Browsing by Subject "anomaly detection"

Now showing 1 - 7 of 7

Access status: Open Access ,
A Big Data processing strategy for hybrid interpretation of flood embankment multisensor data
(Wydawnictwa AGH, 2016) Chuchro, Monika; Franczyk, Anna; Dwornik, Maciej; Leśniak, Andrzej
The assessment of flood embankments is a key component of a country’s comprehensive flood protection. Proper and early information on the possible instability of a flood embankment can make it possible to take preventative action. The assessment method proposed by the ISMOP project is based on a strategy of processing huge data sets (Big Data). The detection of flood embankment anomalies can take two analysis paths. The first involves the computation of numerical models and comparing them with real data measured on a flood embankment. This is the path of model-driven analysis. The second solution is data-driven, meaning time series are analysed in order to detect deviations from average values. Flood embankments are assessed based on the results of model-driven and data-driven analyses and information from preprocessing. An alarm is triggered if a critical value is exceeded in one or both paths of analysis. Tests on synthetic data demonstrate the high efficiency of the chosen methods for assessing the state of flood embankments.
Access status: Open Access ,
Application of the Complex Event Processing system for anomaly detection and network monitoring
(Wydawnictwa AGH, 2015) Frankowski, Gerard; Jerzak, Marcin; Miłostan, Maciej; Nowak, Tomasz; Pawłowski, Marek
Protection of infrastructures for e-science, including grid environments and NREN facilities, requires the use of novel techniques for anomaly detection and network monitoring. The aim is to raise situational awareness and provide early warning capabilities. The main operational problem that most network operators face is integrating and processing data from multiple sensors and systems placed at critical points of the infrastructure. From a scientific point of view, there is a need for the efficient analysis of large data volumes and automatic reasoning while minimizing detection errors. In this article, we describe two approaches to Complex Event Processing used for network monitoring and anomaly detection and introduce the ongoing SECOR project (Sensor Data Correlation Engine for Attack Detection and Support of Decision Process), supported by examples and test results. The aim is to develop methodology that allows for the construction of next-generation IDS systems with artificial intelligence, capable of performing signature-less intrusion detection.
Access status: Restricted ,
Declarative management of distributed wireless networks
(Data obrony: 2019-10-25) Gąsiorowska, Katarzyna
Wydział Informatyki, Elektroniki i Telekomunikacji
Access status: Restricted ,
Machine learning in intrusion detection
(Data obrony: 2019-07-12) Faber, Kamil
Wydział Informatyki, Elektroniki i Telekomunikacji
Access status: Open Access ,
Performance measurement with high-performance computer using HW-GA anomaly-detection algorithms for streaming data
(Wydawnictwa AGH, 2022) Fondaj, Jakup; Hasani, Zirije; Krrabaj, Samedin
Anomaly detection for streaming real-time data is very important, more significant is the performance of an algorithm in order to meet real-time requirements. Anomaly detection is very crucial in every sector because, by knowing what is going wrong with data/digital systems, we can make decisions to help in every sector. Dealing with real-time data requires speed, for this reason, the aim of this paper is to measure the performance of our proposed Holt–Winters genetic algorithm (HW-GA) as compared to other anomaly-detection algorithms with a large amount of data as well as to measure how other factors such as visualization and the performance of the testing environment affect the algorithm’s performance. The experiments will be done in R with different data sets such as the as real COVID-19 and IoT sensor data that we collected from Smart Agriculture Libelium sensors and e-dnevnik as well as three benchmarks from the Numenta data sets. The real data has no known anomalies, but the anomalies are known in the benchmark data, this was done in order to evaluate how the algorithm works in both situations. The novelty of this paper is that the performance will be tested on three different computers (in which one is a high-performance computer), also, a large amount of data will be used for our testing, as will how the visualization phase affects the algorithm’s performance.
Access status: Open Access ,
Probabilistic anomaly detection based on system calls analysis
(2007) Maciołek, Przemysław; Król, Paweł; Koźlak, Jarosław
We present an application of probabilistic approach to the anomaly detection (PAD). By analyzing selected system calls (and their arguments), the chosen applications are monitored in the Linux environment. This allows us to estimate »(ab)normality« of their behavior (by comparison to previously collected profiles). We've attached results of threat detection in a typical computer environment.
Access status: Restricted ,
Zastosowanie wybranych metod uczenia maszynowego do wykrywania anomalii
(Data obrony: 2019-01-23) Filipek, Gabriel
Wydział Elektrotechniki, Automatyki, Informatyki i Inżynierii Biomedycznej