Browsing by Subject "intrusion detection"

Now showing 1 - 4 of 4

Access status: Open Access ,
An octopus-inspired intrusion deterrence model in distributed computing system
(Wydawnictwa AGH, 2016) Olajubu, Emmanuel A.; Akinwale, Abiodun; Ogundoyin, Kazeem Ibraheem
The study formulated and evaluated a model for effective management of malicious nodes in mobile Ad-hoc network based on Ad-Hoc on- demand distance vector routing protocol. A collaborative injection model called Collaborative Injection Deterrence Model (CIDM) was formulated using stochastic theory. The definition of the model was presented using graph theory. CIDM was simulated using three different scenarios. The three scenarios were then compared using packets delivery ratio (PDR), routing load, throughput and delay as performance metrics. The simulation result showed that CIDM reduce considerably the rate of packets dropped caused by malicious nodes in MANET network. CIDM did not introduce additional load to the network and, yet produce higher throughput. Lastly, the access delay in CIDM is minimal compared with convectional OADV. The study developed a model to mete out a punitive measure to rogue nodes as a form of intrusion deterrence without degrading the overall performance of the network. The well known CRAWDAD dataset was used in the simulation.
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: Open Access ,
Gramian angular field transformation-based intrusion detection
(Wydawnictwa AGH, 2022) Terzi, Duygu Sinanc
Cyber threats are increasing progressively in their frequency, scale, sophistication, and cost. The advancement of such threats has raised the need to enhance intelligent intrusion-detection systems. In this study, a different perspective has been developed for intrusion detection. Gramian angular fields were adapted to encode network traffic data as images. Hereby, a way to reveal bilateral feature relationships and benefit from the visual interpretation capability of deep-learning methods has been opened. Then, image-encoded intrusions were classified as binary and multi-class using convolutional neural networks. The obtained results were compared to both conventional machine-learning methods and related studies. According to the results, the proposed approach surpassed the success of traditional methods and produced success rates that were close to the related studies. Despite the use of complex mechanisms such as feature extraction, feature selection, class balancing, virtual data generation, or ensemble classifiers in related studies, the proposed approach is fairly plain – involving only data-image conversion and classification. This shows the power of simply changing the problem space.
Access status: Restricted ,
Machine learning in intrusion detection
(Data obrony: 2019-07-12) Faber, Kamil
Wydział Informatyki, Elektroniki i Telekomunikacji