Grammatikis, Panagiotis Radoglou; Sarigiannidis, Panagiotis; Iturbe, Eider; Rios, Erkuden; Martinez, Saturnino; Sarigiannidis, Antonios; Efstathopoulos, Georgios; Spyridis, Yannis; Sesis, Achilleas; Vakakis, Nikolaos; Tzovaras, Dimitrios; Kafetzakis, Emmanouil; Giannoulakis, Ioannis; Tzifas, Michalis; Giannakoulias, Alkiviadis; Angelopoulos, Michail; Ramos, Francisco
In: Computer Networks, 2021.
The technological leap of smart technologies has brought the conventional electrical grid in a new digital era called Smart Grid (SG), providing multiple benefits, such as two-way communication, pervasive control and self-healing. However, this new reality generates significant cybersecurity risks due to the heterogeneous and insecure nature of SG. In particular, SG relies on legacy communication protocols that have not been implemented having cybersecurity in mind. Moreover, the advent of the Internet of Things (IoT) creates severe cybersecurity challenges. The Security Information and Event Management (SIEM) systems constitute an emerging technology in the cybersecurity area, having the capability to detect, normalise and correlate a vast amount of security events. They can orchestrate the entire security of a smart ecosystem, such as SG. Nevertheless, the current SIEM systems do not take into account the unique SG peculiarities and characteristics like the legacy communication protocols. In this paper, we present the Secure and PrivatE smArt gRid (SPEAR) SIEM, which focuses on SG. The main contribution of our work is the design and implementation of a SIEM system capable of detecting, normalising and correlating cyberattacks and anomalies against a plethora of SG application-layer protocols. It is noteworthy that the detection performance of the SPEAR SIEM is demonstrated with real data originating from four real SG use case (a) hydropower plant, (b) substation, (c) power plant and (d) smart home.
Radoglou-Grammatikis, Panagiotis; Sarigiannidis, Panagiotis; Efstathopoulos, George; Karypidis, Paris-Alexandros; Sarigiannidis, Antonios
In: Proceedings of the 15th International Conference on Availability, Reliability and Security, Association for Computing Machinery, Virtual Event, Ireland, 2020, ISBN: 9781450388337.
In this paper, an Intrusion Detection and Prevention System (IDPS) for the Distributed Network Protocol 3 (DNP3) Supervisory Control and Data Acquisition (SCADA) systems is presented. The proposed IDPS is called DIDEROT (Dnp3 Intrusion DetEction pReventiOn sysTem) and relies on both supervised Machine Learning (ML) and unsupervised/outlier ML detection models capable of discriminating whether a DNP3 network flow is related to a particular DNP3 cyberattack or anomaly. First, the supervised ML detection model is applied, trying to identify whether a DNP3 network flow is related to a specific DNP3 cyberattack. If the corresponding network flow is detected as normal, then the unsupervised/outlier ML anomaly detection model is activated, seeking to recognise the presence of a possible anomaly. Based on the DIDEROT detection results, the Software Defined Networking (SDN) technology is adopted in order to mitigate timely the corresponding DNP3 cyberattacks and anomalies. The performance of DIDEROT is demonstrated using real data originating from a substation environment.
Lytos, Anastasios; Lagkas, Thomas; Sarigiannidis, Panagiotis; Zervakis, Michalis; Livanos, George
In: Computer Networks, 172 , pp. 107147, 2020, ISSN: 1389-1286.
Agriculture is by its nature a complicated scientific field, related to a wide range of expertise, skills, methods and processes which can be effectively supported by computerized systems. There have been many efforts towards the establishment of an automated agriculture framework, capable to control both the incoming data and the corresponding processes. The recent advances in the Information and Communication Technologies (ICT) domain have the capability to collect, process and analyze data from different sources while materializing the concept of agriculture intelligence. The thriving environment for the implementation of different agriculture systems is justified by a series of technologies that offer the prospect of improving agricultural productivity through the intensive use of data. The concept of big data in agriculture is not exclusively related to big volume, but also on the variety and velocity of the collected data. Big data is a key concept for the future development of agriculture as it offers unprecedented capabilities and it enables various tools and services capable to change its current status. This survey paper covers the state-of-the-art agriculture systems and big data architectures both in research and commercial status in an effort to bridge the knowledge gap between agriculture systems and exploitation of big data. The first part of the paper is devoted to the exploration of the existing agriculture systems, providing the necessary background information for their evolution until they have reached the current status, able to support different platforms and handle multiple sources of information. The second part of the survey is focused on the exploitation of multiple sources of information, providing information for both the nature of the data and the combination of different sources of data in order to explore the full potential of ICT systems in agriculture.
Efstathopoulos, Georgios; Grammatikis, Panagiotis Radoglou; Sarigiannidis, Panagiotis; Argyriou, Vasilis; Sarigiannidis, Antonios; Stamatakis, Konstantinos; Angelopoulos, Michail K; Athanasopoulos, Solon K
Operational Data Based Intrusion Detection System for Smart Grid (Inproceedings)
In: 2019 IEEE 24th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), pp. 1-6, 2019.
With the rapid progression of Information and Communication Technology (ICT) and especially of Internet of Things (IoT), the conventional electrical grid is transformed into a new intelligent paradigm, known as Smart Grid (SG). SG provides significant benefits both for utility companies and energy consumers such as the two-way communication (both electricity and information), distributed generation, remote monitoring, self-healing and pervasive control. However, at the same time, this dependence introduces new security challenges, since SG inherits the vulnerabilities of multiple heterogeneous, co-existing legacy and smart technologies, such as IoT and Industrial Control Systems (ICS). An effective countermeasure against the various cyberthreats in SG is the Intrusion Detection System (IDS), informing the operator timely about the possible cyberattacks and anomalies. In this paper, we provide an anomaly-based IDS especially designed for SG utilising operational data from a real power plant. In particular, many machine learning and deep learning models were deployed, introducing novel parameters and feature representations in a comparative study. The evaluation analysis demonstrated the efficacy of the proposed IDS and the improvement due to the suggested complex data representation.
Lytos, Anastasios; Lagkas, Thomas; Sarigiannidis, Panagiotis; Bontcheva, Kalina
In: Information Processing & Management, 56 , 2019.
Argumentation mining is a rising subject in the computational linguistics domain focusing on extracting structured arguments from natural text, often from unstructured or noisy text. The initial approaches on modeling arguments was aiming to identify a flawless argument on specific fields (Law, Scientific Papers) serving specific needs (completeness, effectiveness). With the emerge of Web 2.0 and the explosion in the use of social media both the diffusion of the data and the argument structure have changed. In this survey article, we bridge the gap between theoretical approaches of argumentation mining and pragmatic schemes that satisfy the needs of social media generated data, recognizing the need for adapting more flexible and expandable schemes, capable to adjust to the argumentation conditions that exist in social media. We review, compare, and classify existing approaches, techniques and tools, identifying the positive outcome of combining tasks and features, and eventually propose a conceptual architecture framework. The proposed theoretical framework is an argumentation mining scheme able to identify the distinct sub-tasks and capture the needs of social media text, revealing the need for adopting more flexible and extensible frameworks.
Iliou, Theodoros; Konstantopoulou, Georgia; Lymperopoulou, Christina; Anastasopoulos, Konstantinos; Anastassopoulos, George; Margounakis, Dimitrios; Lymberopoulos, Dimitrios
In: Artificial Intelligence Applications and Innovations, pp. 512–519, Springer International Publishing, Cham, 2019, ISBN: 978-3-030-19823-7.
As real world data tends to be incomplete, noisy and inconsistent, data preprocessing is an important issue for data mining. Data preparation includes data cleaning, data integration, data transformation and data reduction. In this paper, Iliou preprocessing method is compared with Principal Component Analysis in suicide prediction according to family history. The dataset consists of 360 students, aged 18 to 24, who were experiencing family history problems. The performance of Iliou and Principal Component Analysis data preprocessing methods was evaluated using the 10-fold cross validation method assessing ten classification algorithms, IB1, J48, Random Forest, MLP, SMO, JRip, RBF, Na"ive Bayes, AdaBoostM1 and HMM, respectively. Experimental results illustrate that Iliou data preprocessing algorithm outperforms Principal Component Analysis data preprocessing method, achieving 100% against 71.34% classification performance, respectively. According to the classification results, Iliou preprocessing method is the most suitable for suicide prediction.
Lytos, Anastasios; Lagkas, Thomas; Sarigiannidis, Panagiotis; Bontcheva, Kalina
In: 12th Annual South-East European Doctoral Student Conference (DSC2018), 2018.
The field of Argumentation Mining has arisen from the need of determining the underlying causes from an expressed opinion and the urgency to develop the established fields of Opinion Mining and Sentiment Analysis. The recent progress in the wider field of Artificial Intelligence in combination with the available data through Social Web has create great potential for every sub-field of Natural Language Process including Argumentation Mining.