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Automated Processing of Network Service Logs in Linux
Hodermarsky, Jan ; Jeřábek, Jan (referee) ; Ilgner, Petr (advisor)
This thesis is focused on design and implementation of software for a prophylactic real-time logfile analysis and a consequent threat detection apparent therein. The software is to concentre particularly on network services, respectively, on the log files thereof, on Linux platform. The log files are observed for potential security breach attempts in regard to respective service as defined in the configuration file. The present thesis purports to reach the largest extent of versatility possible for a straightforward configuration of a new service which is to be monitored and protected by the software. An important asset of the work is a web-based interface accessible through HTTP protocol which allows the software to be administered remotely with ease.
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Crowd Behavior Anomaly Detection in Drone Videodata
Bažout, David ; Herout, Adam (referee) ; Beran, Vítězslav (advisor)
There have been lots of new drone applications in recent years. Drones are also often used in the field of national security forces. The aim of this work is to design and implement a tool intended for crowd behavior analysis in drone videodata. This tool ensures identification of suspicious behavior of persons and facilitates its localization. The main benefits include the design of a suitable video stabilization algorithm to stabilize small jitters, as well as trace back of the lost scene. Furthermore, two anomaly detectors were proposed, differing in the method of feature vector extraction and background modeling. Compared to the state of the art approaches, they achieved comparable results, but at the same time they brought the possibility of online data processing.
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Recognizing and Classification of Traffic Situations
Zbořil, Jiří ; Musil, Petr (referee) ; Smrž, Pavel (advisor)
The aim of this thesis is to identify and classify dangerous situations from surveillance cameras, monitoring traffic. An example of such situations is dangerous standing near by the road and car crash, on which this work focuses. The created system uses object detector, analyzing average images in given interval, K nearest neighbor and K Means algorithm and re-detection of enlarged local area in a frame to select anomaly candidates. Detected objects, that do not belong on the road are eliminated by attaching created road mask. At the very last phase, the interval, together with the classification is determined. Calculated F1 score is 0.645, S4 score 0.535 and precision of classification 80 %.
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Recognizing and Classification of Traffic Situations
Zbořil, Jiří ; Musil, Petr (referee) ; Smrž, Pavel (advisor)
The aim of this thesis is to identify and classify dangerous situations from surveillance cameras, monitoring traffic. An example of such situations is dangerous standing near by the road and car crash, on which this work focuses. The created system uses object detector, analyzing average images in given interval, K nearest neighbor and K Means algorithm and re-detection of enlarged local area in a frame to select anomaly candidates. Detected objects, that do not belong on the road are eliminated by attaching created road mask. At the very last phase, the interval, together with the classification is determined. Calculated F1 score is 0.645, S4 score 0.535 and precision of classification 80 %.
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Crowd Behavior Anomaly Detection in Drone Videodata
Bažout, David ; Herout, Adam (referee) ; Beran, Vítězslav (advisor)
There have been lots of new drone applications in recent years. Drones are also often used in the field of national security forces. The aim of this work is to design and implement a tool intended for crowd behavior analysis in drone videodata. This tool ensures identification of suspicious behavior of persons and facilitates its localization. The main benefits include the design of a suitable video stabilization algorithm to stabilize small jitters, as well as trace back of the lost scene. Furthermore, two anomaly detectors were proposed, differing in the method of feature vector extraction and background modeling. Compared to the state of the art approaches, they achieved comparable results, but at the same time they brought the possibility of online data processing.
|
|
|
Automated Processing of Network Service Logs in Linux
Hodermarsky, Jan ; Jeřábek, Jan (referee) ; Ilgner, Petr (advisor)
This thesis is focused on design and implementation of software for a prophylactic real-time logfile analysis and a consequent threat detection apparent therein. The software is to concentre particularly on network services, respectively, on the log files thereof, on Linux platform. The log files are observed for potential security breach attempts in regard to respective service as defined in the configuration file. The present thesis purports to reach the largest extent of versatility possible for a straightforward configuration of a new service which is to be monitored and protected by the software. An important asset of the work is a web-based interface accessible through HTTP protocol which allows the software to be administered remotely with ease.
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