Flow entry conflict detection and resolution scheme for software-defined networking

2021 ◽  
pp. 002072092199823
Author(s):  
Lie Tang ◽  
You Fu ◽  
Youwen Zeng ◽  
Zhihao Li ◽  
Shuangqing Li
Keyword(s):  
Software Defined Networking ◽  
Detection Algorithm ◽  
Conflict Detection ◽  
Field Range ◽  
Conflict Situations ◽  
Different Types ◽  
Resolution Scheme ◽  
Matching Field ◽  
Time And Space Complexity ◽  
Correct Expression

Software-defined networking (SDN) has the ability to flexibly configure the network and is widely used in various scenarios. In SDN, different applications require the controller to deploy corresponding flow entries to maintain the effectiveness of network. However, the matching field range covered by entries of different applications may overlap, and when actions specified by these overlapping entries are inconsistent, conflicts may occur. Such conflicts may cause the flow to match the wrong entry, thereby affecting the correct expression of application functions. The scheme we proposed in this paper will be able to detect and resolve conflicts between flow entries. Firstly, we discussed the causes of conflicts, and then classified various conflict situations. This classification will help us to adopt different ways of resolving different types of conflicts and make the resolution of conflicts more targeted. Next, we propose a conflict detection algorithm based on B+ tree. This algorithm can detect different types of conflicts. According to theoretical proof, the use of B+ tree compared with other similar structures better in the performance of the time and space complexity. Finally, for the detected conflicting entries, we propose a conflict resolution scheme based on the failure degree of the flow entry according to the characteristics of SDN services tending to be more detailed. Through experimental evaluation, our scheme can effectively detect and resolve conflicts with lower overhead.

A dual U-Net algorithm for automating feature extraction from satellite imagery

2021 ◽  
pp. 154851292098354
Author(s):  
Samuel Humphries ◽  
Trevor Parker ◽  
Bryan Jonas ◽  
Bryan Adams ◽  
Nicholas J Clark
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Satellite Images ◽  
Detection Algorithm ◽  
Military Operations ◽  
Detection Algorithms ◽  
Us Military ◽  
Standard Product ◽  
Different Types ◽  
Road Intersections

Quick identification of building and roads is critical for execution of tactical US military operations in an urban environment. To this end, a gridded, referenced, satellite images of an objective, often referred to as a gridded reference graphic or GRG, has become a standard product developed during intelligence preparation of the environment. At present, operational units identify key infrastructure by hand through the work of individual intelligence officers. Recent advances in Convolutional Neural Networks, however, allows for this process to be streamlined through the use of object detection algorithms. In this paper, we describe an object detection algorithm designed to quickly identify and label both buildings and road intersections present in an image. Our work leverages both the U-Net architecture as well the SpaceNet data corpus to produce an algorithm that accurately identifies a large breadth of buildings and different types of roads. In addition to predicting buildings and roads, our model numerically labels each building by means of a contour finding algorithm. Most importantly, the dual U-Net model is capable of predicting buildings and roads on a diverse set of test images and using these predictions to produce clean GRGs.

A Data Analytics Framework for Analyzing the Effect of Frac Hits on Parent Well Production

2021 ◽  
Author(s):  
Yifei Guo ◽  
Pradeepkumar Ashok ◽  
Eric van Oort ◽  
Ross Patterson ◽  
Dandan Zheng ◽  
...  
Keyword(s):  
Horizontal Wells ◽  
Detection Algorithm ◽  
Clear Understanding ◽  
Pressure Data ◽  
Daily Production ◽  
Well Production ◽  
Different Types ◽  
Shale Formation ◽  
Well Completions

Abstract Well interference, which is commonly referred to as frac hits, has become a significant factor affecting production in fractured horizontal shale wells with the increase in infill drilling in recent years. Today, there is still no clear understanding on how frac hits affect production. This paper aims to develop a process to automatically identify the different types of frac hits and to determine the effect of stage-to-well distance and frac hit intensity on long-term parent well production. First, child well completions data and parent well pressure data are processed by a frac hit detection algorithm to automatically identify different frac hit intensities and duration within each stage. This algorithm classifies frac hits based on the magnitude of the differential pressure spikes. The frac stage to parent well distance is also calculated. Then, we compare the daily production trend before and after the frac hits to determine the severity of its influence on production. Finally, any evident correlations between the stage-to-well distance, frac hit intensity and production change are identified and investigated. This work utilizes 3 datasets covering 22 horizontal wells in the Bakken Formation and 37 horizontal wells in the Eagle Ford Shale Formation. These sets included well trajectories, child well completions data, parent well pressure data and parent well production data. The frac hit detection algorithm developed can accurately detect frac hits in the available dataset with minimal false alerts. The data analysis results show that frac hit severity (production response) and intensity (pressure response) are not only affected by the distance between parent and child wells, but also affected by the directionality of the wells. Parent wells tend to experience more frac hits from the child frac stages with smaller direction angles and shorter stage-to-parent distances. Formation stress change with time is another factor that affects frac hit intensity. Depleted wells are more susceptible to frac hits even if they are further from the child wells. Also, we observe frac hits in parent wells due to a stimulation of a child well in a different shale formation. This paper presents a novel automated frac hit detection algorithm to quickly identify different types of frac hits. This paper also presents a novel way of carrying out production analysis to determine whether frac hits in a well have positive or negative influence long-term production. Additionally, the paper introduces the concept of the stage-to-well distance as a more accurate metric for analyzing the influence of frac hits on production.

scholarly journals Attribute-Guard: Attribute-Based Flow Access Control Framework in Software-Defined Networking

2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Xianwei Zhu ◽  
ChaoWen Chang ◽  
Qin Xi ◽  
ZhiBin Zuo
Keyword(s):  
Access Control ◽  
Software Defined Networking ◽  
Authentication Protocol ◽  
Network Characteristics ◽  
Fine Grained ◽  
Control Framework ◽  
Data Plane ◽  
Data Validity ◽  
The One ◽  
Matching Field

Software-defined networking (SDN) decouples the control plane from the data plane, offering flexible network configuration and management. Because of this architecture, some security features are missing. On the one hand, because the data plane only has the packet forwarding function, it is impossible to effectively authenticate the data validity. On the other hand, OpenFlow can only match based on network characteristics, and it is impossible to achieve fine-grained access control. In this paper, we aim to develop solutions to guarantee the validity of flow in SDN and present Attribute-Guard, a fine-grained access control and authentication scheme for flow in SDN. We design an attribute-based flow authentication protocol to verify the legitimacy of the validity flow. The attribute identifier is used as a matching field to define a forwarding control. The flow matching based on the attribute identifier and the flow authentication protocol jointly implement fine-grained access control. We conduct theoretical analysis and simulation-based evaluation of Attribute-Guard. The results show that Attribute-Guard can efficiently identify and reject fake flow.

scholarly journals Identification of conflicting questions in the PARES system

2012 ◽  
Vol 13 (3) ◽  
pp. 297 ◽  
Author(s):  
Avgoustos Tsinakos ◽  
Ioannis Kazanidis
Keyword(s):  
Continuing Education ◽  
Statistical Evaluation ◽  
Academic Staff ◽  
Vector Space Model ◽  
Detection Algorithm ◽  
Conflict Detection ◽  
Knowledge Assessment ◽  
Space Model ◽  
Practice Tests ◽  
Test Suites

<p>Student testing and knowledge assessment is a significant aspect of the learning process. In a number of cases, it is expedient not to present the exact same test to all learners all the time (Pritchett, 1999). This may be desired so that cheating in the exam is made harder to carry out or so that the learners can take several practice tests on the same subject as part of the course.</p><p><br />This study presents an e-testing platform, namely PARES, which aims to provide assessment services to academic staff by facilitating the creation and management of question banks and powering the delivery of nondeterministically generated test suites. PARES uses a conflict detection algorithm based on the vector space model to compute the similarity between questions and exclude questions which are deemed to have an unacceptably large similarity from appearing in the same test suite. The conflict detection algorithm and a statistical evaluation of its accuracy are presented. Evaluation results show that PARES succeeds in detecting question types at about 90% and its efficiency can be further increased through continuing education and enrichment of the system’s correlation vocabulary.<br /><br /></p><p> </p>

Aircraft Conflict Risk Assessment Based on Particle Filter

2014 ◽  
Vol 602-605 ◽  
pp. 3416-3420
Author(s):  
Wen Peng Zhai ◽  
Hao Wu ◽  
Lan Ma
Keyword(s):  
Particle Filter ◽  
Early Stage ◽  
Random Perturbation ◽  
Kinematic Model ◽  
Detection Algorithm ◽  
Conflict Detection ◽  
Collision Risk ◽  
Free Flight ◽  
Risk Probability ◽  
Particle Filter Algorithm

Free flight is a method to resolve airspace congestion problem, but raise safety problem. In this paper, with the influence of wind and the presence of positioning error, the model of conflict detection based on particle filter algorithm is presented. According to the flight kinematic model with the influence of random factors, the target trajectory is generated. The particle filter algorithm is used for estimating the real flight trajectory. The flight collision risk probability is calculated. By simulation calculation, the conflict detection with particle filter algorism improves the accuracy of collision risk probability estimation. The results show that the particle filter conflict detection algorithm reduces the estimation and conflict detection error caused by random perturbation. The method can be applied to identify conflict in the early stage in the study of flight free flight.

Object detection and mapping for service robot tasks

Robotica ◽  
2007 ◽  
Vol 25 (2) ◽  
pp. 175-187 ◽  
Author(s):  
Staffan Ekvall ◽  
Danica Kragic ◽  
Patric Jensfelt
Keyword(s):  
Object Detection ◽  
Recognition System ◽  
Detection Algorithm ◽  
Semantic Knowledge ◽  
Service Robot ◽  
Map Building ◽  
Extensive Evaluation ◽  
Localization And Mapping ◽  
Different Types ◽  
Robot Tasks

SUMMARYThe problem studied in this paper is a mobile robot that autonomously navigates in a domestic environment, builds a map as it moves along and localizes its position in it. In addition, the robot detects predefined objects, estimates their position in the environment and integrates this with the localization module to automatically put the objects in the generated map. Thus, we demonstrate one of the possible strategies for the integration of spatial and semantic knowledge in a service robot scenario where a simultaneous localization and mapping (SLAM) and object detection recognition system work in synergy to provide a richer representation of the environment than it would be possible with either of the methods alone. Most SLAM systems build maps that are only used for localizing the robot. Such maps are typically based on grids or different types of features such as point and lines. The novelty is the augmentation of this process with an object-recognition system that detects objects in the environment and puts them in the map generated by the SLAM system. The metric map is also split into topological entities corresponding to rooms. In this way, the user can command the robot to retrieve a certain object from a certain room. We present the results of map building and an extensive evaluation of the object detection algorithm performed in an indoor setting.

BFX: DIAGNOSING CONFLICTING REQUIREMENTS IN CONSTRAINT-BASED RECOMMENDATION

2011 ◽  
Vol 20 (02) ◽  
pp. 297-312 ◽  
Author(s):  
MONIKA SCHUBERT ◽  
ALEXANDER FELFERNIG
Keyword(s):  
Performance Evaluation ◽  
Structural Properties ◽  
Detection Algorithm ◽  
Conflict Detection ◽  
Comparative Performance ◽  
Detection Algorithms ◽  
Minimal Sets ◽  
The Given

When interacting with constraint-based recommender applications, users describe their preferences with the goal of identifying the products that fit their wishes and needs. In such a scenario, users are repeatedly adapting and changing their requirements. As a consequence, situations occur where none of the products completely fulfils the given set of requirements and users need a support in terms of an indicator of minimal sets of requirements that need to be changed in order to be able to find a recommendation. The identification of such minimal sets relies heavily on the existence of (minimal) conflict sets. In this paper we introduce BFX (Boosted FastXplain), a conflict detection algorithm which exploits the basic structural properties of constraint-based recommendation problems. BFX shows a significantly better performance compared to existing conflict detection algorithms. In order to demonstrate the performance of BFX, we report the results of a comparative performance evaluation.

scholarly journals Multivariable Heuristic Approach to Intrusion Detection in Network Environments

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 776
Author(s):  
Marcin Niemiec ◽  
Rafał Kościej ◽  
Bartłomiej Gdowski
Keyword(s):  
Intrusion Detection ◽  
Heuristic Algorithm ◽  
Detection Algorithm ◽  
Intrusion Detection Systems ◽  
The Internet ◽  
Detection Thresholds ◽  
Detection Systems ◽  
Different Types ◽  
Ongoing Development ◽  
Network Environments

The Internet is an inseparable part of our contemporary lives. This means that protection against threats and attacks is crucial for major companies and for individual users. There is a demand for the ongoing development of methods for ensuring security in cyberspace. A crucial cybersecurity solution is intrusion detection systems, which detect attacks in network environments and responds appropriately. This article presents a new multivariable heuristic intrusion detection algorithm based on different types of flags and values of entropy. The data is shared by organisations to help increase the effectiveness of intrusion detection. The authors also propose default values for parameters of a heuristic algorithm and values regarding detection thresholds. This solution has been implemented in a well-known, open-source system and verified with a series of tests. Additionally, the authors investigated how updating the variables affects the intrusion detection process. The results confirmed the effectiveness of the proposed approach and heuristic algorithm.

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