The increase of connected devices and the constantly evolving methods and techniques by attackers pose a challenge for network intrusion detection systems from conception to operation. As a result, we see a constant adoption of machine learning algorithms for network intrusion detection systems. However, the dataset used by these studies has become obsolete regarding both background and attack traffic. This work describes the AB-TRAP framework that enables the use of updated network traffic and considers operational concerns to enable the complete deployment of the solution. AB-TRAP is a five-step framework consisting of ( i) the generation of the attack dataset, (ii) the bonafide dataset, (iii) training of machine learning models, (iv) realization (implementation) of the models, and ( v) the performance evaluation of the realized model after deployment. We exercised the AB-TRAP for local (LAN) and global (internet) environments to detect TCP port scanning attacks. The LAN study case presented an f1-score of 0.96, and an area under the ROC curve of 0.99 using a decision tree with minimal CPU and RAM usage on kernel-space. For the internet case with eight machine learning algorithms with an average f1-score of 0.95, an average area under the ROC curve of 0.98, and an average overhead of 1.4% CPU and 3.6% RAM on user-space in a single-board computer. This framework has the following paramount characteristics: it is reproducible, uses the most up-to-date network traffic, attacks, and addresses the concerns to the model's realization and deployment.

Multipoint communication is an important requirement for many types of applications such as videoconferencing, IPTV and online radio. However, the division of Internet in autonomous systems hinders the widespread adoption of traditional multicast protocols, which, for using distributed algorithms, delay the group control events processing. This paper proposes a multicast clean-slate approach logically centralized based on programmable networks and anticipated processing for all routes from each possible source, aiming to reduce event delays. A prototype was implemented based on OpenFlow technology. In addition, extensive evaluation was performed and results show promising delays comparable to the requirements of multipoint applications.

Drones can play a game-changing role in reducing both cost and time in the context of last-mile deliveries. This paper addresses the last-mile delivery problem from a complex system viewpoint, where the collective performance of the drones is investigated. We consider a last-mile delivery system with a tradable permit model (TPM) for airspace use. Typically, in other research works regarding lastmile delivery drones, a fully cooperative centralized scenario is contemplated. In our approach, due to the TPM, the agents (i.e. drones) need to compete for airspace permits in a distributed manner. We simulate the system and evaluate how different parameters, such as the arrival rate and airspace dimensions, impact the system behavior in terms of the cost and time needed by the drones to acquire flight permits, and the airspace utilization. We use a simplified simulation model, where the agents’ strategies are naïve, and the drones’ flight dynamics are not accounted for. Nevertheless, the simulation’s level of detail is adequate for capturing interesting properties from the agents’ collective behavior, as our results support. The obtained results show that the system’s performance is satisfactory, even with naïve agents and under high traffic conditions. Moreover, a real-world implementation of our competitive decentralized approach would lead to advantages, such as fast permit transactions, simple computational infrastructures, and error resilience.

According to recent studies, unmanned aerial vehicles can play a game-changing part in cost reduction and speed increase to address the last-mile delivery problem and attend to emergencies. Last-mile delivery services are getting more and more relevant, especially when in times when social distance is required. However, a systemic view of how to provide a feasible solution to enable this application as a vertical market in the urban context is still inexistent. Given this scenario, our paper contributes by proposing a cyber-physical system roadmap applicable to last-mile delivery drones. The proposed CPS guidelines are based on the system of systems to enable an enhanced operation towards smart cities' governance. This paper also discusses topics from air space control and reservation to communication infrastructure and decentralized control supported on a blockchain.

The growth in the number of connected vehicles in ad-hoc networks or VANETs enables the use of increasingly sophisticated services and applications. One emerging application in this scenario is Platooning, a stable stream of connected autonomous vehicles. This application improves safety, vehicle flow on the road, energy consumption, among other variables. However, vehicles using this technology need to share space with vehicles not connected or with other automation levels. Among the bottlenecks in scenarios like this, Platoons formation and splits stand out, as these maneuvers involve costs and risk. Therefore, our contribution is developing a new strategy for Platoons formation in a mixed universe of vehicles to bring some of these desired benefits. The results show that the proposed solution achieved an accuracy of 89% of the entire amount of platoons in the datasets. Our experiments demonstrate cases with collisions, and the proposed solution provides a solution to a more safe operation on roads. Finally, we conclude that our solution provides more determinism in the traffic conditions, as vehicles achieving stationary speeds with low variation.