@article{articledb,

title = {Strategic Honeypot Deployment in Ultra-Dense Beyond 5G Networks: A Reinforcement Learning Approach},

author = {Panagiotis Radoglou Grammatikis and Panagiotis Sarigiannidis and Panagiotis Diamantoulakis and Thomas Lagkas and Theocharis Saoulidis and Eleftherios Fountoukidis and George Karagiannidis},

url = {https://www.researchgate.net/publication/361139812_Strategic_Honeypot_Deployment_in_Ultra-Dense_Beyond_5G_Networks_A_Reinforcement_Learning_Approach},

doi = {10.1109/TETC.2022.3184112},

issn = {2168-6750},

year  = {2022},

date = {2022-06-01},

urldate = {2022-01-01},

journal = {IEEE Transactions on Emerging Topics in Computing},

abstract = {The progression of Software Defined Networking (SDN) and the virtualisation technologies lead to the beyond 5G era, providing multiple benefits in the smart economies. However, despite the advantages, security issues still remain. In particular, SDN/NFV and cloud/edge computing are related to various security issues. Moreover, due to the wireless nature of the entities, they are prone to a wide range of cyberthreats. Therefore, the presence of appropriate intrusion detection mechanisms is critical. Although both Machine Learning (ML) and Deep Learning (DL) have optimised the typical rule-based detection systems, the use of ML and DL requires labelled pre-existing datasets. However, this kind of data varies based on the nature of the respective environment. Another smart solution for detecting intrusions is to use honeypots. A honeypot acts as a decoy with the goal to mislead the cyberatatcker and protect the real assets. In this paper, we focus on Wireless Honeypots (WHs) in ultradense networks. In particular, we introduce a strategic honeypot deployment method, using two Reinforcement Learning (RL) techniques: (a) e−Greedy and (b) Q−Learning. Both methods aim to identify the optimal number of honeypots that can be deployed for protecting the actual entities. The experimental results demonstrate the efficacy of both methods.},

keywords = {Honeypot, Intrusion detection, ReinforcementLearning, Wireless communication},

pubstate = {published},

tppubtype = {article}

}

@article{RadoglouGrammatikis2021,

title = {SPEAR SIEM: A Security Information and Event Management system for the Smart Grid},

author = { P. Radoglou-Grammatikis and P. Sarigiannidis and E. Iturbe and E. Rios and S. Martinez and A. Sarigiannidis and G. Eftathopoulos and I. Spyridis and A. Sesis and N. Vakakis and D. Tzovaras and E. Kafetzakis and I. Giannoulakis and M. Tzifas and A. Giannakoulias and M. Angelopoulos and F. Ramos},

url = {https://www.researchgate.net/publication/350287201_SPEAR_SIEM_A_Security_Information_and_Event_Management_system_for_the_Smart_Grid},

doi = {10.1016/j.comnet.2021.108008},

year  = {2021},

date = {2021-04-01},

journal = {Computer Networks},

pages = {108008},

publisher = {Elsevier BV},

abstract = {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.},

keywords = {Anomaly Detection, Cybersecurity, Deep Learning, Intrusion detection, machine learning, SCADA, Security Information and Event Management, Smart Grid},

pubstate = {published},

tppubtype = {article}

}

@article{Loukas2019124,

title = {A taxonomy and survey of cyber-physical intrusion detection approaches for vehicles},

author = { G. Loukas and E. Karapistoli and E. Panaousis and P. Sarigiannidis and A. Bezemskij and T. Vuong},

url = {https://www.researchgate.net/publication/328025147_A_taxonomy_and_survey_of_cyber-physical_intrusion_detection_approaches_for_vehicles},

doi = {10.1016/j.adhoc.2018.10.002},

year  = {2019},

date = {2019-01-01},

journal = {Ad Hoc Networks},

volume = {84},

pages = {124-147},

abstract = {With the growing threat of cyber and cyber-physical attacks against automobiles, drones, ships, driverless pods and other vehicles, there is also a growing need for intrusion detection approaches that can facilitate defence against such threats. Vehicles tend to have limited processing resources and are energy-constrained. So, any security provision needs to abide by these limitations. At the same time, attacks against vehicles are very rare, often making knowledge-based intrusion detection systems less practical than behaviour-based ones, which is the reverse of what is seen in conventional computing systems. Furthermore, vehicle design and implementation can differ wildly between different types or different manufacturers, which can lead to intrusion detection designs that are vehicle-specific. Equally importantly, vehicles are practically defined by their ability to move, autonomously or not. Movement, as well as other physical manifestations of their operation may allow cyber security breaches to lead to physical damage, but can also be an opportunity for detection. For example, physical sensing can contribute to more accurate or more rapid intrusion detection through observation and analysis of physical manifestations of a security breach. This paper presents a classification and survey of intrusion detection systems designed and evaluated specifically on vehicles and networks of vehicles. Its aim is to help identify existing techniques that can be adopted in the industry, along with their advantages and disadvantages, as well as to identify gaps in the literature, which are attractive and highly meaningful areas of future research. © 2018 Elsevier B.V.},

keywords = {Aircraft, Automobiles, Cyber security, Cyber-physical systems, Driverless pods, Intrusion detection, Robotic land vehicles, unmanned aerial vehicles, VANET, Vehicles, Vehicular networks},

pubstate = {published},

tppubtype = {article}

}