TY - JOUR AU - Altangerel, Gereltsetseg AU - Tejfel, Máté AU - Tsogbaatar, Enkhtur TI - IoT Anomaly Detection with 1D CNN Using P4 Capabilities JF - ACTA ELECTROTECHNICA ET INFORMATICA J2 - ACTA ELECTROTECH INF VL - 23 PY - 2023 IS - 2 SP - 3 EP - 12 PG - 10 SN - 1335-8243 DO - 10.2478/aei-2023-0006 UR - https://m2.mtmt.hu/api/publication/34563318 ID - 34563318 AB - Although the Internet of Things (IoT) is a rapidly developing technology, it also brings a number of security challenges, such as IoT attacks. Currently, research on IoT anomaly detection in Software-Defined Networking (SDN) relies only on the control plane. In this study, we aim to detect IoT anomalies by covering the advantages of the control and data plane. First, we collected real-time network telemetry data from the data plane based on the capabilities of the P4. Then, using this telemetry data, we built different anomaly detection models and compared their performance. Among them, the one-Dimensional Convolutional Neural Network (1D CNN) model classified our data best and showed the highest performance, so we proposed this model for IoT anomaly detection on the control plane. To our knowledge, our approach is the first solution that integrates the control plane and data plane for IoT anomaly detection. Finally, when evaluating the performance of our proposed 1D CNN model, the accuracy, F1 score, and Matthews correlation coefficient (MCC) are the same or better than existing studies. LA - English DB - MTMT ER - TY - JOUR AU - Alwahab, Dhulfiqar Zoltán AU - Pataki, Norbert AU - Tejfel, Máté TI - Chatbot-Based Querying of IoT Devices in EdgeX JF - CEUR WORKSHOP PROCEEDINGS J2 - CEUR WORKSHOP PROC VL - 3588 PY - 2023 SP - 104 EP - 113 PG - 10 SN - 1613-0073 UR - https://m2.mtmt.hu/api/publication/34434848 ID - 34434848 AB - The increasing number of IoT devices connected to EdgeX makes it challenging to retrieve data from these devices efficiently. In this paper, we propose a chatbot-based solution for querying IoT devices connected to EdgeX. The chatbot utilizes natural language processing (NLP) techniques to understand user queries and retrieve relevant data from the EdgeX database. Our solution offers an easy-to-use interface for nontechnical users to retrieve data from IoT devices, enabling them to quickly and easily access information about their devices. Our results demonstrate that our chatbot-based solution is efficient and effective in retrieving data from IoT devices, offering a more user-friendly approach for querying EdgeX databases. The proposed chatbot-based solution has the potential to improve the accessibility and efficiency of data retrieval from IoT devices in EdgeX. LA - English DB - MTMT ER - TY - JOUR AU - Altangerel, Gereltsetseg AU - Tejfel, Máté TI - In-network DDoS detection and mitigation using INT data for IoT ecosystem JF - INFOCOMMUNICATIONS JOURNAL J2 - INFOCOMM J VL - 15 PY - 2023 IS - Special Issue SP - 49 EP - 54 PG - 6 SN - 2061-2079 DO - 10.36244/ICJ.2023.5.8 UR - https://m2.mtmt.hu/api/publication/34130949 ID - 34130949 AB - Due to the limited capabilities and diversity of Internet of Things (IoT) devices, it is challenging to implement robust and unified security standards for these devices. Additionally, the fact that vulnerable IoT devices are beyond the network’s control makes them susceptible to being compromised and used as bots or part of botnets, leading to a surge in attacks involving these devices in recent times. We proposed a real-time IoT anomaly detection and mitigation solution at the programmable data plane in a Software-Defined Networking (SDN) environment using Inband Network telemetry (INT) data to address this issue. As far as we know, it is the first experiment in which INT data is used to detect IoT attacks in the programmable data plane. Based on our performance evaluation, the detection delay of our proposed approach is much lower than the results of previous Distributed Denial-of-Service (DDoS) research, and the detection accuracy is similarly high. LA - English DB - MTMT ER - TY - CONF AU - Altangerel, Gereltsetseg AU - Tejfel, Máté TI - In-network DDoS detection and mitigation using INT data for IoT ecosystem T2 - 12th International Conference on Applied Informatics (ICAI 2023) PB - Eszterházy Károly Katolikus Egyetem C1 - Eger PY - 2023 SP - 1 EP - 3 PG - 3 UR - https://m2.mtmt.hu/api/publication/34130323 ID - 34130323 LA - English DB - MTMT ER - TY - CONF AU - Tejfel, Máté AU - Lukács, Dániel AU - Péter, Hegyi TI - P4 Specific Refactoring Steps T2 - 12th International Conference on Applied Informatics (ICAI 2023) PB - Eszterházy Károly Katolikus Egyetem C1 - Eger PY - 2023 SP - 1 EP - 3 PG - 3 UR - https://m2.mtmt.hu/api/publication/34122640 ID - 34122640 LA - English DB - MTMT ER - TY - JOUR AU - Lukács, Dániel AU - Tóth, Gabriella AU - Tejfel, Máté TI - P4Query: Static analyser framework for P4 JF - ANNALES MATHEMATICAE ET INFORMATICAE J2 - ANN MATH INFORM VL - 57 PY - 2023 SP - 49 EP - 64 PG - 16 SN - 1787-5021 DO - 10.33039/ami.2023.03.002 UR - https://m2.mtmt.hu/api/publication/34009519 ID - 34009519 AB - There are many important tasks in a conventional software development process which can be supported by different analysis techniques. P4 is a high level domain-specific language for describing the data plane layer of packet processing algorithms. It has several uncommon language elements and concepts that often make the analysis of P4 programs a laborious task. The paper presents P4Query, an analysis framework for the P4 language that enables the specification of different P4-related analysis methods in a generic and data-centric way. The framework uses an internal graph representation which contains the results of applied analysis methods too. In this way, the framework supports the rapid implementation of new analysis methods in a way where the results will be also easily reusable by other methods. LA - English DB - MTMT ER - TY - CHAP AU - Altangerel, Gereltsetseg AU - Tejfel, Máté AU - Enkhtur, Tsogbaatar ED - Steingartner, William ED - Korečko, Štefan ED - Szakál, Anikó TI - A 1D CNN-based model for IoT anomaly detection using INT data T2 - 2022 IEEE 16th International Scientific Conference on Informatics - Proceedings PB - Institute of Electrical and Electronics Engineers (IEEE) CY - Poprad CY - Piscataway (NJ) CY - Red Hook (NY) SN - 9798350310351 PY - 2022 SP - 106 EP - 113 PG - 8 DO - 10.1109/Informatics57926.2022.10083469 UR - https://m2.mtmt.hu/api/publication/33267353 ID - 33267353 AB - Due to the limited capacity and versatility of Internet of Things (IoT) devices, it isn’t easy to implement advanced security mechanisms and adhere to common security standards on IoT devices. Our study proposes a network-based solution to address these issues in the IoT environment. This solution leverages the advantages of a programmable data plane, Software-Defined Networking (SDN), and machine learning. In-Band Network Telemetry (INT) is a novel monitoring application developed using a programmable data plane to collect network characteristics (INT data) in real time without affecting network performance. We aim to detect IoT attacks based on INT data using a 1D CNN-based deep learning model. As far as we know, this model is the first attempt to use INT data to detect IoT attacks. We created an SDN network infrastructure in a simulation environment and collected INT data from IoT devices in the event of an attack or non-attack. Our proposed 1D CNN-based model using INT data can detect IoT attacks with approximately 99.63% accuracy. Our solution is relatively cost-effective and performs well compared to other competing models. LA - English DB - MTMT ER - TY - CONF AU - Lukács, Dániel AU - Tejfel, Máté ED - Jász, Judit ED - Bánhelyi, Balázs ED - Gergely, Tamás ED - Katona, Melinda ED - Kincses, Zoltán TI - Overlaying control flow graphs on P4 syntax trees with Gremlin. T2 - The 13th Conference of PhD Students in Computer Science PB - Szegedi Tudományegyetem, Informatikai Intézet C1 - Szeged PY - 2022 SP - 50 EP - 54 PG - 5 UR - https://m2.mtmt.hu/api/publication/33108392 ID - 33108392 LA - English DB - MTMT ER - TY - JOUR AU - Lukács, Dániel AU - Pongrácz, Gergely AU - Tejfel, Máté TI - Model Checking-Based Performance Prediction for P4 JF - ELECTRONICS (SWITZ) VL - 11 PY - 2022 IS - 14 SN - 2079-9292 DO - 10.3390/electronics11142117 UR - https://m2.mtmt.hu/api/publication/33108365 ID - 33108365 AB - Next-generation networks focus on scale and scope at the price of increasing complexity, leading to difficulties in network design and planning. As a result, anticipating all hardware- and software-related factors of network performance requires time-consuming and expensive benchmarking. This work presents a framework and software tool for automatically inferring the performance of P4 programmable network switches based on the P4 source code and probabilistic models of the execution environment with the hope of eliminating the requirement of the costly set-up of networked hardware and conducting benchmarks. We designed the framework using a top-down approach. First, we transform high-level P4 programs into a representation that can be refined incrementally by adding probabilistic environment models of increasing levels of complexity in order to improve the estimation precision. Then, we use the PRISM probabilistic model checker to perform the heavy weight calculations involved in static performance prediction. We present a formalization of the performance estimation problem, detail our solution, and illustrate its usage and validation through a case study conducted using a small P4 program and the P4C-BM reference switch. We show that the framework is already capable of performing estimation, and it can be extended with more concrete information to yield better estimates. LA - English DB - MTMT ER - TY - JOUR AU - Altangerel, Gereltsetseg AU - Tejfel, Máté TI - Study on Emerging Applications on Data Plane and Optimization Possibilities JF - INTERNATIONAL JOURNAL OF DISTRIBUTED AND PARALLEL SYSTEMS J2 - IJDPS VL - 13 PY - 2022 IS - 1 SP - 1 EP - 11 PG - 11 SN - 2229-3957 DO - 10.5121/ijdps.2022.13101 UR - https://m2.mtmt.hu/api/publication/32799829 ID - 32799829 LA - English DB - MTMT ER -