software is inarguably more important than ever. However, despite years of research into privilege separating programs, it remains difficult to actually do so and such efforts can take years of labor-intensive engineering to reach fruition. At the same time, new intra-process isolation primitives make strong data isolation and privilege separation more attractive from a performance perspective. Yet, substituting intra-process security boundaries for time-tested process boundaries opens the door to subtle but devastating privilege leaks. In this work, we present Polytope, a language extension to C++ that aims to make efficient privilege separation accessible to a wider audience of developers. Polytope defines a policy language encoded as C++11 attributes that separate code and data into distinct program partitions. A modified Clang front-end embeds source-level policy as metadata nodes in the LLVM IR. An LLVM pass interprets embedded policy and instruments an IR with code to enforce the source-level policy using Intel MPK. A run-time support library manages partitions, protection keys, dynamic memory operations, and indirect call target privileges. An evaluation demonstrates that Polytope provides equivalent protection to prior systems with a low annotation burden and comparable performance overhead. Polytope also renders privilege leaks that contradict intended policy impossible to express.

Ioannis Agadakos, Nikolaos Agadakos, Jason Polakis et al.

Researchers have demonstrated various techniques for fingerprinting and identifying devices. Previous approaches have identified devices from their network traffic or transmitted signals while relying on software or operating system specific artifacts (e.g., predictability of protocol header fields) or characteristics of the underlying protocol (e.g.,frequency offset). As these constraints can be a hindrance in real-world settings, we introduce a practical, generalizable approach that offers significant operational value for a variety of scenarios, including as an additional factor of authentication for preventing impersonation attacks. Our goal is to identify artifacts in transmitted signals that are caused by a device's unique hardware "imperfections" without any knowledge about the nature of the signal. We develop RF-DCN, a novel Deep Complex-valued Neural Network (DCN) that operates on raw RF signals and is completely agnostic of the underlying applications and protocols. We present two DCN variations: (i) Convolutional DCN (CDCN) for modeling full signals, and (ii) Recurrent DCN (RDCN) for modeling time series. Our system handles raw I/Q data from open air captures within a given spectrum window, without knowledge of the modulation scheme or even the carrier frequencies. While our experiments demonstrate the effectiveness of our system, especially under challenging conditions where other neural network architectures break down, we identify additional challenges in signal-based fingerprinting and provide guidelines for future explorations. Our work lays the foundation for more research within this vast and challenging space by establishing fundamental directions for using raw RF I/Q data in novel complex-valued networks.