A Heuristic Local-sensitive Program-Wide Diffing Method for IoT Binary Files

Code reuse brings vulnerabilities in third-party library to many Internet of Things (IoT) devices, opening them to attacks such as distributed denial of service. Program-wide binary diffing technology can help detect these vulnerabilities in IoT devices whose source codes are not public. Considering the architectures of IoT devices may vary, we propose a data-aware program-wide diffing method across architectures and optimization levels. We rely on the defined anchor functions and call relationship to expand the comparison scope within the target file, reducing the impact of different architectures on the diffing result. To make the diffing result more accurate, we extract the semantic features that can represent the code by data flow dependence analysis. Earth mover distance is used to calculate the similarity of functions in two files based on semantic features. We implemented a proof-of-concept DAPDiff and compared it with baseline BinDiff, TurboDiff and Asm2vec. Experiments showed the availability and effectiveness of our method across optimization levels and architectures. DAPDiff outperformed BinDiff in recall and precision by 41.4% and 9.2% on average when making diffing between standard third-party library and the real-world firmware files. This proves that DAPDiff can be applicable for the vulnerability detection in IoT devices.

Flow dependence of wintertime subseasonal prediction skill over Europe

Issuing skillful forecasts beyond the typical horizon of weather predictability remains a challenge actively addressed by the scientific community. This study evaluates winter subseasonal reforecasts delivered by the CNRM and ECMWF dynamical systems and identifies that the level of skill for predicting temperature in Europe varies fairly consistently in both systems. In particular, forecasts initialized during positive North-Atlantic Oscillation (NAO) phases tend to be more skillful over Europe at week 3 in both systems. Composite analyses performed in an atmospheric reanalysis, a long-term climate simulation and both forecast systems unveil very similar temperature and sea-level pressure patterns three weeks after NAO conditions. Furthermore, regressing these fields onto the 3-week prior NAO index in a reanalysis shows consistent patterns over Europe but also other regions of the northern hemisphere extratropics, thereby suggesting a lagged teleconnection, either related to the persistence or recurrence of the postive and negative phases of the NAO. This teleconnection, conditionned to the intensity of the initial NAO phase, is well captured by forecast systems. As a result, it is a key mechanism for determining a priori confidence in the skill of wintertime subseasonal forecasts over Europe as well as others parts of the northern hemisphere.

Investigating the primacy of B-matrix EDA flow dependence within the Copernicus Regional Re-Analysis (CERRA)

<p>The new Copernicus European Regional Re-Analysis (CERRA) is a 5.5km reanalysis, starting in 1984 and ending “near-real-time”, 2021. The reanalysis was delivered using the ALADIN model under the HARMONIE scripting garb. The upper-air is analysed using a 3DVAR technique cycled 3-hourly, while the surface analysis is achieved through a conventional OI technique (MESCAN). Analyses produced by CERRA at 5.5km are assisted through an accompanying 10-member Ensemble Data Assimilation (EDA) system with 11km horizontal resolution cycled 6-hourly. The EDA system is used mainly for serially updated background error covariance estimation (B-matrix) used in the deterministic upper-air 3DVAR minimisation to produce the upper-air analysis.</p><p>The B-matrix comprises 2 principal EDA-derived components. The first component is estimated from same-resolution (5.5km) forecast differences, run in the winter and the summer periods, to represent seasonal climatology. This component also varies in time, such that a linearly appropriated proportion of summer or winter differences is taken, based on the current time of year of the reanalysis. The second component comes from the lower-resolution (11km) set of forecast differences, which represents ‘errors of the day’. This second component is a 2.5 day moving average ingested into a new B-matrix every 2 days. The B-matrix is thus comprised of 80% forecast differences coming from the first component and 20% coming from the second component. </p><p>We show results from our study on the primacy of varying the weighting on the 2 components of forecast differences mentioned above, and how it has the potential, given a suitable observation network, to provide better B-matrix statistics.</p>

Flow dependence of wintertime subseasonal prediction skill over Europe

<p><span>Issuing skillful forecasts beyond the typical horizon of weather predictability</span> <span>remains a challenge actively addressed by the scientific community. This study evaluates winter subseasonal reforecasts delivered by the</span> <span>CNRM and ECMWF dynamical systems and identifies that the level of skill for predicting temperature in Europe </span><span>varies</span> <span>fairly </span><span>consistently</span><span> in both systems. In particular, forecasts initialized during positive NAO phases tend to be more skillful over Europe at week three in both systems. Composite analyses performed in an atmospheric reanalysis, a long-term climate simulation and both forecast systems unveil very similar temperature and sea-level pressure patterns 3 weeks after NAO+ conditions. Furthermore, regressing these fields onto the 3-week </span><span>pre</span><span>vious</span> <span>NAO index in a reanalysis shows consistent patterns over Europe but also eastern North America, thereby revealing</span> <span>a lagged teleconnection,</span> <span>either related to the persistence or recurrence of the NAO+</span> <span>weather regime. Since this feature is well captured by forecast systems, this </span><span>is </span><span>a key mechanism for determining a priori confidence in the skill of wintertime subseasonal forecasts over Europe and North America.</span></p>

INFORMATION FLOW DEPENDENCE IN FINANCIAL MARKETS

In response to empirical evidence, we propose a continuous-time model for multivariate asset returns with a two-layered dependence structure. The price process is subject to multivariate information arrivals driving the market activity modeled by nondecreasing pure-jump Lévy processes. A Lévy copula determines the jump dependence and allows for a generic multivariate information flow with a flexible structure. Conditional on the information flow, asset returns are jointly normal. Within this setup, we provide an estimation framework based on maximum simulated likelihood. We apply novel multivariate models to equity data and obtain estimates which meet an economic intuition with respect to the two-layered dependence structure.