Biblio

In view of the increasingly severe network security situation of power information system, this paper draws on the experience of construction of security technology system at home and abroad, with the continuous monitoring and analysis as the core, covering the closed-loop management of defense, detection, response and prediction security as the starting point, Based on the existing defense-based static security protection architecture, a dynamic security technology architecture based on detection and response is established. Compared with the traditional PDR architecture, the architecture adds security threat prediction, strengthens behavior-based detection, and further explains the concept of dynamic defense, so that it can adapt to changes in the grid IT infrastructure and business application systems. A unified security strategy can be formed to deal with more secretive and professional advanced attacks in the future. The architecture emphasizes that network security is a cyclical confrontation process. Enterprise network security thinking should change from the past “emergency response” to “continuous response”, real-time dynamic analysis of security threats, and automatically adapt to changing networks and threat environments, and Constantly optimize its own security defense mechanism, thus effectively solving the problem of the comprehensive technology transformation and upgrading of the security technology system from the traditional passive defense to the active sensing, from the simple defense to the active confrontation, and from the independent protection to the intelligence-driven. At the same time, the paper also gives the technical evolution route of the architecture, which provides a planning basis and a landing method for the continuous fulfillment of the new requirements of the security of the power information system during the 13th Five-Year Plan period.

Fuzzing is a simple yet effective approach to discover software bugs utilizing randomly generated inputs. However, it is limited by coverage and cannot find bugs hidden in deep execution paths of the program because the randomly generated inputs fail complex sanity checks, e.g., checks on magic values, checksums, or hashes. To improve coverage, existing approaches rely on imprecise heuristics or complex input mutation techniques (e.g., symbolic execution or taint analysis) to bypass sanity checks. Our novel method tackles coverage from a different angle: by removing sanity checks in the target program. T-Fuzz leverages a coverage-guided fuzzer to generate inputs. Whenever the fuzzer can no longer trigger new code paths, a light-weight, dynamic tracing based technique detects the input checks that the fuzzer-generated inputs fail. These checks are then removed from the target program. Fuzzing then continues on the transformed program, allowing the code protected by the removed checks to be triggered and potential bugs discovered. Fuzzing transformed programs to find bugs poses two challenges: (1) removal of checks leads to over-approximation and false positives, and (2) even for true bugs, the crashing input on the transformed program may not trigger the bug in the original program. As an auxiliary post-processing step, T-Fuzz leverages a symbolic execution-based approach to filter out false positives and reproduce true bugs in the original program. By transforming the program as well as mutating the input, T-Fuzz covers more code and finds more true bugs than any existing technique. We have evaluated T-Fuzz on the DARPA Cyber Grand Challenge dataset, LAVA-M dataset and 4 real-world programs (pngfix, tiffinfo, magick and pdftohtml). For the CGC dataset, T-Fuzz finds bugs in 166 binaries, Driller in 121, and AFL in 105. In addition, found 3 new bugs in previously-fuzzed programs and libraries.

Open Science Big Data is emerging as an important area of research and software development. Although there are several high quality frameworks for Big Data, additional capabilities are needed for Open Science Big Data. These include data provenance, citable reusable data, data sources providing links to research literature, relationships to other data and theories, transparent analysis/reproducibility, data privacy, new optimizations/advanced algorithms, data curation, data storage and transfer. An important part of science is explanation of results, ideally leading to theory formation. In this paper, we examine means for supporting the use of theory in big data analytics as well as using big data to assist in theory formation. One approach is to fit data in a way that is compatible with some theory, existing or new. Functional Data Analysis allows precise fitting of data as well as penalties for lack of smoothness or even departure from theoretical expectations. This paper discusses principal differential analysis and related techniques for fitting data where, for example, a time-based process is governed by an ordinary differential equation. Automation in theory formation is also considered. Case studies in the fields of computational economics and finance are considered.