Nowadays there are many DNS firewall solutions to prevent users accessing malicious domains. These can provide real-time protection and block illegitimate communications, contributing to the cybersecurity posture of the organizations. Most of these solutions are based on known malicious domain lists that are being constantly updated. However, in this way, it is only possible to block malicious communications for known malicious domains, leaving out many others that are malicious but have not yet been updated in the blocklists. This work provides a study to implement a DNS firewall solution based on ML and so improve the detection of malicious domain requests on the fly. For this purpose, a dataset with 34 features and 90 k records was created based on real DNS logs. The data were enriched using OSINT sources. Exploratory analysis and data preparation steps were carried out, and the final dataset submitted to different Supervised ML algorithms to accurately and quickly classify if a domain request is malicious or not. The results show that the ML algorithms were able to classify the benign and malicious domains with accuracy rates between 89% and 96%, and with a classification time between 0.01 and 3.37 s. The contributions of this study are twofold. In terms of research, a dataset was made public and the methodology can be used by other researchers. In terms of solution, the work provides the baseline to implement an in band DNS firewall.

Intrusion Detection Systems (IDS) are used to prevent attacks by detecting potential harmful intrusion attempts. Currently, there are a set of available Open Source IDS with different characteristics. The Open Source Host-based Intrusion Detection System (OSSEC) supports multiple features and its implementation consists of Agents that collect and send event logs to a Manager that analyzes and tests them against specific rules. In the Manager, if certain events match a specific rule, predefined actions are triggered in the Agents such as to block or unblock a particular IP address. However, once an action is triggered, the systems administrator is not able to centrally check and obtain detailed information of the past event logs. In addition, OSSEC may assume false positive or negative detections and their triggered actions: previously harmless but blocked IP addresses by OSSEC have to be unblocked in order to reestablish normal operation or potential harmful IP addresses not previously blocked by OSSEC should be blocked in order to increase protection levels. These operations to override OSSEC actions must be manually performed in every Agent, thus requiring time and human resources. Both these limitations have a higher impact on large scale OSSEC deployments assuming tens or hundreds of Agents. This paper proposes an extension to OSSEC that improves the administrator analysis capability by maintaining, organizing and presenting Agent logs in a central point, and it allows for blocking or unblocking IP addresses in order to override actions triggered by false detections. The proposed extension aims to increase efficiency of time and human resources management, mainly considering large scale OSSEC deployments.

In mobile networks, 5G Ultra-Dense Networks (UDNs) have emerged as they effectively increase the network capacity due to cell splitting and densification. A Base Station (BS) is a fixed transceiver that is the main communication point for one or more wireless mobile client devices. As UDNs are densely deployed, the number of BSs and communication links is dense, raising concerns about resource management with regard to energy efficiency, since BSs consume much of the total cost of energy in a cellular network. It is expected that 6G next-generation mobile networks will include technologies such as artificial intelligence as a service and focus on energy efficiency. Using machine learning it is possible to optimize energy consumption with cognitive management of dormant, inactive and active states of network elements. Reinforcement learning enables policies that allow sleep mode techniques to gradually deactivate or activate components of BSs and decrease BS energy consumption. In this work, a sleep mode management based on State Action Reward State Action (SARSA) is proposed, which allows the use of specific metrics to find the best tradeoff between energy reduction and Quality of Service (QoS) constraints. The results of the simulations show that, depending on the target of the 5G use case, in low traffic load scenarios and when a reduction in energy consumption is preferred over QoS, it is possible to achieve energy savings up to 80% with 50 ms latency, 75% with 20 ms and 10 ms latencies and 20% with 1 ms latency. If the QoS is preferred, then the energy savings reach a maximum of 5% with minimal impact in terms of latency.INDEX TERMS 5G, energy efficiency, sleep mode, reinforcement learning.

An intrusion detection system (IDS) is an important tool to prevent potential threats to systems and data. Anomaly-based IDSs may deploy machine learning algorithms to classify events either as normal or anomalous and trigger the adequate response. When using supervised learning, these algorithms require classified, rich, and recent datasets. Thus, to foster the performance of these machine learning models, datasets can be generated from different sources in a collaborative approach, and trained with multiple algorithms. This paper proposes a vote-based architecture to generate classified datasets and improve the performance of supervised learning-based IDSs. On a regular basis, multiple IDSs in different locations send their logs to a central system that combines and classifies them using different machine learning models and a majority vote system. Then, it generates a new and classified dataset, which is trained to obtain the best updated model to be integrated into the IDS of the companies involved. The proposed architecture trains multiple times with several algorithms. To shorten the overall runtimes, the proposed architecture was deployed in Fed4FIRE+ with Ray to distribute the tasks by the available resources. A set of machine learning algorithms and the proposed architecture were assessed. When compared with a baseline scenario, the proposed architecture enabled to increase the accuracy by 11.5% and the precision by 11.2%.