arXiv subjects

Omer Katz

Publications and source records attributed to Omer Katz.

4 recordsLinked to original sources

arXiv2021

Sensor characterization for the ULTRASAT space telescope

The Ultraviolet Transient Astronomical Satellite is a scientific space mission carrying an astronomical telescope. The mission is led by the Weizmann Institute of Science in Israel and the Israel Space Agency, while the camera in the focal plane is designed and built by Deutsches Elektronen Synchrotron in Germany. Two key science goals of the mission are the detection of counterparts to gravitational wave sources and supernovae. The launch to geostationary orbit is planned for 2024. The telescope with a field-of-view of $\approx200$deg$^2$, is optimized to work in the near-ultraviolet band between $220$ and $280$nm. The focal plane array is composed of four $22.4$-megapixel, backside-illuminated CMOS sensors with a total active area of 90x90mm$^2$. Prior to sensor production, smaller test sensors have been tested to support critical design decisions for the final flight sensor. These test sensors share the design of epitaxial layer and anti-reflective coatings (ARC) with the flight sensors. Here, we present a characterization of these test sensors. Dark current and read noise are characterized as a function of the device temperature. A temperature-independent noise level is attributed to on-die infrared emission and the read-out electronics` self-heating. We utilize a high-precision photometric calibration setup to obtain the test sensors` quantum efficiency (QE) relative to PTB/NIST-calibrated transfer standards ($220$-$1100$nm), the quantum yield for $λ< 300$nm, the non-linearity of the system, and the conversion gain. The uncertainties are discussed in the context of the newest results on the setup`s performance parameters. From three ARC options, Tstd, T1 and T2, the latter optimizes out-of-band rejection and peaks in the mid of the ULTRASAT operational waveband (max. QE $\approx80\%$ at $245\mathrm{nm}$). We recommend ARC option T2 for the final ULTRASAT UV sensor.

astro-ph.IM↗

arXiv2021

Design of the ULTRASAT UV camera

The Ultraviolet Transient Astronomical Satellite (ULTRASAT) is a scientific UV space telescope that will operate in geostationary orbit. The mission, targeted to launch in 2024, is led by the Weizmann Institute of Science (WIS) in Israel and the Israel Space Agency (ISA). Deutsches Elektronen Synchrotron (DESY) in Germany is tasked with the development of the UV-sensitive camera at the heart of the telescope. The camera's total sensitive area of ~90mm x 90mm is built up by four back-side illuminated CMOS sensors, which image a field of view of ~200 deg2. Each sensor has 22.4 megapixels. The Schmidt design of the telescope locates the detector inside the optical path, limiting the overall size of the assembly. As a result, the readout electronics is located in a remote unit outside the telescope. The short focal length of the telescope requires an accurate positioning of the sensors within +-50 mu along the optical axis, with a flatness of +-10 mu. While the telescope will be at around 295K during operations, the sensors are required to be cooled to 200K for dark current reduction. At the same time, the ability to heat the sensors to 343K is required for decontamination. In this paper, we present the preliminary design of the UV sensitive ULTRASAT camera.

astro-ph.IM↗

arXiv2019

Towards Neural Decompilation

We address the problem of automatic decompilation, converting a program in low-level representation back to a higher-level human-readable programming language. The problem of decompilation is extremely important for security researchers. Finding vulnerabilities and understanding how malware operates is much easier when done over source code. The importance of decompilation has motivated the construction of hand-crafted rule-based decompilers. Such decompilers have been designed by experts to detect specific control-flow structures and idioms in low-level code and lift them to source level. The cost of supporting additional languages or new language features in these models is very high. We present a novel approach to decompilation based on neural machine translation. The main idea is to automatically learn a decompiler from a given compiler. Given a compiler from a source language S to a target language T , our approach automatically trains a decompiler that can translate (decompile) T back to S . We used our framework to decompile both LLVM IR and x86 assembly to C code with high success rates. Using our LLVM and x86 instantiations, we were able to successfully decompile over 97% and 88% of our benchmarks respectively.

cs.PL↗

arXiv2018

Security: Doing Whatever is Needed... and Not a Thing More!

As malware, exploits, and cyber-attacks advance over time, so do the mitigation techniques available to the user. However, while attackers often abandon one form of exploitation in favor of a more lucrative one, mitigation techniques are rarely abandoned. Mitigations are rarely retired or disabled since proving they have outlived their usefulness is often impossible. As a result, performance overheads, maintenance costs, and false positive rates induced by the different mitigations accumulate, culminating in an outdated, inefficient, and costly security solution. We advocate for a new kind of tunable framework on which to base security mechanisms. This new framework enables a more reactive approach to security allowing us to optimize the deployment of security mechanisms based on the current state of attacks. Based on actual evidence of exploitation collected from the field, our framework can choose which mechanisms to enable/disable so that we can minimize the overall costs and false positive rates while maintaining a satisfactory level of security in the system. We use real-world Snort signatures to simulate the benefits of reactively disabling signatures when no evidence of exploitation is observed and compare them to the costs of the current state of deployment. Additionally, we evaluate the responsiveness of our framework and show that in case disabling a security mechanism triggers a reappearance of an attack we can respond in time to prevent mass exploitation. Through large-scale simulations that use integer linear and Bayesian solvers, we discover that our responsive strategy is both computationally affordable and results in significant reductions in false positives (~20% over traces that are about 9 years long), at the cost of introducing a moderate number of false negatives. Finding the optimal sampling strategy takes less than 2.5 minutes in the vast majority of cases.

cs.CR↗