@article {798,
	title = {On the Design of IoT Security: Analysis of Software Vulnerabilities for Smart Grids},
	journal = {Energies},
	volume = {14},
	year = {2021},
	month = {may},
	pages = {2818},
	abstract = {<p>The 5G communication network will underpin a vast number of new and emerging services, paving the way for unprecedented performance and capabilities in mobile networks. In this setting, the Internet of Things (IoT) will proliferate, and IoT devices will be included in many 5G application contexts, including the Smart Grid. Even though 5G technology has been designed by taking security into account, design provisions may be undermined by software-rooted vulnerabilities in IoT devices that allow threat actors to compromise the devices, demote confidentiality, integrity and availability, and even pose risks for the operation of the power grid critical infrastructures. In this paper, we assess the current state of the vulnerabilities in IoT software utilized in smart grid applications from a source code point of view. To that end, we identified and analyzed open-source software that is used in the power grid and the IoT domain that varies in characteristics and functionality, ranging from operating systems to communication protocols, allowing us to obtain a more complete view of the vulnerability landscape. The results of this study can be used in the domain of software development, to enhance the security of produced software, as well as in the domain of automated software testing, targeting improvements to vulnerability detection mechanisms, especially with a focus on the reduction of false positives.</p>
},
	keywords = {Internet of Things, robustness, security, software vulnerabilities},
	doi = {10.3390/en14102818},
	author = {Christos-Minas Mathas and Costas Vassilakis and Nicholas Kolokotronis and Charilaos C. Zarakovitis and Michail-Alexandros Kourtis}
}
@conference {mathas2018a,
	title = {Evaluation of Apache Spot\textquotesingles machine learning capabilities in an SDN/NFV enabled environment},
	booktitle = {Proceedings of the 13th International Conference on Availability, Reliability and Security - ARES 2018},
	year = {2018},
	publisher = {ACM Press},
	organization = {ACM Press},
	abstract = {Software Defined Networking (SDN) and Network Function Virtualisation (NFV) are transforming modern networks towards a service-oriented architecture. At the same time, the cybersecurity industry is rapidly adopting Machine Learning (ML) algorithms to improve detection and mitigation of complex attacks. Traditional intrusion detection systems perform signature-based detection, based on well-known malicious traffic patterns that signify potential attacks. The main drawback of this method is that attack patterns need to be known in advance and signatures must be preconfigured. Hence, typical systems fail to detect a zero-day attack or an attack with unknown signature. This work considers the use of machine learning for advanced anomaly detection, and specifically deploys the Apache Spot ML framework on an SDN/NFV-enabled testbed running cybersecurity services as Virtual Network Functions (VNFs). VNFs are used to capture traffic for ingestion by the ML algorithm and apply mitigation measures in case of a detected anomaly. Apache Spot utilises Latent Dirichlet Allocation to identify anomalous traffic patterns in Netflow, DNS and proxy data. We evaluate the overall performance of Apache Spot by deploying Denial of Service (Slowloris, BoNeSi) and a Data Exfiltration attack (iodine).},
	keywords = {Apache Spot, Latent Dirichlet Allocation, Machine Learning, Network Function Virtualisation, Penetration Testing, SHIELD Project�, Software Defined Networking},
	doi = {10.1145/3230833.3233278},
	author = {Christos M. Mathas and Olga E. Segou and Georgios Xylouris and Dimitris Christinakis and Michail-Alexandros Kourtis and Costas Vassilakis and Anastasios Kourtis}
}
