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Detecting RTOS Runtime Anomalies
Arm, Jakub ; Jalovecký, Rudolf (referee) ; Blecha, Petr (referee) ; Bradáč, Zdeněk (advisor)
Due to higher requirements of computational power and safety, or functional safety ofequipments intended for the use in the industrial domain, embedded systems containing areal-time operating system are still the active area of research. This thesis addresses thehardware-assisted control module that is based on the runtime model-based verificationof a target application. This subsystem is intended to increase the diagnostic coverage,particularly, the detection of the execution errors. After the specification of the architecture,the formal model is defined and implemented into hardware using FPGA technology.This thesis also discuss some other aspects and embodies new approaches in the area ofembedded flow control, e.g. the integration of the design patterns. Using the simulation,the created module was tested using the created scenarios, which follow the real programexecution record. The results suggest that the error detection time is lower than usingstandard techniques, such a watchdog.
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Methods for Network Traffic Classification
Jacko, Michal ; Ovšonka, Daniel (referee) ; Barabas, Maroš (advisor)
This paper deals with a problem of detection of network traffic anomaly and classification of network flows. Based on existing methods, paper describes proposal and implementaion of a tool, which can automatically classify network flows. The tool uses CUDA platform for network data processing and computation of network flow metrics using graphics processing unit. Processed flows are subsequently classified by proposed methods for network anomaly detection.
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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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Behavioral Analysis of DDoS Network Attacks
Kvasnica, Ondrej ; Homoliak, Ivan (referee) ; Očenášek, Pavel (advisor)
This bachelor thesis deals with anomaly detection in computer networks using artificial intelligence method. Main focus is on the detection of DDoS attacks based on the information from the lower layers of the OSI model. The target is to design and implement a system that is capable of detecting different types of DDoS attacks and characterize common features among them. Selected attacks are SYN flood, UDP flood and ICMP flood. Description and feature selection of the attacks is included. Furthermore, a system is designed that evaluates whether the network traffic (organized into flows) is a DDoS attack or not. Attacks are detected using the XGBoost method, which uses supervised learning. The final model is validated using cross-validation and tested on attacks generated by the author.
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Deep Neural Networks for Defect Detection
Juřica, Tomáš ; Herout, Adam (referee) ; Hradiš, Michal (advisor)
The goal of this work is to bring automatic defect detection to the manufacturing process of plastic cards. A card is considered defective when it is contaminated with a dust particle or a hair. The main challenges I am facing to accomplish this task are a very few training data samples (214 images), small area of target defects in context of an entire card (average defect area is 0.0068 \% of the card) and also very complex background the detection task is performed on. In order to accomplish the task, I decided to use Mask R-CNN detection algorithm combined with augmentation techniques such as synthetic dataset generation. I trained the model on the synthetic dataset consisting of 20 000 images. This way I was able to create a model performing 0.83 AP at 0.1 IoU on the original data test set.
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Automatic quality control of painted metal parts production using neural networks
Ježek, Štěpán ; Kolařík, Martin (referee) ; Burget, Radim (advisor)
This thesis is focused on the problem of visual quality control during painted metal parts fabrication. The main problem of the thesis is the design of automatic quality control method based on modern artificial intelligence and computer vision techniques. Quality control is an important part of a large number of industrial production processes, in which it is necessary to ensure compliance with a number of quality requirements for manufactured products. Until now, quality control is carried out mainly by specialized staff, who are subject to a number of expertise requirements. Currently known methods of visual quality control based on artificial intelligence are characterized by high demands on the size of the training data set and low tolerance for a significant change in position and rotation of the inspected objects relative to the scanning device. As a result of these shortcomings, the use of automated visual quality control in many current industrial applications is impossible. The main contribution of this thesis is the design of a new method for quality control, which shows a strong ability to function reliably even in cases where the above mentioned phenomena of change in position, rotation of objects and lack of training data occur during manufacturing. The accuracy of the method proposed in this thesis is experimentally verified on a data set based on the issue of quality control of painted metal parts. According to the measurement results of defect detection accuracy, the proposed method outperformed other, currently known methods by 10, 25 % using the AUROC metric.
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User Behavior Anomaly Detection
Petrovič, Lukáš ; Veselý, Vladimír (referee) ; Pluskal, Jan (advisor)
The aim of this work is to create an application that allows modeling of user behavior and subsequent search for anomalies in this behavior. An application entry is a list of actions the user has executed on his workstation. From this information and from information about the events that occurred on this device the behavioral model for a specific time is created. Subsequently, this model is compared to models in different time periods or with other users' models. From this comparison, we can get additional information about user behavior and also detect anomalous behavior. The information about the anomalies is useful to build security software that prevents valuable data from being stolen (from the corporate enviroment).
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Application Monitoring of IoT Devices
Krajč, Patrik ; Ryšavý, Ondřej (referee) ; Matoušek, Petr (advisor)
IoT devices use various standards at the level of the transmission medium and communication protocol. The aim of the work is to create a system, which we can unify a heterogeneous network of the Internet of Things for monitoring purposes. For data collection from the IoT network was used the Home Assistant platform which is uses SNMP agent we created. The monitoring system includes the Nagios core system, which is extended with machine learning-based anomaly detection.
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System Log Analysis for Anomaly Detection Using Machine Learning
Šiklóši, Miroslav ; Fujdiak, Radek (referee) ; Hošek, Jiří (advisor)
Táto diplomová práca sa venuje problematike využitia strojového učenia na detekciu anomálií na základe analýzy systémových logov. Navrhnuté modely sú založené na algoritmoch strojového učenia s učiteľom, bez učiteľa a na hlbokom učení. Funkčnosť a správanie týchto algoritmov sú objasnené ako teoreticky, tak aj prakticky. Okrem toho boli využité metódy a postupy na predspracovanie dát predtým, než boli vložené do modelov strojového učenia. Navrhnuté modely sú na konci porovnané s využitím viacerých metrík a otestované na syslogoch, ktoré modely predtým nevideli. Najpresnejší výkon podali modely Klasifikátor rozhodovacích stromov, Jednotriedny podporný vektorový stroj a model Hierarchické zoskupovanie, ktoré správne označili 93,95%, 85,66% a 85,3% anomálií v uvedenom poradí.
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