Malware is emerging day by day. To evade detection, many malware obfuscation techniques have emerged. Dynamic malware detection methods based on data flow graphs have attracted much attention since they can deal with ...
详细信息
ISBN:
(纸本)9789811951947;9789811951930
Malware is emerging day by day. To evade detection, many malware obfuscation techniques have emerged. Dynamic malware detection methods based on data flow graphs have attracted much attention since they can deal with the obfuscation problem to a certain extent. Many malware classification methods based on data flow graphs have been proposed. Some of them are based on user-defined features or graph similarity of data flow graphs. graph neural networks have also recently been used to implement malware classification recently. This paper provides an overview of current data flow graph-based malware classification methods. Their respective advantages and disadvantages are summarized as well. In addition, the future trend of the data flow graph-based malware classification method is analyzed, which is of great significance for promoting the development of malware detection technology.
Smart contracts with natural economic attributes have been widely and rapidly developed in various fields. However, the bugs and vulnerabilities in smart contracts have brought huge economic losses, which has strength...
详细信息
ISBN:
(纸本)9781665425872
Smart contracts with natural economic attributes have been widely and rapidly developed in various fields. However, the bugs and vulnerabilities in smart contracts have brought huge economic losses, which has strengthened people's attention to the security issues of smart contracts. The immutability of smart contracts makes people more willing to conduct security checks before deploying smart contracts. Nonetheless, existing smart contract vulnerability detection techniques are far away from enough: static analysis approaches rely heavily on manually crafted heuristics which is difficult to reuse across different types of vulnerabilities while deep learning based approaches also have unique limitations. In this study, we propose a novel approach, Peculiar, which uses Pre-training technique for detection of smart contract vulnerabilities based on crucial data flow graph. Compared against the traditional data flow graph which is already utilized in existing approach, crucial data flow graph is less complex and does not bring an unnecessarily deep hierarchy, which makes the model easy to focus on the critical features. Moreover, we also involve pre-training technique in our model due to the dramatic improvements it has achieved on a variety of NLP tasks. Our empirical results show that Peculiar can achieve 91.80% precision and 92.40% recall in detecting reentrancy vulnerability, one of the most severe and common smart contract vulnerabilities, on 40,932 smart contract files, which is significantly better than the state-of-the-art methods (e.g., Smartcheck achieves 79.37% precision and 70.50% recall). Meanwhile, another experiment shows that Peculiar is more discerning to reentrancy vulnerability than existing approaches. The ablation experiment reveals that both crucial data flow graph and pre-trained model contribute significantly to the performances of Peculiar.
Hardware Trojans have become a significant threat to computing reliability and data security in reconfigurable hardware. One of the most effective techniques of run-time detection and recovery is based on Triple Modul...
详细信息
ISBN:
(纸本)9781509019977
Hardware Trojans have become a significant threat to computing reliability and data security in reconfigurable hardware. One of the most effective techniques of run-time detection and recovery is based on Triple Modular Redundancy (TMR) mechanism;however, this mechanism causes a large resource overhead because the protected circuit needs to be totally duplicated twice for detection stage and decision stage. This paper proposes a novel methodology called Optimal Partitioning Triple Modular Redundancy (OPTMR) that optimizes the data flow graph (DFG) partitioning to reduce the total overhead of TMR. The mathematical relationship between DFG partitioning and total overhead is discussed and the principle of DFG node division is presented. The efficiency and effectiveness of OPTMR is verified through simulation on an actual Field Programmable Gate Array (FPGA) device.
As the increasing demand for large and complex signal processing requirements, the efficient and fast design of signal processing circuits becomes an important issues. Agile design offers a new approach for rapid hard...
详细信息
ISBN:
(纸本)9798350352047;9798350352030
As the increasing demand for large and complex signal processing requirements, the efficient and fast design of signal processing circuits becomes an important issues. Agile design offers a new approach for rapid hardware design cycles. While this approach is a standard for software design, how to adapt it to hardware design properly remains an open question. In our work, we propose a framework for agile software and hardware co-design named graphRTL. The tool addresses the challenge of designing hardware for Digital Signal Processing (DSP). Instead of direct coding and debugging iteration, we employ data flow graphs (DFG) and control flowgraphs (CFG) to automatically generate RTL code. The input to graphRTL is the flowgraph of the designed circuit. Subsequently, the tool checks the graphs, reconstructs it, and then translates it into a configuration file for the compiler. Finally, the compiler autonomously generates the corresponding software to hardware code and RTL code. Compared to the traditional design route, graphRTL enhances design efficiency and broadens the design space. In our experiments, we achieved up to an 70% reduction in design time while maintaining a 5 to 10% reduction in hardware overhead for the designed circuits.
High throughput issue is one of the major obstacles for opening up the new big data stream computing era. A high throughput big data stream computing system is need, optimising the data flow graph is an important way ...
详细信息
High throughput issue is one of the major obstacles for opening up the new big data stream computing era. A high throughput big data stream computing system is need, optimising the data flow graph is an important way to offer a high throughput computing environment in big data stream computing system. In this paper, the definition of data flow graph in big data stream computing is given and the properties of data flow graph are systematically analysed by referring to the stream computing theories. A series of optimisation strategies for data flow graph in big data stream computing environments are put forward, all those strategies will greatly improve the structure of data flow graph, and provide a high throughput environment for big data stream computing.
data flow graphs are a program representation in machine learning, big data analytics, signal processing, and, increasingly, networking, where graph nodes correspond to processing primitives and graph edges describe c...
详细信息
data flow graphs are a program representation in machine learning, big data analytics, signal processing, and, increasingly, networking, where graph nodes correspond to processing primitives and graph edges describe control flow. To improve CPU cache locality and exploit data-level parallelism, nodes usually process data in batches. Batchy is a scheduler for data flow graph based packet processing engines, which uses controlled queuing to reconstruct fragmented batches inside a data flow graph in accordance with strict Service-Level Objectives (SLOs). Earlier work showed that Batchy yields up to 10x performance improvement in real-life use cases, thanks to maximally exploiting batch processing gains. Batchy, however, is fundamentally restricted to single-threaded execution. In this paper, we generalize Batchy to parallel execution on multiple CPU cores. We extend the analytical model to the parallel setting and present a primal decomposition framework, where each core runs an unmodified Batchy controller to schedule batch-processing on a subset of the data flow graph, orchestrated by a master controller that distributes the delay-SLOs across the cores using subgradient search. Evaluations on a real software switch provide experimental evidence that our decomposition framework produces 2.5x performance improvement while accurately satisfying delay SLOs that are otherwise not feasible with single-core Batchy.
In recent years, Convolution Neural Networks (CNN) have been extensively adopted in broad Artificial Intelligence (AI) applications and have demonstrated ability and effectiveness in solving learning problems. However...
详细信息
In recent years, Convolution Neural Networks (CNN) have been extensively adopted in broad Artificial Intelligence (AI) applications and have demonstrated ability and effectiveness in solving learning problems. However, developing high-performance hardware accelerators on Field Programmable Gate Array (FPGA) for CNNs often demands skills in hardware design and verification, accurate distribution localization, and long development cycles. Besides, the depth of CNN architectures increases by reusing and replicating several layers. In this work, we take advantage of the replication of CNN layers to achieve improvement in design performance and productivity. We propose a programming flow for CNNs on FPGA to generate high-performance accelerators by assembling CNN pre-implemented components as a puzzle based on the graph topology. Using pre-implemented components allows us to use minimum of resources, predict the performance, and gain in productivity since there is no need to synthesize any Hardware Description Language (HDL) source code. Furthermore, the pre-implemented components are reused for different range of applications, reducing the engineering time. Through prototyping, we demonstrate the viability and relevance of our approach. Experiments show a productivity improvement of up to 69% compared to a traditional FPGA implementation while achieving over 1.75x higher Fmax with lower resources and higher energy efficiency.(C) 2022 Elsevier Inc. All rights reserved.
Privacy and security flaws in apps are commonly detected by static and dynamic analysis approaches. However, the realistic impact rating for detected flaws is often limited. Static approaches lack runtime information ...
详细信息
ISBN:
(数字)9783031311086
ISBN:
(纸本)9783031311079;9783031311086
Privacy and security flaws in apps are commonly detected by static and dynamic analysis approaches. However, the realistic impact rating for detected flaws is often limited. Static approaches lack runtime information and dynamic analysis miss program structure information. We aim to build a dynamic analysis environment and rate the flaw impact based on the dataflow context. We correlate the traced API call chains with data sources and sinks of processed information. This way, the actual runtime information is used and program structures for significant dataflows can be reconstructed. Therefore, this publication proposes a method for collecting execution traces based on automated function hooking and mechanisms to create and analyze data flow graphs from these traces. We demonstrate the scalability of our privacy and security analysis by automatically analyzing and evaluating the top 1000 free apps from Google Play. Manual app analysis and damn vulnerable app projects prove the high quality results of our automated approach during evaluation.
Deep learning (DL) is attracting considerable attention in the design of communication systems. This paper derives a deep unfolded conjugate gradient (CG) architecture for large-scale multiple-input multiple-output de...
详细信息
Deep learning (DL) is attracting considerable attention in the design of communication systems. This paper derives a deep unfolded conjugate gradient (CG) architecture for large-scale multiple-input multiple-output detection. The proposed technique combines the advantages of a model-driven approach in readily incorporating domain knowledge and deep learning in effective parameters learning. The parameters are trained via backpropagation over a data flow graph inspired from the iterative conjugate gradient method. We derive the closed-form expressions for the gradients for parameters training and discuss early results on the performance in a statistically identical and independent distributed channel where the training overhead is considerably low. It is worth noting that the loss function is based on the residual error that is not an explicit function of the desired signal, which makes the proposed algorithm blind. As an initial framework, we will point to the inherent issues and future directions.
The utilization of computation resources and reconfiguration time has a large impact on reconfiguration system performance. In order to promote the performance, a dynamical self-reconfigurable mechanism for data-drive...
详细信息
The utilization of computation resources and reconfiguration time has a large impact on reconfiguration system performance. In order to promote the performance, a dynamical self-reconfigurable mechanism for data-driven cell array is proposed. Cells can be fired only when the needed data arrives, and cell array can be worked on two modes: fixed execution and reconfiguration. On reconfiguration mode, cell function and dataflow direction are changed automatically at run time according to contexts. Simultaneously using an H-tree interconnection network, through pre-storing multiple application mapping contexts in reconfiguration buffer, multiple applications can execute concurrently and context switching time is the minimal. For verifying system performance, some algorithms are selected for mapping onto the proposed structure, and the amount of configuration contexts and execution time are recorded for statistical analysis. The results show that the proposed self-reconfigurable mechanism can reduce the number of contexts efficiently, and has a low computing time.
暂无评论