Dov Murik

Ehud Aharoni, Allon Adir, Moran Baruch et al.

Privacy-preserving solutions enable companies to offload confidential data to third-party services while fulfilling their government regulations. To accomplish this, they leverage various cryptographic techniques such as Homomorphic Encryption (HE), which allows performing computation on encrypted data. Most HE schemes work in a SIMD fashion, and the data packing method can dramatically affect the running time and memory costs. Finding a packing method that leads to an optimal performant implementation is a hard task. We present a simple and intuitive framework that abstracts the packing decision for the user. We explain its underlying data structures and optimizer, and propose a novel algorithm for performing 2D convolution operations. We used this framework to implement an HE-friendly version of AlexNet, which runs in three minutes, several orders of magnitude faster than other state-of-the-art solutions that only use HE.

Fady Copty, Andre Kassis, Sharon Keidar-Barner et al.

Many applications have security vulnerabilities that can be exploited. It is practically impossible to find all of them due to the NP-complete nature of the testing problem. Security solutions provide defenses against these attacks through continuous application testing, fast-patching of vulnerabilities, automatic deployment of patches, and virtual patching detection techniques deployed in network and endpoint security tools. These techniques are limited by the need to find vulnerabilities before the black-hats. We propose an innovative technique to virtually patch vulnerabilities before they are found. We leverage testing techniques for supervised-learning data generation, and show how artificial intelligence techniques can use this data to create predictive deep neural-network models that read an application's input and predict in real time whether it is a potential malicious input. We set up an ahead-of-threat experiment in which we generated data on old versions of an application, and then evaluated the predictive model accuracy on vulnerabilities found years later. Our experiments show ahead-of-threat detection on LibXML2 and LibTIFF vulnerabilities with 91.3% and 93.7% accuracy, respectively. We expect to continue work on this field of research and provide ahead-of-threat virtual patching for more libraries. Success in this research can change the current state of endless racing after application vulnerabilities and put the defenders one step ahead of the attackers

Fady Copty, Francisco Hernandez, Dov Murik et al.

Cybercriminals use Return Oriented Programming techniques to attack systems and IoT devices. While defenses have been developed, not all of them are applicable to constrained devices. We present Shakedown, which is a compile-time randomizing build tool which creates several versions of the binary, each with a distinct memory layout. An attack developed against one device will not work on another device which has a different memory layout. We tested Shakedown on an industrial IoT device and shown that its normal functionality remained intact while an exploit was blocked.