Side-channel Vulnerability Factor

ABSTRACT

There have been many attacks that exploit information leakage and many proposed countermeasures to protect against these attacks. Currently, however, there is no systematic, holistic methodology for understanding leakage or making performance-security trade-offs. As a result, it is not well studied or known how various system design decisions affect information leakage or their vulnerability to side- channel attacks.

In this paper, we propose a new metric for measuring information leakage called the Side-channel Vulnerability Factor (SVF). SVF is based on our observation that all side-channel attacks ranging from physical to microarchitectural to software rely on recognizing leaked execution patterns. Accordingly, SVF examines patterns in attackers' observations and measures their correlation to the victim's actual execution patterns.

SVF can be used by designers to evaluate security options and create robust systems, and by at- tackers to pick out leaky targets. As a detailed case study, we show how SVF can be used to evaluate microarchitectural design choices. This study demonstrates the danger of evaluating ad hoc side-channel countermeasures in a vacuum and without quantitative leakage metrics. A whole-system security met- ric like SVF has the potential to establish a quantitative basis for evaluating system security decisions pertaining to side-channel leaks.

1 Introduction

Computing is steadily moving to a model where most of the data lives on the cloud and personal electronic devices are used to access this data from multiple locations. The aggregation of services in the backend provides increased efficiency but also creates a security risk because of information leakage through shared resources such as processors, caches and networks. These information leaks, known as side-channel leaks, can compromise security by allowing a user to obtain information from an unauthorized security level or even by simply violating observability restrictions.

In addition to being central to side channels, patterns have the useful property of being computationally recognizable. In fact, pattern recognition in the form of phase detection [3, 5] is well known and used in computer architecture. In light of our observation about patterns, it seems obvious that side-channel attackers actually do no more than recognize execution phase shifts over time in victim applications. In the case of encryption, computing with a 1 bit from the key is one phase, whereas computing with a 0 bit is another. By detecting shifts from one phase to the other, an attacker can reconstruct the original key [4, 2]. Even HTTPS attacks work similarly - the attacker detects the network phase transitions from "request" to "waiting" to "transferring" and times each phase. The timing of each phase is, in many cases, sufficient to identify a surprising amount and variety of information about the request and user session [1]. Given this commonality of side-channel attacks, our key insight is that side-channel information leakage can be characterized entirely by recognition of patterns through the channel. Figure 2 shows an example of pattern leakage through two microarchitectures, one of which transmits patterns readily and one of which does not.

Accordingly, we can measure information leakage by computing the correlation between ground-truth patterns and attacker observed patterns. We call this correlation Side-channel Vulnerability Factor (SVF). SVF measures the signal-to-noise ratio in an attacker's observations. While any amount of leakage could compromise a system, a low signal-to-noise ratio means that the attacker must either make do with inaccurate results (and thus make many observations to create an accurate result) or become much more intelligent about recovering the original signal. This assertion appears to be historically true, as evidenced by the example SVFs given in Table ??. While the attack [4] on the 0.73 SVF system was relatively simple, the 0.27 system's attack [2] required a trained artificial neural network to filter noisy observations.