检索结果-内蒙古大学图书馆

21st Conference on Detection of Intrusions and Malware and Vulnerability Assessment (DIMVA)

作者： Bockenek, Joshua Verbeek, Freek Ravindran, Binoy Virginia Tech Blacksburg VA 24061 USA Open Univ Netherlands Heerlen Netherlands

ISBN: (纸本)9783031641701;9783031641718

Standard control flow graphs (CFGs) extracted from binaries by state-of-the-art disassembly/decompilation tools do not include information about exception-related control flow. However, such information is useful when statically analyzing programs that utilize structured exceptions. To fill that gap, we propose the concept of Exceptional Interprocedural control flow graphs (EICFGs). These graphs extend traditional CFGs by adding edges for stack unwinding, frame cleanup, and try/catch behavior caused by thrown exceptions. We provide an approach for generating EICFGs from x86-64 binaries featuring C++ exceptions. The approach is based on symbolically executing an abstract semantics that includes binary-level exception-related function calls. We validated our abstract semantics by generating concrete test cases that were then evaluated using real binaries. We applied an implementation of our approach to 341 off-the-shelf x86-64 binaries compiled from C++ as well as C and Fortran source code. From those binaries, we identified 2574 unique throws and successfully resolved the exceptional control flow for every one of them. We show that resolving throws leads to increased instruction reachability and uncovers edges not found by state-of-the-art tools such as Ghidra.

关键词： C plus plus exceptions Binary Analysis control flow graphs

来源：评论

学校读者我要写书评

暂无评论

Android Malware Analysis Approach Based on control flow graphs and Machine Learning Algorithms 4

Android Malware Analysis Approach Based on Control Flow Grap...

引用

4th International Symposium on Digital Forensic and Security (ISDFS)

作者： Atici, Mehmet Ali Sagiroglu, Seref Dogru, Ibrahim Alper Gazi Univ Fac Engn Dept Comp Engn Ankara Turkey

ISBN: (纸本)9781467398657

Smart devices from smartphones to wearable computers today have been used in many purposes. These devices run various mobile operating systems like Android, iOS, Symbian, Windows Mobile, etc. Since the mobile devices are widely used and contain personal information, they are subject to security attacks by mobile malware applications. In this work we propose a new approach based on control flow graphs and machine learning algorithms for static Android malware analysis. Experimental results have shown that the proposed approach achieves a high classification accuracy of 96.26% in general and high detection rate of 99.15% for DroidKungfu malware families which are very harmful and difficult to detect because of encrypting the root exploits, by reducing data dimension significantly for real time analysis.

关键词： Android mobile security malware static analysis control flow graphs machine learning

来源：评论

学校读者我要写书评

暂无评论

Convolutional Neural Networks over control flow graphs for Software Defect Prediction 29

Convolutional Neural Networks over Control Flow Graphs for S...

引用

29th Annual IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Anh Viet Phan Minh Le Nguyen Lam Thu Bui Japan Adv Inst Informat Technol Nomi 9231211 Japan Le Quy Don Tech Univ Hanoi Vietnam

ISBN: (纸本)9781538638767

Existing defects in software components is unavoidable and leads to not only a waste of time and money but also many serious consequences. To build predictive models, previous studies focus on manually extracting features or using tree representations of programs, and exploiting different machine learning algorithms. However, the performance of the models is not high since the existing features and tree structures often fail to capture the semantics of programs. To explore deeply programs' semantics, this paper proposes to leverage precise graphs representing program execution flows, and deep neural networks for automatically learning defect features. Firstly, control flow graphs are constructed from the assembly instructions obtained by compiling source code;we thereafter apply multi-view multi-layer directed graph-based convolutional neural networks (DGCNNs) to learn semantic features. The experiments on four real-world datasets show that our method significantly outperforms the baselines including several other deep learning approaches.

关键词： Software Defect Prediction control flow graphs Convolutional Neural Networks

来源：评论

学校读者我要写书评

暂无评论

Evading control flow graph based GNN malware detectors via active opcode insertion method with maliciousness preserving

引用

SCIENTIFIC REPORTS 2025年第1期15卷 1-19页

作者： Peng, Hao Yu, Zehao Zhao, Dandan Ding, Zhiguo Yang, Jieshuai Zhang, Bo Han, Jianming Zhang, Xuhong Ji, Shouling Zhong, Ming Zhejiang Normal Univ Coll Comp Sci & Technol Jinhua Zhejiang Peoples R China Zhejiang Normal Univ Key Lab Intelligent Educ Technol & Applicat Zhejia Jinhua 321004 Peoples R China Shanghai Jiao Tong Univ Sch Cyber Sci & Engn Shanghai Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou Zhejiang Peoples R China Georgia Inst Technol Elect & Comp Engn Atlanta GA USA

With the continuous advancement of machine learning, numerous malware detection methods that leverage this technology have emerged, presenting new challenges to the generation of adversarial malware. Existing function-preserving adversarial attacks fall short of effectively modifying portable executable (PE) malware control flow graphs (CFGs), thereby failing to bypass the graph neural network (GNN) models that utilize CFGs for detection. To solve this issue, we introduce a novel base modification method called active opcode insertion, which modifies PE CFGs while preserving functionality by inserting a processed sequence of benign and jump opcodes to connect with the original base block. Using reinforcement learning, MalAOI identifies optimal insertion points and benign opcode sequences to autonomously generate adversarial malware that evades GNN model detection. We tested our approach on the BODMAS and SOREL-20M datasets, and the results demonstrate that MalAOI-generated adversarial malware achieves an average evasion rate of 93.73% against the GNN detection model, with only 12.87% increase in byte size.

关键词： Malware Detection Reinforcement Learning Graph Neural Networks control flow graphs Function-preserving

来源：评论

学校读者我要写书评

暂无评论

Cognitive Complexity and Graph Convolutional Approach Over control flow Graph for Software Defect Prediction

引用

IEEE ACCESS 2022年 10卷 108870-108894页

作者： Gupta, Mansi Rajnish, Kumar Bhattacharjee, Vandana Birla Inst Technol Mesra Dept Comp Sci & Engn Ranchi 835215 Bihar India

The software engineering community is working to develop reliable metrics to improve software quality. It is estimated that understanding the source code accounts for 60% of the software maintenance effort. Cognitive informatics is important in quantifying the degree of difficulty or the efforts made by developers to understand the source code. Several empirical studies were conducted in 2003 to assign cognitive weights to each possible basic control structure of software, and these cognitive weights are used by several researchers to evaluate the cognitive complexity of software systems. In this paper, an effort has been made to categorize the control flow graphs (CFGs) nodes according to their node features. In our case, we extracted seven unique features from the program, and each unique feature was assigned an integer value that we evaluated through Cognitive Complexity Measures (CCMs). We then incorporated CCMs' results as a node feature value in CFGs and generated the same based on the node connectivity for a graph. In order to obtain the feature representation of the graph, a node vector matrix is then created for the graph and passed to the Graph Convolutional Network (GCN). We prepared our data sets using GCN output and then built Deep Neural Network Defect Prediction (DNN-DP) and Convolutional Neural Network Defect Prediction (CNN-DP) models to predict software defects. The Python programming language is used, along with Keras and Tensorflow. Three hundred twenty Python programs were written by our talented UG and PG students, and all experiments were carried out during laboratory classes. Together with three skilled lab programmers, they compiled and ran each individual program and detected defect/no-defect programs before categorizing them into three different classes, namely Simple, Medium, and Complex programs. Accuracy, Receiver Operating Characteristics (ROC), Area Under Curve (AUC), F-measure, Precision and hyper-parameter tuning procedures are used

关键词： Software reliability Complexity theory Deep learning Predictive models Convolutional neural networks Neural networks Feature extraction Cognition Informatics Software quality Basic control structures cognitive complexity measures control flow graphs graph convolutional network neural network software defect prediction cognitive informatics

来源：评论

学校读者我要写书评

暂无评论

Making graphs reducible with controlled node splitting

引用

ACM TRANSACTIONS ON PROGRAMMING LANGUAGES AND SYSTEMS 1997年第6期19卷 1031-1052页

作者： Janssen, J Corporaal, H Delft Univ Technol Dept Elect Engn NL-2600 GA Delft Netherlands

Several compiler optimizations, such as data, flow analysis, the exploitation of instruction-level parallelism (ILP), loop transformations, and memory disambiguation, require programs with reducible control flow graphs. However, not all programs satisfy this property. A new method for transforming irreducible control flow graphs to reducible control how graphs, called controlled Node Splitting (CNS), is presented. CNS duplicates nodes of the control flow graph to obtain reducible control flow graphs. CNS results in a minimum number of splits and a minimum number of duplicates. Since the computation time to find the optimal split sequence is large, a heuristic has been developed. The results of this heuristic are close to the optimum. Straightforward application of node splitting resulted in an average code size increase of 235% per procedure of our benchmark programs. CNS with the heuristic limits this increase to only 3%. The impact on the total code size of the complete programs is 13.6% for a straightforward application of node splitting. However, when CNS is used, with the heuristic the average growth in code size of a complete program dramatically reduces to 0.2%.

关键词： compilation control flow graphs instruction-level parallelism irreducibility node splitting reducibility

来源：评论

学校读者我要写书评

暂无评论

Assertion-based slicing and slice graphs

引用

FORMAL ASPECTS OF COMPUTING 2012年第2期24卷 217-248页

作者： Barros, Jose Bernardo da Cruz, Daniela Henriques, Pedro Rangel Pinto, Jorge Sousa Univ Minho Dept Informat CCTC P-4710057 Braga Portugal

This paper revisits the idea of slicing programs based on their axiomatic semantics, rather than using criteria based on control/data dependencies. We show how the forward propagation of preconditions and the backward propagation of postconditions can be combined in a new slicing algorithm that is more precise than the existing specification-based algorithms. The algorithm is based on (a) a precise test for removable statements, and (b) the construction of a slice graph, a program control flow graph extended with semantic labels and additional edges that "short-circuit" removable commands. It improves on previous approaches in two aspects: it does not fail to identify removable commands;and it produces the smallest possible slice that can be obtained (in a sense that will be made precise). Iteration is handled through the use of loop invariants and variants to ensure termination. The paper also discusses in detail applications of these forms of slicing, including the elimination of (conditionally) unreachable and dead code, and compares them to other related notions.

关键词： Program slicing Program analysis Verification conditions control flow graphs

来源：评论

学校读者我要写书评

暂无评论

A Buffered Genetic Algorithm for Automated Branch Coverage in Software Testing

引用

JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2019年第2期35卷 245-273页

作者： Manikumar, T. Kumar, A. John Sanjeev RVS Coll Engn Dept Comp Applicat Dindigul 624005 Tamil Nadu India Thiagarajar Coll Engn Dept Comp Applicat Madurai 625015 Tamil Nadu India

Each and every software product has to be tested for assess its quality, which is time-consuming if it is performed manually. Moreover, it is difficult to generate all possible data for finite testing set. Search based Software Testing (SBST) are used to resolve this issue by utilizing metaheuristic algorithms to automate the test data generation. Hence, an efficient test data set could be generated with minimum cost. Among many metaheuristic algorithms, Genetic Algorithm (GA) is widely used for test data generation. This research work implements GA for generating test data to execute all the branches in a program. In the literature, existing approaches for test data generation using genetic algorithms are starts with random test data and find the optimum test data for a targeted branch. Then the entire GA process will be repeated to find the test data for the next target branch and it continues for all the target branches. In this a paper, a novel GA approach with a small buffer space is proposed for automated test data generation for branch coverage. When GA is searching test data for a particular target branch heuristically, it may reach the other target branches, if so happen, then those test data will get stored into the buffer space hence it is not necessary to run GA to cover that branch. Thus the Buffered Genetic Algorithm (BGA) approach outperforms the other GA based automated test data generation approaches in terms of number of iterations and search effectiveness. The proposed approach employs control flow graph to traverse and predicate the branch coverage. Seven benchmark programs are instrumented to evaluate performance of the proposed BGA based approach.

关键词： software testing automatic test data generation genetic algorithm control flow graphs buffered genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Horn clauses as an intermediate representation for program analysis and transformation

引用

THEORY AND PRACTICE OF LOGIC PROGRAMMING 2015年第4-5期15卷 526-542页

作者： Gange, Graeme Navas, Jorge A. Schachte, Peter Sondergaard, Harald Stuckey, Peter J. Univ Melbourne Dept Comp & Informat Syst Melbourne Vic 3010 Australia NASA Ames Res Ctr Moffett Field CA 94035 USA

Many recent analyses for conventional imperative programs begin by transforming programs into logic programs, capitalising on existing LP analyses and simple LP semantics. We propose using logic programs as an intermediate program representation throughout the compilation process. With restrictions ensuring determinism and single-modedness, a logic program can easily be transformed to machine language or other low-level language, while maintaining the simple semantics that makes it suitable as a language for program analysis and transformation. We present a simple LP language that enforces determinism and single-modedness, and show that it makes a convenient program representation for analysis and transformation.

关键词： compilers control flow graphs intermediate representation program analysis and transformation SSA

来源：评论

学校读者我要写书评

暂无评论

Mining execution relations for crosscutting concerns

引用

IET SOFTWARE 2008年第2期2卷 65-78页

作者： Krinke, J. FernUniv Hagen Germany

Aspect mining tries to identify crosscutting concerns in the code of existing systems and thus supports their adaption to an aspect-oriented design. A semi-automatic static aspect mining approach is described, where constraints,such as the requirement that the patterns have to exist in different calling contexts. Two case studies done with the implemented tool show that many discovered candidates for crosscutting concerns are instances of delegation and should not be refactored into aspects. More generally, it is shown that aspect mining techniques need a way to distinguish between delegation and superimposed behaviour.

关键词： Data handling techniques aspect-oriented design data mining control flow graphs recurring execution patterns Knowledge engineering techniques Object-oriented programming object-oriented programming semi-automatic static aspect mining crosscutting concerns

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：