IoT botnets have been used to launch Distributed Denial-of-Service (DDoS) attacks affecting the Internet infrastructure. To protect the Internet from such threats and improve security mechanisms, it is critical to understand the botnets’ intents and characterize their behavior. Current malware analysis solutions, when faced with IoT, present limitations in regard to the network access containment and network traffic manipulation. In this paper, we present an approach for handling the network traffic generated by the IoT malware in an analysis environment. The proposed solution can modify the traffic at the network layer based on the actions performed by the malware. In our study case, we investigated the Mirai and Bashlite botnet families, where it was possible to block attacks to other systems, identify attacks targets, and rewrite botnets commands sent by the botnet controller to the infected devices.

Network slicing emerges as a key technology in next generation networks, boosted by the integration of software‐defined networking and network functions virtualization. However, while allowing resource sharing among multiple tenants, such networks must also ensure the security requirements needed for the scenarios they are employed. This letter presents the leading security challenges on the use of network slices at the packet core, the solutions that academy and industry are proposing to address them, pointing out some directions that should be considered.

Software-Defined Networking (SDN) is an emerging paradigm that arguably facilitates network innovation and simplifies network management. However, in the context of SDN, management activities, such as monitoring, visualization, and configuration can be considerably different from traditional networks. An SDN controller, for example, can be customized by network administrators according to their needs. Such customizations might pose an impact on resource consumption and traffic forwarding performance, which is difficult to assess without an SDN-devoted management system. In this paper, we initially present an analysis of control traffic in SDN aiming to better understand the impact of the communication between the controller and forwarding devices. Afterwards, we propose an interactive approach to SDN management through monitoring, visualization, and configuration that includes the administrator in the management loop. To show the feasibility of our approach a prototype has been developed. The results obtained with this prototype show that our approach can help the administrator to better understand the impact of configuring SDN-related parameters on the overall network performance.

Despite the large number of research efforts that applied specific machine learning algorithms for network traffic classification, recent work has highlighted limitations and particularities of individual algorithms that make them more suitable to specific types of traffic and scenarios. As such, an important topic in this area is how to combine individual algorithms using meta-learning techniques in order to obtain more robust traffic classification metrics. This paper presents a comparative analysis among meta-learning approaches and individual classifiers to classify network traffic. We investigate and evaluate a range of meta-learning techniques, including Voting, Stacking, Bagging and Boosting. We then propose a new experimental analysis of different meta-learning techniques - also known as ensemble learners- and compare them with their own base classifiers when used individually. Finally, considering the emerging popularity of Neural Networks, we analyze this scenario using the Multi-layer Perceptron classifier. The experiments were performed with data provided by the UCI Machine Learning Repository. The best performance was obtained by an ensemble technique (Bagging), which obtained accuracy of 99.972% and false positive rate of 0.00018%.

In this letter, we propose an airtime-based Resource Allocation (RA) model for network slicing in IEEE 802.11 Radio Access Networks (RANs). We formulate this problem as a Quadratically Constrained Quadratic Program (QCQP), where the overall queueing delay of the system is minimized while strict Ultra-Reliable Low Latency Communication (URLLC) constraints are respected. We evaluated our model using three different solvers where the optimal and feasible sets of airtime configurations were computed. We also validated our model with experimentation in real hardware. Our results show that the solution time for computing optimal and feasible configurations vary according to the slice's demand distribution and the number of slices to be allocated. Our findings support the need for precise RA over IEEE 802.11 RANs and present the limitations of performing such optimizations at runtime.