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Real-time detection of hidden traffic patterns

Fang Hao, M. Kodialam, T.V. Lakshman
Proceedings of the 12th IEEE International Conference on Network Protocols, 2004. ICNP 2004.  
In this paper, we develop an effective scheme to identify and measure hidden traffic patterns.  ...  However, there are many scenarios where traffic patterns are hidden in the sense that there is no clear knowledge of what exactly to look for and there is no natural a priori definition of flow.  ...  The patterns are considered hidden because the real time uncovering of these patterns in the traffic stream requires a priori knowledge regarding the set of "interesting" flows.  ... 
doi:10.1109/icnp.2004.1348123 dblp:conf/icnp/HaoKL04 fatcat:xmottdjv55et3kusz733sbeb54

TorBot Stalker: Detecting Tor Botnets Through Intelligent Circuit Data Analysis

Oluwatobi Fajana, Gareth Owenson, Mihaela Cocea
2018 2018 IEEE 17th International Symposium on Network Computing and Applications (NCA)  
at the Tor network border, in real-time, while preserving the privacy of legitimate users.  ...  We use machine learning to analyse and fingerprint the timings and frequency of Tor network circuit data when routing botnet traffic, and build a detection mechanism that is able to identify infected hosts  ...  Our approach employs a machine learning algorithm for the detection of botnet traffic patterns in real time and preserves the privacy of its users.  ... 
doi:10.1109/nca.2018.8548313 dblp:conf/nca/FajanaOC18 fatcat:fxli4gnpazhmbek2o4cg5k3nqe

Highway traffic incident detection based on BPNN

Xueqing Cheng, Wenfang Lin, Enxiang Liu, Dan Gu
2010 Procedia Engineering  
Because traffic incident is the main cause of traffic congestion in Highway, traffic incident detection plays an important role in ITS.  ...  Simulation result shows that the new method has many advantages such as higher detection rate, lower false alarm rate and shorter mean detection time.  ...  as well as realize the real-time monitoring on traffic incident condition.  ... 
doi:10.1016/j.proeng.2010.11.080 fatcat:7odocldcxjdebdy73kc6h7hnge

Urban Traffic Intersection Incident Prediction Using AI Algorithm

Yaguang Kong, Huakui Chen
2006 Proceedings of the 9th Joint Conference on Information Sciences (JCIS)  
The test results indicate the feasibility of achieving real-time incident detection utilizing the proposed method.  ...  As a component of a real-time traffic adaptive control system for signal control, the algorithm feeds an incident report to the system's optimization manager, which uses the information to determine the  ...  The algorithm can be developed as part of a real-time, traffic adaptive, diamond interchange control system.  ... 
doi:10.2991/jcis.2006.302 dblp:conf/jcis/KongC06 fatcat:67otfxwbirgwvotcc77w6b5yjy

Malicious Traffic Detection and Containment based on Connection Attempt Failures using Kernelized ELM with Automated Worm Containment Algorithm

S. Divya, G. Padmavathi
2016 Indian Journal of Science and Technology  
Then proposed kEA method blocks all the detected malicious IP addresses with 100% containment at the time span of 33 ms.  ...  The proposed kernelized Extreme Learning Machine with Automated Worm Containment Algorithm (kEA) method is used for detection and containment of malicious traffic from non-existing IP addresses based on  ...  After training, the binary patterns of hidden neuron outputs are extracted. With the extracted output, behavior of new pattern is designed to detect new worms.  ... 
doi:10.17485/ijst/2016/v9i41/86922 fatcat:exdqustp5vfm5mld6vuxsyjf3e

A Real-Time Hidden Anomaly Detection of Correlated Data in Wireless Networks

Tengfei Sui, Xiaofeng Tao, Shida Xia, Hui Chen, Huici Wu, Xuefei Zhang, Kechen Chen
2020 IEEE Access  
We present a novel data Decomposition aided Random Matrix Theory (DC-RMT) framework, which enables a real-time anomaly detection of large scale multi-dimensional and highly-correlated data.  ...  Current Random Matrix Theory (RMT) approaches are unable to detect hidden anomalies with a satisfying tolerance of data correlation.  ...  The successful real-time detection of anomaly patterns proves the effectiveness of DC-RMT in the analysis of highly-correlated and multi-dimensional wireless network traffic data.  ... 
doi:10.1109/access.2020.2984276 fatcat:ear6vf33kveelkabsb5fxmspiu

Artificial neuron network implementation in detection and classification of DDoS traffic

Dragan Perakovic, Marko Perisa, Ivan Cvitic, Sinisa Husnjak
2016 2016 24th Telecommunications Forum (TELFOR)  
Detection of DDoS (Distributed Denial of Service) traffic is of great importance for the availability protection of services and other information and communication resources.  ...  Simulation results of developed model showed accuracy of 95.6% in classification of pre-defined classes of traffic.  ...  The research [1] shows developed model of ANN that can detect known and unknown DDoS attacks in real-time.  ... 
doi:10.1109/telfor.2016.7818791 fatcat:r4rogprcx5estlcbt5f7cjkrby

W-NINE: A Two-Stage Emulation Platform for Mobile and Wireless Systems

Emmanuel Conchon, Tanguy Pérennou, Johan Garcia, Michel Diaz
2010 EURASIP Journal on Wireless Communications and Networking  
This paper proposes a network emulation platform, called W-NINE, based on off-line computations preceding online pattern-based traffic shaping.  ...  Testing the implementation of such applications and protocols is a real challenge as the position of the mobile terminals and environmental effects strongly affect the overall performance.  ...  After 5 s, the TO running on the emulator detects concurrent traffics from F 1 and F 2 on the experimentation network. It informs the emulation manager of the detected hidden terminal situation.  ... 
doi:10.1155/2010/149075 fatcat:dr3noabpwzagxoikp6r2yr6c6m

Normalization of Input Vectors in Deep Belief Networks (DBNs) for Automatic Incident Detection

Daehyon Kim
2018 Asia-pacific Journal of Convergent Research Interchange  
Traffic incidents have a serious negative impact on safety and traffic flow, and fast accurate automatic incident detection on freeways is a major theme in transportation engineering.  ...  improved performance in terms of detection and false alarm rates.  ...  Recently, ANNs, which are easy to implement a real-time detection system, are the most attractive algorithms for detecting incidents on freeways, because they can provide fast, reliable and fault tolerant  ... 
doi:10.21742/apjcri.2018.12.07 fatcat:ouu7z4ig3bbfnb6bmkbgzrg5wq

Bayonet-corpus: A trajectory prediction method based on bayonet context and bidirectional GRU

Mengyang Huang, Menggang Zhu, Yunpeng Xiao, Yanbing Liu
2020 Digital Communications and Networks  
so the high-dimensional similarity between corpus nodes are able to be used to measure the semantic relation of real traffic intersections.  ...  This algorithm maps vehicle trajectory nodes into a high-dimensional space vector, blocking complex structure of real traffic network and reconstructing the traffic network space.  ...  Acknowledgments This paper is partially supported by the National Natural Science Foundation of China  ... 
doi:10.1016/j.dcan.2020.03.002 fatcat:ghkrtc6eofdydip3zu3xnhzhyi

Analysis and Forecasting for Traffic Flow Data

Yitian Wang, Joseph Jaja
2019 Sensors and materials  
The algorithm is implemented to derive core traffic patterns of traffic flow data of Baltimore, Maryland, US.  ...  The k-nearest-neighbor (KNN) method is applied to predict the values of these core traffic patterns in the near future.  ...  At each time step, we receive traffic information about a large number of road segments, which has to be analyzed and disseminated in real time.  ... 
doi:10.18494/sam.2019.2315 fatcat:ab4w2wcf45emblv4shbwwiqfhq

Activity-informed Dynamic Data Driven Simulation

Xiaolin Hu, Sanish Rai, Xiaoming Wang, A. Muzy, O. Michel, D.R.C. Hill
2013 ITM Web of Conferences  
The real time behavior pattern detection layer uses Hidden Markov Model (HMM) to detect the behavior patterns of a system in real time and uses the detected behavior pattern to inform the simulation model  ...  This paper builds on previous work and presents a framework that adds a real time behavior pattern detection layer on top of data assimilation for dynamic data driven simulation.  ...  Besides the wildfire application described above, other potential applications include traffic simulation where real time traffic sensor data are assimilated to improve traffic predictions and crowd behavior  ... 
doi:10.1051/itmconf/20130102002 fatcat:tc4yuuk2gvb55knbcfvkftdac4

Real time road signs classification

Paolo Medici, Claudio Caraffi, Elena Cardarelli, Pier Paolo Porta, Guido Ghisio
2008 2008 IEEE International Conference on Vehicular Electronics and Safety  
The main focus will be on the final neural network based classification stage of the candidates provided by an existing traffic sign detection algorithm.  ...  Benchmarks are presented, showing that the system is able to classify more then 200 different Italian road sign in real-time, with a recognition rate of 80% to 90%.  ...  Brief benchmarks of networks with thresholds chosen for the final application are presented in This paper described the final stage of a traffic sign recognition system, capable of classifying in real-time  ... 
doi:10.1109/icves.2008.4640906 dblp:conf/icves/MediciCCPG08 fatcat:njvcmj2zojb6xkwmxbqxkijd5a

Detection of Distributed Denial of Service Attacks Using Artificial Neural Networks

Abdullah Aljumah
2017 International Journal of Advanced Computer Science and Applications  
In this research article, we have proposed a DDoS detection system using artificial neural networks that will flag (mark) malicious and genuine data traffic and will save network from losing performance  ...  which separate the legitimate traffic from malicious traffic that were given to artificial neural networks during its training process.  ...  There are other advantages of neural networks which include Adaptive learning, Self organization, Real time operation, redundant information coding, etc. [11] .  ... 
doi:10.14569/ijacsa.2017.080841 fatcat:aj5vol6llvffdpuk23pb2t73ru

Customization of Automatic Incident Detection Algorithms for Signalized Urban Arterials

Bidisha Ghosh, Damien P. Smith
2014 Journal of Intelligent Transportation Systems / Taylor & Francis  
The new strategy focuses on preprocessing the traffic information before being used as input to a freeway/highway based AIDA to lessen the effect of traffic signals and to imitate the input patterns in  ...  The main hindrance to such synthesis is that the traffic patterns on the signalised urban arterials are significantly different from the same on highways/freeways due to the presence of traffic intersections  ...  time of incident i and id t is the time of detection of the incident i.  ... 
doi:10.1080/15472450.2013.806843 fatcat:qcvddbapc5fcnle7haknvt7exi
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