Runtime Protection via Dataflow Flattening

Most dynamic binary translators (DBT) and optimizers (DBO) target binary traces, i.e. frequently executed paths, as code regions to be translated and optimized. Code region formation is the most important first step in all DBTs and DBOs. The quality of the dynamically formed code regions determines the extent and the types of optimization opportunities that can be exposed to DBTs and DBOs, and thus, determines the ultimate quality of the final optimized code. The Next-Executing-Tail (NET) trace formation method used in HP Dynamo is an early example of such techniques. Many existing trace formation schemes are variants of NET. They work very well for most binary traces, but they also suffer a major problem: the formed traces may contain a large number of early exits that could be branched out during the execution. If this happens frequently, the program execution will spend more time in the slow binary interpreter or in the unoptimized code regions than in the optimized traces in code cache. The benefit of the trace optimization is thus lost. Traces/regions with frequently taken early-exits are called delinquent traces/regions. Our empirical study shows that at least 8 of the 12 SPEC CPU2006 integer benchmarks have delinquent traces. In this paper, we propose a light-weight region formation technique called Early-Exit Guided Region Formation (EEG) to improve the quality of the formed traces/regions. It iteratively identifies and merges delinquent regions into larger code regions. We have implemented our EEG algorithm in two LLVM-based multi-threaded DBTs targeting ARM and IA32 instruction set architecture (ISA), respectively. Using SPEC CPU2006 benchmark suite with reference inputs, our results show that compared to an NET-variant currently used in QEMU, a state-of-the-art retargetable DBT, EEG can achieve a significant performance improvement of up to 72% (27% on average), and to 49% (23% on average) for IA32 and ARM, respectively.

Most dynamic binary translators (DBT) and optimizers (DBO) target binary traces, i.e. frequently executed paths, as code regions to be translated and optimized. Code region formation is the most important first step in all DBTs and DBOs. The quality of the dynamically formed code regions determines the extent and the types of optimization opportunities that can be exposed to DBTs and DBOs, and thus, determines the ultimate quality of the final optimized code. The Next-Executing-Tail (NET) trace formation method used in HP Dynamo is an early example of such techniques. Many existing trace formation schemes are variants of NET. They work very well for most binary traces, but they also suffer a major problem: the formed traces may contain a large number of early exits that could be branched out during the execution. If this happens frequently, the program execution will spend more time in the slow binary interpreter or in the unoptimized code regions than in the optimized traces in code cache. The benefit of the trace optimization is thus lost. Traces/regions with frequently taken early-exits are called delinquent traces/regions. Our empirical study shows that at least 8 of the 12 SPEC CPU2006 integer benchmarks have delinquent traces.

Focus on software

Techniques and tools for verification of multi-threaded programs must cope with the huge number of possible thread interleavings. Tools based on systematic exploration of a program state space employ partial order reduction to avoid redundant thread interleavings. The key idea is to make non-deterministic thread scheduling choices only at statements that read or modify the global state shared by multiple threads. We focus on the approach to partial order reduction used in tools such as Java Pathfinder (JPF), which construct the program state space on-the-fly, and therefore can use only information available in the current program state and execution history to identify statements that may be globally-relevant. In our previous work, we developed a field access analysis that provides information about fields that may be accessed during program execution, and used it in JPF for more precise identification of globally-relevant statements. We build upon that and propose a may-happen-before analysis that computes a sound approximation of the happens-before ordering. Partial order reduction techniques can use the happens-before ordering to detect pairs of globally-relevant field access statements that cannot be interleaved arbitrarily (due to thread synchronization), and based on that avoid making unnecessary thread scheduling choices. The may-happen-before analysis combines static analysis with knowledge of information available from the dynamic program state. Results of experiments with several Java programs show that usage of the may-happen-before analysis further improves the performance of JPF.

Smart contracts are executable programs that enable the building of a\nprogrammable trust mechanism between multiple entities without the need of a\ntrusted third-party. Researchers have developed several security scanners in\nthe past couple of years. However, many of these analyzers either do not scale\nwell, or if they do, produce many false positives. This issue is exacerbated\nwhen bugs are triggered only after a series of interactions with the functions\nof the contract-under-test. A depth-n vulnerability, refers to a vulnerability\nthat requires invoking a specific sequence of n functions to trigger. Depth-n\nvulnerabilities are time-consuming to detect by existing automated analyzers,\nbecause of the combinatorial explosion of sequences of functions that could be\nexecuted on smart contracts.\n In this paper, we present a technique to analyze depth-n vulnerabilities in\nan efficient and scalable way by combining symbolic execution and data\ndependency analysis. A significant advantage of combining symbolic with static\nanalysis is that it scales much better than symbolic alone and does not have\nthe problem of false positive that static analysis tools typically have. We\nhave implemented our technique in a tool called MPro, a scalable and automated\nsmart contract analyzer based on the existing symbolic analysis tool\nMythril-Classic and the static analysis tool Slither. We analyzed 100 randomly\nchosen smart contracts on MPro and our evaluation shows that MPro is about\nn-times faster than Mythril-Classic for detecting depth-n vulnerabilities,\nwhile preserving all the detection capabilities of Mythril-Classic.\n

This study aims to determine the direction of land use in Ratahan Subdistrict, Ratahan Timur and Pasan, Southeast Minahasa Regency, based on the Agroecology Zone using the application of Geographic Information Systems (GIS). The type of research used in this study is quantitative research with a method of data and spatial interpretation. The types of data used in the study are primary data and secondary data. The data generated is arranged in the form of information and spatial. The study was conducted in 3 (three) subdistricts namely Ratahan Subdistrict, Ratahan Timur Subdistrict and Pasan Subdistrict, Southeast Minahasa Regency, North Sulawesi Province, which were included in 1 (one) sub-district, Ratahan Sub-district. While the implementation time in April to June 2018. Analysis of the data used in this study where the resulting data is arranged in the form of data and spatial information, then compiling and analyzing data using the Quantum ArcGIS version 10.2 program application. Besides that, biophysical data analysis and agricultural potential data will be carried out. The results of the study based on the concept of land use in Ratahan Subdistrict, East Ratahan and Pasan results of agroecological zone maps using GIS obtained 32 zones from the results of analysis of soil type and slope data and climate type. The results of the analysis of the allocation in accordance with the conditions of land use (land cover) as well as the output of the map of Southeast Minahasa Regency RTRW space map in 2013, the agroecological zone can be divided into two main uses, namely protected zone and cultivation zone. In protected areas recommended in protected forests, water catchment areas, river borders and lake bordersaround the springs. Whereas in the cultivation area it is recommended to use land for agroforesty, rice fields, settlements, mixed farming, conservation settlements, plantations and production forest.*eprm*

Hardware information flow tracking based on lightweight path awareness

Developing fast chip multiprocessor simulation techniques is a challenging problem. Solving this problem is especially valuable for design space exploration purposes during the early stages of the design cycle where a large number of design points need to be evaluated quickly. This paper studies statistical simulation as a fast simulation technique for chip multiprocessor (CMP) design space exploration. The idea of statistical simulation is to measure a number of program execution characteristics from a real program execution through profiling, to generate a synthetic trace from it, and simulate that synthetic trace as a proxy for the original program. The important benefit is that the synthetic trace is much shorter compared to a real program trace, which leads to substantial simulation speedups. This paper enhances state-of-the-art statistical simulation: 1) by modeling the memory address stream behavior in a more microarchitecture-independent way and 2) by modeling a program's time-varying execution behavior. These two enhancements enable accurately modeling resource conflicts in shared resources as observed in the memory hierarchy of contemporary chip multiprocessors when multiple programs are coexecuting on the CMP. Our experimental evaluation using the SPEC CPU benchmarks demonstrates average prediction error of 7.3 percent across a range of CMP configurations while varying the number of cores and memory hierarchy configurations.

Antarctic Mapping Tools for Matlab

Today, with the involvement of artificial intelligence (AI), where sensitive information is being collected massively to analyse data for various purposes, privacy-preserving data analysis has gained much importance in the data-driven world. Traditionally, using normal data analysis techniques, privacy-preserving methods often faced several security challenges to ensure the privacy of individuals and organizations where the shared data resources have been used for collaboration or decision-making. Hence, generative artificial intelligence has offered numerous solutions that can ensure privacy to such security concerns by enabling privacy-preserving analysis while getting fruitful knowledge from data. Therefore, in this chapter, the authors aim to provide an overview of privacy-preserving data analysis techniques using AI-based approaches such as differential privacy, federated learning (FL), generative adversarial network (GAN), and other variational auto-encoders (VAEs). While providing individuals protection in privacy, they have discussed how generative AI can be used to preserve statistical properties of plain data using synthetic data analysis. Moreover, some real-world applications have also been part of the discussion to illustrate the effectiveness of such techniques in various domains. Finally, they have also provided some future challenges, limitations, and ethical considerations for implementing these techniques effectively and have provided future directions in the rapidly emerging domain.

This paper explores the concept of Generalized Roughness in LA-Semigroups and its applications in various mathematical disciplines. We highlight the fundamental properties and structures of Generalized Roughness, examining its relationships with Fuzzy Lie Algebras, Order Theory, Lattice Structures, Algebraic Structures, and Categorical Perspectives. Moreover, we investigate the potential of mathematical modeling, optimization techniques, data analysis, and machine learning in the context of Generalized Roughness. Our findings reveal important results in Generalized Roughness, such as the preservation of roughness under the fuzzy equivalence relation and the composition of roughness sets. We demonstrate the significance of Generalized Roughness in the context of order theory and lattice structures, presenting key propositions and a theorem that elucidate its properties and relationships. Furthermore, we explore the applications of Generalized Roughness in mathematical modeling and optimization, highlighting the optimization of roughness measures, parameter estimation, and decision-making processes related to LA-Semigroup operations. We showcase how mathematical techniques can enhance understanding and utilization of LA-Semigroups in practical scenarios. Lastly, we delve into the role of data analysis and machine learning in uncovering patterns, relationships, and predictive models in Generalized Roughness. By leveraging these techniques, we provide examples and insights into how data analysis and machine learning can contribute to enhancing our understanding of LA-Semigroup behavior and supporting decision-making processes.

The recent dramatic increase in the popularity of smartphones has led to increased interest in smartphone security research. From the perspective of a security researcher the noteworthy attributes of a modern smartphone are the ability to install new applications, possibility to access Internet and presence of private or sensitive information such as messages or location. These attributes are also present in a large class of more traditional feature phones. Mobile platform security architectures in these types of devices have seen a much larger scale of deployment compared to platform security architectures designed for PC platforms. In this paper we start by describing the business, regulatory and end-user requirements which paved the way for this widespread deployment of mobile platform security architectures. We briefly describe typical hardware-based security mechanism that provide the foundation for mobile platform security. We then describe and compare the currently most prominent open mobile platform security architectures and conclude that many features introduced recently are borrowed, or adapted with a twist, from older platform security architectures. Finally, we identify a number of open problems in designing effective mobile platform security.

In response to the heightened demands placed on accounting professionals in the era of big data, and the need to cultivate students’ capacities in big data analysis and digital literacy, this study proposes an innovative approach to teaching financial big data analysis courses rooted in the Outcome-based Education (OBE) framework. Addressing prevalent issues in current teaching practices, such as inadequate alignment with real-world business scenarios, students’ lack of a strong sense of purpose in their learning, and rigid assessment methods, the research undertakes a thorough optimization of the course structure. By carefully considering institutional context, student requirements, and market dynamics, the study refines course objectives, reconfigures instructional content and resources, and advocates for teaching mode innovation and assessment reform. Resulting from these efforts is a comprehensive teaching model that seamlessly integrates “theoretical exposition, practical business scenario analysis, hands-on Python demonstrations, and task-oriented exercises”. Grounded in a pedagogical logic that emphasizes an instructional approach “digging deep into a single case”, this model significantly enhances students’ proficiency in accounting practices, data analysis techniques, business acumen, and data literacy.

At work considers the main directions for improving effective ways to protect information in the information exchange system when controlling unmanned aerial vehicles (UAVs) by building an optimal information transmission system under the influence of external influencing factors. The main aspects of the interaction of technical means of MGCS and UAV, which form the basis of the UAV control system, are presented. The prospects of increasing the possibilities of using UAVs through the use of combined groups, including UAVs and manned aircraft (MA) controlled by unified PNPU with an open architecture, are determined. It is shown that effective management of processes and flows in the infrastructure of unmanned vehicles presupposes the presence of information systems that ensure the detection of malicious software and information security of testing technology in the process of standardization and certification. An algorithm for the implementation of ISS certification is presented, which complements the access control system (ACS) with the capabilities to search for UA IES channels, identify their scenarios and eliminate them It is shown how useful some provisions of the theory can be used to improve the efficiency of information exchange systems. An algorithm for the synthesis of the unauthorized access detection system is given and recommendations for improving the noise immunity of radio control lines «mobile ground control station (MGCS)-UAV» in negative external influences (NEI). The expediency of developing a decision making mechanism is noted, according to which, in relation to the operating conditions of radio lines, their operability is assessed and the possibilities of their functioning with a given level of quality are determined. Based on the analysis of data on the operability of the entire complex of software and hardware controls and communications involved in the operation of MGCS and UAVs in NEI conditions, recommendations and proposals for improving the operability of radio lines in NEI conditions are formulated.

Fuzzers are often considered one of the most effective tools to uncover bugs in software, including security vulnerabilities. Current tools are designed to test the full programs by employing strategies that select and generate testcases that increase code coverage. In this paper, we propose to evolve these ideas to make fuzzers focus on specific portions (i.e., lines) of the code, which we call targets, avoiding wasting resources on the other regions. The aim is to support efficient testing of evolving software, and other practical scenarios such as confirming the results from static analysis.