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Network intrusion detection is a key pillar towards the sustainability and normal operation of information systems. Complex threat patterns and malicious actors are able to cause severe damages to cyber-systems. In this work, we propose novel Deep Learning formulations for detecting threats and alerts on network logs that were acquired by pfSense, an open-source software that acts as firewall on FreeBSD operating system. pfSense integrates several powerful security services such as firewall, URL filtering, and virtual private networking among others. The main goal of this study is to analyse the logs that were acquired by a local installation of pfSense software, in order to provide a powerful and efficient solution that controls traffic flow based on patterns that are automatically learnt via the proposed, challenging DL architectures. For this purpose, we exploit the Convolutional Neural Networks (CNNs), and the Long Short Term Memory Networks (LSTMs) in order to construct robust multi-class classifiers, able to assign each new network log instance that reaches our system into its corresponding category. The performance of our scheme is evaluated by conducting several quantitative experiments, and by comparing to state-of-the-art formulations.
Konstantina Fotiadou; Terpsichori-Helen Velivassaki; Artemis Voulkidis; Dimitrios Skias; Sofia Tsekeridou; Theodore Zahariadis. Network Traffic Anomaly Detection via Deep Learning. Information 2021, 12, 215 .
AMA StyleKonstantina Fotiadou, Terpsichori-Helen Velivassaki, Artemis Voulkidis, Dimitrios Skias, Sofia Tsekeridou, Theodore Zahariadis. Network Traffic Anomaly Detection via Deep Learning. Information. 2021; 12 (5):215.
Chicago/Turabian StyleKonstantina Fotiadou; Terpsichori-Helen Velivassaki; Artemis Voulkidis; Dimitrios Skias; Sofia Tsekeridou; Theodore Zahariadis. 2021. "Network Traffic Anomaly Detection via Deep Learning." Information 12, no. 5: 215.
Autonomous fault detection plays a major role in the Critical Energy Infrastructure (CEI) domain, since sensor faults cause irreparable damage and lead to incorrect results on the condition monitoring of Cyber-Physical (CP) systems. This paper focuses on the challenging application of wind turbine (WT) monitoring. Specifically, we propose the two challenging architectures based on learning deep features, namely—Long Short Term Memory-Stacked Autoencoders (LSTM-SAE), and Convolutional Neural Network (CNN-SAE), for semi-supervised fault detection in wind CPs. The internal learnt features will facilitate the classification task by assigning each upcoming measurement into its corresponding faulty/normal operation status. To illustrate the quality of our schemes, their performance is evaluated against real-world’s wind turbine data. From the experimental section we are able to validate that both LSTM-SAE and CNN-SAE schemes provide high classification scores, indicating the high detection rate of the fault level of the wind turbines. Additionally, slight modification on our architectures are able to be applied on different fault/anomaly detection categories on variant Cyber-Physical systems.
Konstantina Fotiadou; Terpsichori Helen Velivassaki; Artemis Voulkidis; Dimitrios Skias; Corrado De Santis; Theodore Zahariadis. Proactive Critical Energy Infrastructure Protection via Deep Feature Learning. Energies 2020, 13, 2622 .
AMA StyleKonstantina Fotiadou, Terpsichori Helen Velivassaki, Artemis Voulkidis, Dimitrios Skias, Corrado De Santis, Theodore Zahariadis. Proactive Critical Energy Infrastructure Protection via Deep Feature Learning. Energies. 2020; 13 (10):2622.
Chicago/Turabian StyleKonstantina Fotiadou; Terpsichori Helen Velivassaki; Artemis Voulkidis; Dimitrios Skias; Corrado De Santis; Theodore Zahariadis. 2020. "Proactive Critical Energy Infrastructure Protection via Deep Feature Learning." Energies 13, no. 10: 2622.
5G is considered to be the technology that will accommodate the development and management of innovative services with stringent and diverse requirements from end users, calling for new business models from the industry. In this context, the development and efficient management of Network Services (NS) serving specific vertical industries and spanning across multiple administrative domains and heterogeneous infrastructures is challenging. The main challenges regard the efficient provision of NSs considering the Quality of Service (QoS) requirements per vertical industry along with the optimal usage of the allocated resources. Towards addressing these challenges, this paper details an innovative approach that we have developed for managing and orchestrating such NSs, called SONATA, and compare it with OSM and Cloudify, which are two of the most known open-source Management and Orchestration (MANO) frameworks. In addition to examining the supported orchestration mechanisms per MANO framework, an evaluation of main operational and functional KPIs is provided based on experimentation using a real testbed. The final aim is the identification of their strong and weak points, and the assessment of their suitability for serving diverse vertical industry needs, including of course the Internet of Things (IoT) service ecosystem.
Panagiotis Trakadas; Panagiotis Karkazis; Helen C. Leligou; Theodore Zahariadis; Felipe Vicens; Arturo Zurita; Pol Alemany; Thomas Soenen; Carlos Parada; Jose Bonnet; Eleni Fotopoulou; Anastasios Zafeiropoulos; Evgenia Kapassa; Marios Touloupou; Dimosthenis Kyriazis. Comparison of Management and Orchestration Solutions for the 5G Era. Journal of Sensor and Actuator Networks 2020, 9, 4 .
AMA StylePanagiotis Trakadas, Panagiotis Karkazis, Helen C. Leligou, Theodore Zahariadis, Felipe Vicens, Arturo Zurita, Pol Alemany, Thomas Soenen, Carlos Parada, Jose Bonnet, Eleni Fotopoulou, Anastasios Zafeiropoulos, Evgenia Kapassa, Marios Touloupou, Dimosthenis Kyriazis. Comparison of Management and Orchestration Solutions for the 5G Era. Journal of Sensor and Actuator Networks. 2020; 9 (1):4.
Chicago/Turabian StylePanagiotis Trakadas; Panagiotis Karkazis; Helen C. Leligou; Theodore Zahariadis; Felipe Vicens; Arturo Zurita; Pol Alemany; Thomas Soenen; Carlos Parada; Jose Bonnet; Eleni Fotopoulou; Anastasios Zafeiropoulos; Evgenia Kapassa; Marios Touloupou; Dimosthenis Kyriazis. 2020. "Comparison of Management and Orchestration Solutions for the 5G Era." Journal of Sensor and Actuator Networks 9, no. 1: 4.
Tele-immersive 3D communications pose significant challenges in networking research and request for efficient construction of overlay networks, to guarantee the efficient delivery. In the last couple of years various overlay construction methods have been subject to many research projects and studies. However, in most cases, the selection of the overlay nodes is mainly based on network and geographic only criteria. In this book chapter, we focus on the social interaction of the participants and the way that social interaction could be taken into account for the construction of a network multicasting overlay. More precisely, we mine information from the social network structures and correlate it with network characteristics to select the nodes that could potentially serve more than one users, and thus contribute toward the overall network overlay optimization.
Theodore Zahariadis; Ioannis Koufoudakis; Helen C. Lelligou; Lambros Sarakis; Panagiotis Karkazis. Utilizing Social Interaction Information for Efficient 3D Immersive Overlay Communications. Novel 3D Media Technologies 2014, 225 -240.
AMA StyleTheodore Zahariadis, Ioannis Koufoudakis, Helen C. Lelligou, Lambros Sarakis, Panagiotis Karkazis. Utilizing Social Interaction Information for Efficient 3D Immersive Overlay Communications. Novel 3D Media Technologies. 2014; ():225-240.
Chicago/Turabian StyleTheodore Zahariadis; Ioannis Koufoudakis; Helen C. Lelligou; Lambros Sarakis; Panagiotis Karkazis. 2014. "Utilizing Social Interaction Information for Efficient 3D Immersive Overlay Communications." Novel 3D Media Technologies , no. : 225-240.