scholarly journals A Framework to Test Resistency of Detection Algorithms for Stepping-Stone Intrusion on Time-Jittering Manipulation

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lixin Wang ◽  
Jianhua Yang ◽  
Michael Workman ◽  
Peng-Jun Wan
Keyword(s):  
Data Stream ◽  
Detection Method ◽  
Detection Algorithm ◽  
Efficient Algorithms ◽  
Detection Methods ◽  
The Internet ◽  
Stepping Stones ◽  
Stepping Stone ◽  
Detection Algorithms ◽  
A Chain

Hackers on the Internet usually send attacking packets using compromised hosts, called stepping-stones, in order to avoid being detected and caught. With stepping-stone attacks, an intruder remotely logins these stepping-stones using programs like SSH or telnet, uses a chain of Internet hosts as relay machines, and then sends the attacking packets. A great number of detection approaches have been developed for stepping-stone intrusion (SSI) in the literature. Many of these existing detection methods worked effectively only when session manipulation by intruders is not present. When the session is manipulated by attackers, there are few known effective detection methods for SSI. It is important to know whether a detection algorithm for SSI is resistant on session manipulation by attackers. For session manipulation with chaff perturbation, software tools such as Scapy can be used to inject meaningless packets into a data stream. However, to the best of our knowledge, there are no existing effective tools or efficient algorithms to produce time-jittered network traffic that can be used to test whether an SSI detection method is resistant on intruders’ time-jittering manipulation. In this paper, we propose a framework to test resistency of detection algorithms for SSI on time-jittering manipulation. Our proposed framework can be used to test whether an existing or new SSI detection method is resistant on session manipulation by intruders with time-jittering.

scholarly journals Mining Network Traffic with the k -Means Clustering Algorithm for Stepping-Stone Intrusion Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lixin Wang ◽  
Jianhua Yang ◽  
Xiaohua Xu ◽  
Peng-Jun Wan
Keyword(s):  
Intrusion Detection ◽  
Network Traffic ◽  
Efficient Algorithm ◽  
Clustering Algorithm ◽  
Detection Algorithm ◽  
Target System ◽  
The Internet ◽  
Stepping Stones ◽  
Network Attacks ◽  
Stepping Stone

Intruders on the Internet usually launch network attacks through compromised hosts, called stepping stones, in order to reduce the chance of being detected. With stepping-stone intrusions, an attacker uses tools such as SSH to log in several compromised hosts remotely and create an interactive connection chain and then sends attacking packets to a target system. An effective method to detect such an intrusion is to estimate the length of a connection chain. In this paper, we develop an efficient algorithm to detect stepping-stone intrusion by mining network traffic using the k -means clustering. Existing approaches for connection-chain-based stepping-stone intrusion detection either are not effective or require a large number of TCP packets to be captured and processed and, thus, are not efficient. Our proposed detection algorithm can accurately determine the length of a connection chain without requiring a large number of TCP packets being captured and processed, so it is more efficient. Our proposed detection algorithm is also easier to implement than all existing approaches for stepping-stone intrusion detection. The effectiveness, correctness, and efficiency of our proposed detection algorithm are verified through well-designed network experiments.

scholarly journals A multiple pattern complex event detection scheme based on decomposition and merge sharing for massive event streams

2020 ◽  
Vol 16 (10) ◽  
pp. 155014772096133
Author(s):  
Jianhua Wang ◽  
Bang Ji ◽  
Feng Lin ◽  
Shilei Lu ◽  
Yubin Lan ◽  
...  
Keyword(s):  
Event Detection ◽  
Detection Method ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Detection Scheme ◽  
Event Stream ◽  
Detection Model ◽  
Efficient Detection ◽  
High Efficient ◽  
Complex Events

Quickly detecting related primitive events for multiple complex events from massive event stream usually faces with a great challenge due to their single pattern characteristic of the existing complex event detection methods. Aiming to solve the problem, a multiple pattern complex event detection scheme based on decomposition and merge sharing is proposed in this article. The achievement of this article lies that we successfully use decomposition and merge sharing technology to realize the high-efficient detection for multiple complex events from massive event streams. Specially, in our scheme, we first use decomposition sharing technology to decompose pattern expressions into multiple subexpressions, which can provide many sharing opportunities for subexpressions. We then use merge sharing technology to construct a multiple pattern complex events by merging sharing all the same prefix, suffix, or subpattern into one based on the above decomposition results. As a result, our proposed detection method in this article can effectively solve the above problem. The experimental results show that the proposed detection method in this article outperforms some general detection methods in detection model and detection algorithm in multiple pattern complex event detection as a whole.

scholarly journals A Parameter-Free Outlier Detection Algorithm Based on Dataset Optimization Method

Information ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 26
Author(s):  
Liying Wang ◽  
Lei Shi ◽  
Liancheng Xu ◽  
Peiyu Liu ◽  
Lindong Zhang ◽  
...  
Keyword(s):  
Outlier Detection ◽  
Detection Method ◽  
Computational Cost ◽  
Optimization Method ◽  
Detection Algorithm ◽  
Threshold Function ◽  
Detection Algorithms ◽  
Original Dataset ◽  
Data Clusters ◽  
High Computational Cost

Recently, outlier detection has widespread applications in different areas. The task is to identify outliers in the dataset and extract potential information. The existing outlier detection algorithms mainly do not solve the problems of parameter selection and high computational cost, which leaves enough room for further improvements. To solve the above problems, our paper proposes a parameter-free outlier detection algorithm based on dataset optimization method. Firstly, we propose a dataset optimization method (DOM), which initializes the original dataset in which density is greater than a specific threshold. In this method, we propose the concepts of partition function (P) and threshold function (T). Secondly, we establish a parameter-free outlier detection method. Similarly, we propose the concept of the number of residual neighbors, as the number of residual neighbors and the size of data clusters are used as the basis of outlier detection to obtain a more accurate outlier set. Finally, extensive experiments are carried out on a variety of datasets and experimental results show that our method performs well in terms of the efficiency of outlier detection and time complexity.

scholarly journals Detecting Metachanges in Data Streams from the Viewpoint of the MDL Principle

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1134 ◽  
Author(s):  
Shintaro Fukushima ◽  
Kenji Yamanishi
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Minimum Description Length ◽  
Detection Algorithm ◽  
Change Points ◽  
Detection Methods ◽  
Code Length ◽  
Warning Signals ◽  
Mdl Principle ◽  
Synthetic Datasets

This paper addresses the issue of how we can detect changes of changes, which we call metachanges, in data streams. A metachange refers to a change in patterns of when and how changes occur, referred to as “metachanges along time” and “metachanges along state”, respectively. Metachanges along time mean that the intervals between change points significantly vary, whereas metachanges along state mean that the magnitude of changes varies. It is practically important to detect metachanges because they may be early warning signals of important events. This paper introduces a novel notion of metachange statistics as a measure of the degree of a metachange. The key idea is to integrate metachanges along both time and state in terms of “code length” according to the minimum description length (MDL) principle. We develop an online metachange detection algorithm (MCD) based on the statistics to apply it to a data stream. With synthetic datasets, we demonstrated that MCD detects metachanges earlier and more accurately than existing methods. With real datasets, we demonstrated that MCD can lead to the discovery of important events that might be overlooked by conventional change detection methods.

Road crack detection and quantification based on segmentation network using architecture of matrix

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Gang Li ◽  
Yongqiang Chen ◽  
Jian Zhou ◽  
Xuan Zheng ◽  
Xue Li
Keyword(s):  
Crack Detection ◽  
Detection Method ◽  
Detection Methods ◽  
Content Type ◽  
The Road ◽  
Detection Algorithms ◽  
Model Training ◽  
Relative Errors ◽  
Detection Effect ◽  
Better Than

PurposePeriodic inspection and maintenance are essential for effective pavement preservation. Cracks not only affect the appearance of the road and reduce the levelness, but also shorten the life of road. However, traditional road crack detection methods based on manual investigations and image processing are costly, inefficiency and unreliable. The research aims to replace the traditional road crack detection method and further improve the detection effect.Design/methodology/approachIn this paper, a crack detection method based on matrix network fusing corner-based detection and segmentation network is proposed to effectively identify cracks. The method combines ResNet 152 with matrix network as the backbone network to achieve feature reuse of the crack. The crack region is identified by corners, and segmentation network is constructed to extract the crack. Finally, parameters such as the length and width of the cracks were calculated from the geometric characteristics of the cracks and the relative errors with the actual values were 4.23 and 6.98% respectively.FindingsTo improve the accuracy of crack detection, the model was optimized with the Adam algorithm and mixed with two publicly available datasets for model training and testing and compared with various methods. The results show that the detection performance of our method is better than many excellent algorithms, and the anti-interference ability is strong.Originality/valueThis paper proposed a new type of road crack detection method. The detection effect is better than a variety of detection algorithms and has strong anti-interference ability, which can completely replace traditional crack detection methods and meet engineering needs.

Soft Fault Detection Algorithms for Multi-Parallel Data Streams Under the Cloud Computing

2018 ◽  
Vol 22 (7) ◽  
pp. 1114-1119
Author(s):  
Hongbing Meng ◽  
Keyword(s):  
Fault Detection ◽  
Data Streams ◽  
Data Stream ◽  
Error Probability ◽  
Detection Efficiency ◽  
Detection Algorithm ◽  
Traditional Methods ◽  
Detection Algorithms ◽  
Parallel Data ◽  
Soft Fault

In the fault detection of multi-parallel data streams, the error probability of traditional methods is large, which cannot effectively meet the soft fault detection for multi-parallel data stream, causing the problem of low detection efficiency. A soft fault detection algorithm based on adaptive multi-parallel data stream is proposed. The soft fault feature in the data stream is extracted, and the adaptive soft fault detection algorithm is used to detect the fault of the multi-parallel data stream, which can overcome the disadvantages of traditional methods, effectively improve the efficiency, safety and the accuracy. Experimental results showed that the proposed method can effectively improve the efficiency of fault detection.

scholarly journals A Vertex Concavity-Convexity Detection Method for Three-Dimensional Spatial Objects Based on Geometric Algebra

2020 ◽  
Vol 9 (1) ◽  
pp. 25
Author(s):  
Pengcheng Yin ◽  
Jiyi Zhang ◽  
Xiying Sun ◽  
Di Hu ◽  
Zhifeng Shi ◽  
...  
Keyword(s):  
Geometric Algebra ◽  
Detection Method ◽  
Three Dimensional ◽  
Detection Algorithm ◽  
Basic Algorithm ◽  
Outer Product ◽  
Spatial Objects ◽  
Detection Algorithms ◽  
Product Operation ◽  
Object Based

Vertex concavity-convexity detection for spatial objects is a basic algorithm of computer graphics, as well as the foundation for the implementation of other graphics algorithms. In recent years, the importance of the vertex concavity-convexity detection algorithm for three-dimensional (3D) spatial objects has been increasingly highlighted, with the development of 3D modeling, artificial intelligence, and other graphics technologies. Nonetheless, the currently available vertex concavity-convexity detection algorithms mostly use two-dimensional (2D) polygons, with limited research on vertex concavity-convexity detection algorithms for 3D polyhedrons. This study investigates the correlation between the outer product and the topology of the spatial object based on the unique characteristic that the outer product operation in the geometric algebra has unified and definitive geometric implications in space, and with varied dimensionality. Moreover, a multi-dimensional unified vertex concavity-convexity detection algorithm framework for spatial objects is proposed, and this framework is capable of detecting vertex concavity-convexity for both 2D simple polygons and 3D simple polyhedrons.

scholarly journals A Fast Orientation Invariant Detector Based on the One-stage Method

2018 ◽  
Vol 232 ◽  
pp. 04036
Author(s):  
Jun Yin ◽  
Huadong Pan ◽  
Hui Su ◽  
Zhonggeng Liu ◽  
Zhirong Peng
Keyword(s):  
Object Detection ◽  
Loss Function ◽  
High Efficiency ◽  
Detection Method ◽  
State Of The Art ◽  
Orientation Angle ◽  
Detection Methods ◽  
Detection Algorithms ◽  
Bounding Boxes ◽  
The One

We propose an object detection method that predicts the orientation bounding boxes (OBB) to estimate objects locations, scales and orientations based on YOLO (You Only Look Once), which is one of the top detection algorithms performing well both in accuracy and speed. Horizontal bounding boxes(HBB), which are not robust to orientation variances, are used in the existing object detection methods to detect targets. The proposed orientation invariant YOLO (OIYOLO) detector can effectively deal with the bird’s eye viewpoint images where the orientation angles of the objects are arbitrary. In order to estimate the rotated angle of objects, we design a new angle loss function. Therefore, the training of OIYOLO forces the network to learn the annotated orientation angle of objects, making OIYOLO orientation invariances. The proposed approach that predicts OBB can be applied in other detection frameworks. In additional, to evaluate the proposed OIYOLO detector, we create an UAV-DAHUA datasets that annotated with objects locations, scales and orientation angles accurately. Extensive experiments conducted on UAV-DAHUA and DOTA datasets demonstrate that OIYOLO achieves state-of-the-art detection performance with high efficiency comparing with the baseline YOLO algorithms.

An Effective Method for Defects Detection in Radiographic Images of Welds Based on Edge Detection and Morphology

2013 ◽  
Vol 290 ◽  
pp. 71-77
Author(s):  
Wen Ming Guo ◽  
Yan Qin Chen
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Detection Algorithm ◽  
Morphological Processing ◽  
Radiographic Images ◽  
X Ray ◽  
Weld Defects ◽  
Detection Algorithms ◽  
Detection Quality

In the current industrial production, as steel weld X-ray images are low contrasted and noisy, the efficiency and precision can’t be both ensured. This paper has studied three different edge detection algorithms and found the most suitable one to detect weld defects. Combined with this edge detection algorithm, we proposed a new weld defects detection method. This method uses defect features to find the defects in edge images with morphological processing. Compared to the traditional methods, the method has ensured detection quality of weld defects detection.

scholarly journals A New Semantic-Based Tool Detection Method for Robots

2021 ◽  
Vol 16 (2) ◽  
Author(s):  
Wenbai Chen ◽  
Chao He ◽  
Chen W.Z. ◽  
Chen Q.L. ◽  
Wu P.L.
Keyword(s):  
Detection Method ◽  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Detection Algorithms ◽  
Recognition Ability ◽  
Home Helper ◽  
Point Detection ◽  
Functional Components ◽  
Instance Segmentation ◽  
Better Than

Home helper robots have become more acceptable due to their excellent image recognition ability. However, some common household tools remain challenging to recognize, classify, and use by robots. We designed a detection method for the functional components of common household tools based on the mask regional convolutional neural network (Mask-R-CNN). This method is a multitask branching target detection algorithm that includes tool classification, target box regression, and semantic segmentation. It provides accurate recognition of the functional components of tools. The method is compared with existing algorithms on the dataset UMD Part Affordance dataset and exhibits effective instance segmentation and key point detection, with higher accuracy and robustness than two traditional algorithms. The proposed method helps the robot understand and use household tools better than traditional object detection algorithms.

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