Discovering software vulnerabilities using data-flow analysis and machine learning

2018 ◽  
Author(s):  
Jorrit Kronjee ◽  
Arjen Hommersom ◽  
Harald Vranken
Keyword(s):  
Machine Learning ◽  
Data Flow ◽  
Flow Analysis ◽  
Data Flow Analysis ◽  
Software Vulnerabilities ◽  
Using Data

Packet flow analysis in IP networks using data-flow analysis

2012 ◽  
Author(s):  
Raghavan Komondoor ◽  
K. Vasanta Lakshmi ◽  
Deva P. Seetharam ◽  
Sudha Balodia
Keyword(s):  
Data Flow ◽  
Flow Analysis ◽  
Ip Networks ◽  
Data Flow Analysis ◽  
Using Data

MAINTAINING DATA DEPENDENCIES ACROSS BPEL PROCESS FRAGMENTS

2008 ◽  
Vol 17 (03) ◽  
pp. 259-282 ◽  
Author(s):  
RANIA KHALAF ◽  
OLIVER KOPP ◽  
FRANK LEYMANN
Keyword(s):  
Collective Behavior ◽  
Data Flow ◽  
Continuous Process ◽  
Flow Analysis ◽  
Control Flow ◽  
Continuous Process Improvement ◽  
Data Dependencies ◽  
Data Flow Analysis ◽  
Data Links ◽  
Using Data

Continuous process improvement (CPI) may require a BPEL process to be split amongst different participants. In this paper, we enable splitting standard BPEL — without requiring any new middleware for the case of flat flows. The solution also supports splitting loops and scopes that have compensation and/or fault handlers. When splitting loops and scopes, we extend existing Web services standards and frameworks in a standard compliant manner in order to support the resulting split control (not data) between the fragments. Data dependencies, however, are handled directly using BPEL constructs placed in the fragments even for split loops and scopes. We present a solution that uses a BPEL process, partition information, and results of data-flow analysis to produce a BPEL process for each participant. The collective behavior of these participant processes recreates the control and data flow of the non-split process. Previous work presented process splitting using a variant of BPEL where data flow is modeled explicitly using "data links". We reuse the control flow aspect from that work as well as the control flow aspect from our work on splitting loops and scopes, focusing in this paper on maintaining the data dependencies in standard BPEL.

A New Method for Code Optimization

2014 ◽  
Vol 599-601 ◽  
pp. 1346-1349
Author(s):  
Hai Lin ◽  
Bao Liang Mu
Keyword(s):  
Data Flow ◽  
Source Code ◽  
Flow Analysis ◽  
Simultaneous Equations ◽  
New Method ◽  
Code Optimization ◽  
Data Flow Analysis ◽  
Using Data ◽  
Set Up

In this paper, we propose a new method for code optimization. This new method is based on backward data flow analysis, and the optimization is done on the source code level. First we design a tiny language to experiment with. Then, using data flow analysis, we set up some simultaneous equations and collect the liveness information for all program points. This is the basis for the optimization. Using this technique, some code can be safely eliminated without affecting the semantics of the code segment.

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