Academic Commons


Anti-Virus in Silicon

Tang, Beng Chiew; Demme, John David; Sethumadhavan, Simha; Stolfo, Salvatore

Anti-virus (AV) software is fundamentally broken. AV systems today rely on correct functioning of not only the AV software but also the underlying OS and VMM. Thus proper functioning of software AV requires millions of lines of complex code – which houses thousands of bugs – to work correctly. Needless to say, and as evidenced in numerous software AV attacks, effective software AV systems have been difficult to build. At the same time, malware incidents are increasing and there is strong demand for good anti-virus solutions; the software anti-virus market is estimated at close to 8B dollars annually.
In this work we present a new class of robust AV systems called Silicon anti-virus systems. Unlike software AV systems, these systems are lean and mostly implemented in hardware to avoid reliance on complex software, but, like software AV systems, are updatable in the field when new malware is encountered. We describe the first generation of silicon AV that uses simple machine learning techniques with existing performance counter infrastructure. Our published and unpublished work shows that common malware such as viruses and adware, and even zero day exploits can be detected accurately. These systems form a very effective first-line, energy- efficient defense against malware.


More About This Work

Academic Units
Computer Science
Published Here
February 2, 2017