Enterprises are targeted by various malware activities at a staggering rate. To counteract the increased sophistication of cyber attacks, most enterprises deploy within their perimeter a number of security technologies, including firewalls, antivirus software, and web proxies, as well as specialized teams of security analysts forming Security Operations Centers (SOCs).

We introduce Drifting Keys (DKs), a simple new approach to detecting device impersonation. DKs enable detection of complete compromise by an attacker of the device and its secret state, e.g., cryptographic keys. A DK evolves within a device randomly over time. Thus an attacker will create DKs that randomly diverge from those in the original, valid device over time, alerting a trusted verifier to the attack. DKs may be transmitted unidirectionally from a device, eliminating interaction between the device and verifier. Device emissions of DK values can be quite compact - even just a single bit - and DK evolution and emission require minimal computation. Thus DKs are well suited for highly constrained devices, such as sensors and hardware authentication tokens. We offer a formal adversarial model for DKs, and present a simple scheme that we prove essentially optimal (undominated) for a natural class of attack timelines. We explore application of this scheme to one-time passcode authentication tokens. Using the logs of a large enterprise, we experimentally study the effectiveness of DKs in detecting the compromise of such tokens.

Infrastructure-as-a-system compute clouds such as Amazon's EC2 allow users to pay a flat hourly rate to run their virtual machine (VM) on a server providing some combination of CPU access, storage, and network. But not all VM instances are created equal: distinct underlying hardware differences, contention, and other phenomena can result in vastly differing performance across supposedly equivalent instances. The result is striking variability in the resources received for the same price.

Finite fields are widely used in constructing error-correcting codes and cryptographic algorithms. In practice, error-correcting codes use small finite fields to achieve high-throughput encoding and decoding. Conversely, cryptographic systems employ considerably larger finite fields to achieve high levels of security. We focus on developing efficient software implementations of arithmetic operations in reasonably large finite fields as needed by secure storage applications. In this article, we study several arithmetic operation implementations for finite fields ranging from GF (2 32 ) to GF (2 128 ). We implement multiplication and division in these finite fields by making use of precomputed tables in smaller fields, and several techniques of extending smaller field arithmetic into larger field operations. We show that by exploiting known techniques, as well as new optimizations, we are able to efficiently support operations over finite fields of interest. We perform a detailed evaluation of several techniques, and show that we achieve very practical performance for both multiplication and division. Finally, we show how these techniques find applications in the implementation of HAIL, a highly available distributed cloud storage layer. Using the newly implemented arithmetic operations in GF (2 64 ), HAIL improves its performance by a factor of two, while simultaneously providing a higher level of security.

This paper presents a new challenge--verifying that a remote server is storing a file in a fault-tolerant manner, i.e., such that it can survive hard-drive failures. We describe an approach called the Remote Assessment of Fault Tolerance (RAFT). The key technique in a RAFT is to measure the time taken for a server to respond to a read request for a collection of file blocks. The larger the number of hard drives across which a file is distributed, the faster the read-request response. Erasure codes also play an important role in our solution. We describe a theoretical framework for RAFTs and offer experimental evidence that RAFTs can work in practice in several settings of interest.

Knowledge-based authentication systems generally rely upon users' explicit recollection of passwords, facts, or personal preferences. These systems impose a cognitive burden that often results in forgotten secrets or secrets with poor entropy. We propose an authentication system that instead draws on implicit memory - that is, the unconscious encoding and usage of information. In such a system, a user is initially presented with images of common objects in a casual familiarization task. When the user later authenticates, she is asked to perform a task involving a set of degraded images, some of which are based upon the images in the familiarization task. The prior exposure to those images influences the user's responses in the task, thereby eliciting authentication information. We ran a user study to investigate the plausibility of our system design. Our results suggest that implicit memory has potential as a basis for low-cognitive-overhead, high-stability, knowledge-based authentication.

We introduce HAIL (High-Availability and Integrity Layer), a distributed cryptographic system that allows a set of servers to prove to a client that a stored file is intact and retrievable. HAIL strengthens, formally unifies, and streamlines distinct approaches from the cryptographic and distributed-systems communities. Proofs in HAIL are efficiently computable by servers and highly compact---typically tens or hundreds of bytes, irrespective of file size. HAIL cryptographically verifies and reactively reallocates file shares. It is robust against an active, mobile adversary, i.e., one that may progressively corrupt the full set of servers. We propose a strong, formal adversarial model for HAIL, and rigorous analysis and parameter choices. We show how HAIL improves on the security and efficiency of existing tools, like Proofs of Retrievability (PORs) deployed on individual servers. We also report on a prototype implementation.