One of the innovations brought by Mirai and its derived malware is the adoption of self-contained loaders for infecting IoT devices and recruiting them in botnets. Functionally decoupled from other botnet components and not embedded in the payload, loaders cannot be analysed using conventional approaches that rely on honeypots for capturing samples. Different approaches are necessary for studying the loaders evolution and defining a genealogy. To address the insufficient knowledge about loaders' lineage in existing studies, in this paper, we propose a semantic-aware method to measure, categorize, and compare different loader servers, with the goal of highlighting their evolution, independent from the payload evolution. Leveraging behavior-based metrics, we cluster the discovered loaders and define eight families to determine the genealogy and draw a homology map. Our study shows that the source code of Mirai is evolving and spawning new botnets with new capabilities, both on the client side and the server side. In turn, shedding light on the infection loaders can help the cybersecurity community to improve detection and prevention tools.

Data storage is one of the main computing issues of this century. Not only storage devices are converging to strict physical limits, but also the amount of data generated by users is growing at an unbelievable rate. To face these challenges, data centres grew constantly over the past decades. However, this growth comes with a price, particularly from the environmental point of view. Among various promising media, DNA is one of the most fascinating candidate. In our previous work, we have proposed an automated archival architecture which uses bioengineered bacteria to store and retrieve data, previously encoded into DNA. The similarities between biological media and classical ones can be a drawback, as malicious parties might replicate traditional attacks on the former archival system, using biological instruments and techniques. In this paper, first we analyse the main characteristics of our storage system and the different types of attacks that could be executed on it. Then, aiming at identifying on-going attacks, we propose and evaluate detection techniques, which rely on traditional metrics and machine learning algorithms. We identify and adapt two suitable metrics for this purpose, namely generalized entropy and information distance.

IoT devices permeate our society, collect personal data, and support critical infrastructures such as the healthcare. Therefore, there is a critical need for authentication and authorization schemes for IoT devices to meet privacy requirements, such as mutual authentication and user anonymity, as well as robustness against security attacks. In this paper, we propose a device authentication and key agreement scheme for IoT networks. Our proposal takes as a model the scheme proposed by Rezai et al., and combines it with a physical layer security technique and a hyper-elliptic curve cryptosystem. Our results show that not only our authentication scheme provides anonymity, mutual authentication, and efficiency, but it also provides resilience to various attacks, including man-in-the-middle, replay, and de-synchronization attacks. Our comparison shows that our scheme performs better than the state-of-the-art in terms of security properties, while adding a small overhead of ≈10(ms).

Since the birth of computer and networks, fueled by pervasive computing, Internet of Things and ubiquitous connectivity, the amount of data stored and transmitted has exponentially grown through the years. Due to this demand, new storage solutions are needed. One promising media is the DNA as it provides numerous advantages, which includes the ability to store dense information while achieving long-term reliability. However, the question as to how the data can be retrieved from a DNA-based archive, still remains. In this paper, we aim to address this question by proposing a new storage solution that relies on bacterial nanonetworks properties. Our solution allows digitally-encoded DNA to be stored into motility-restricted bacteria, which compose an archival architecture of clusters, and to be later retrieved by engineered motile bacteria, whenever reading operations are needed. We conducted extensive simulations, in order to determine the reliability of data retrieval from motility-restricted storage clusters, placed spatially at different locations. Aiming to assess the feasibility of our solution, we have also conducted wet lab experiments that show how bacteria nanonetworks can effectively retrieve a simple message, such as 'Hello World,' by conjugation with motility-restricted bacteria, and finally mobilize towards a target point for delivery.