The arms race: adversarial search defeats entropy used to detect malware

Malware creators have been getting their way for too long now. String-based similarity measures can leverage ground truth in a scalable way and can operate at a level of abstraction that is difficult to combat from the code level. At the string level, information theory and, specifically, entropy play an important role related to detecting patterns altered by concealment strategies, such as polymorphism or encryption. Controlling the entropy levels in different parts of a disk resident executable allows an analyst to detect malware or a black hat to evade the detection. This paper shows these two perspectives into two scalable entropy-based tools: EnTS and EEE. EnTS, the detection tool, shows the effectiveness of detecting entropy patterns, achieving 100% precision with 82% accuracy. It outperforms VirusTotal for accuracy on combined Kaggle and VirusShare malware. EEE, the evasion tool, shows the effectiveness of entropy as a concealment strategy, attacking binary-based state of the art detectors. It learns their detection patterns in up to 8 generations of its search process, and increments their false negative rate from range 0–9%, up to the range 90–98.7%

Calibration of the effective tunneling bandgap in GaAsSb/InGaAs for improved TFET performance prediction

© 2016 IEEE. The effective bandgap for heterojunction band-to-band tunneling (Eg,eff) is a crucial design parameter for a heterojunction tunneling FET (TFET). However, there is significant uncertainty on Eg,eff, especially for In0.53Ga0.47As/ GaAs0.5Sb0.5. This makes the prediction of TFET performance difficult. We calibrate Eg,eff by fabricating heterojunction p+/i/n+ diodes, comparing the simulated and the measured current-voltage and capacitance-voltage curves, while taking Eg,eff as a fitting parameter. Our calibration significantly reduces the uncertainty on Eg,eff compared with the range found in the literature. The comparison with the previous work on highly doped heterojunction diodes suggests that dopant-dependent bandgap narrowing reduces Eg,eff and therefore significantly impacts the performance of highly doped TFET.status: publishe

Extracting the effective bandgap of heterojunctions using Esaki diode I-V measurements

The effective bandgap is a crucial design parameter of heterojunction tunneling field-effect transistors. In this letter, we demonstrate a method to measure the effective bandgap directly from the band-to-band tunneling current of a heterojunction Esaki diode, of which we only require knowledge of the electrostatic potential profile. The method is based on a characteristic exponentially increasing current with forward bias, caused by sharp energy filtering at cryogenic temperature. We apply this method experimentally to a n+In0.53Ga0.47As/pGaAs(0.5)Sb(0.5) Esaki diode and define requirements to apply it to other heterojunctions.status: publishe

Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g. cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale