Signature-based Intrusion Detection Systems (SIDS) play a crucial role within the arsenal of security components of most organizations. They can find traces of known attacks in the network traffic or host events for which patterns or signatures have been pre-established. SIDS include standard packages of detection rulesets, but only those rules suited to the operational environment should be activated for optimal performance. However, some organizations might skip this tuning process and instead activate default off-the-shelf rulesets without understanding its implications and trade-offs. In this work, we help gain insight into the consequences of using predefined rulesets in the performance of SIDS. We experimentally explore the performance of three SIDS in the context of web attacks. In particular, we gauge the detection rate obtained with predefined subsets of rules for Snort, ModSecurity and Nemesida using seven attack datasets. We also determine the precision and rate of alert generated by each detector in a real-life case using a large trace from a public webserver. Results show that the maximum detection rate achieved by the SIDS under test is insufficient to protect systems effectively and is lower than expected for known attacks. Our results also indicate that the choice of predefined settings activated on each detector strongly influences its detection capability and false alarm rate. Snort and ModSecurity scored either a very poor detection rate (activating the less-sensitive predefined ruleset) or a very poor precision (activating the full ruleset). We also found that using various SIDS for a cooperative decision can improve the precision or the detection rate, but not both. Consequently, it is necessary to reflect upon the role of these open-source SIDS with default configurations as core elements for protection in the context of web attacks. Finally, we provide an efficient method for systematically determining which rules deactivate from a ruleset to significantly reduce the false alarm rate for a target operational environment. We tested our approach using Snort’s ruleset in our real-life trace, increasing the precision from 0.015 to 1 in less than 16 h of work.

When telecommunication infrastructure is damaged by natural disasters, creating a network that can handle voice channels can be vital for search and rescue missions. Unmanned Aerial Vehicles (UAV) equipped with WiFi access points could be rapidly deployed to provide wireless coverage to ground users. This WiFi access network can in turn be used to provide a reliable communication service to be used in search and rescue missions. We formulate a new problem for UAVs optimal deployment which considers not only WiFi coverage but also the mac sublayer (i.e., quality of service). Our goal is to dispatch the minimum number of UAVs for provisioning a WiFi network that enables reliable VoIP communications in disaster scenarios. Among valid solutions, we choose the one that minimizes energy expenditure at the user’s WiFi interface card in order to extend ground user’s smartphone battery life as much as possible. Solutions are found using well-known heuristics such as K-means clusterization and genetic algorithms. Via numerical results, we show that the IEEE 802.11 standard revision has a decisive impact on the number of UAVs required to cover large areas, and that the user’s average energy expenditure (attributable to communications) can be reduced by limiting the maximum altitude for drones or by increasing the VoIP speech quality.

Continuity of care requires the exchange of health information among organizations and care teams. The EU General Data Protection Regulation (GDPR) establishes that subject of care should give explicit consent to the treatment of her personal data, and organizations must obey the individual’s will. Nevertheless, few solutions focus on guaranteeing the proper execution of consents. We propose a service-oriented architecture, backed by blockchain technology, that enables: (1) tamper-proof and immutable storage of subject of care consents; (2) a fine-grained access control for protecting health data according to consents; and (3) auditing tasks for supervisory authorities (or subjects of care themselves) to assess that healthcare organizations comply with GDPR and granted consents. Standards for health information exchange and access control are adopted to guarantee interoperability. Access control events and the subject of care consents are maintained on a blockchain, providing a trusted collaboration between organizations, supervisory authorities, and individuals. A prototype of the architecture has been implemented as a proof of concept to evaluate the performance of critical components. The application of subject of care consent to control the treatment of personal health data in federated and distributed environments is a pressing concern. The experimental results show that blockchain can effectively support sharing consent and audit events among healthcare organizations, supervisory authorities, and individuals.

The new capabilities of autonomous cars can be used to mitigate to a large extent safety concerns and nuisance traditionally associated with double parking. In this paper double parking for autonomous cars is proposed as a new approach to temporarily increase parking capacity in locations in clear need for extra provision when best alternatives cannot be found. The basic requirements, operation, and procedures of the proposed solution are outlined. A curbside parking has been simulated implementing the suggested double parking operation and important advantages have been identified for drivers, the environment, and the city. Double parking can increase over 50% the parking capacity of a given area. Autonomous car owners would (at least) double their probabilities of finding parking compared to traditional drivers, saving cruising time and emissions. However, significant work and technological advances are still needed in order to make this feasible in the near future.

This paper formulates a new problem for the optimal placement of Unmanned Aerial Vehicles (UAVs) geared towards wireless coverage provision for Voice over WiFi (VoWiFi) service to a set of ground users confined in an open area. Our objective function is constrained by coverage and by VoIP speech quality and minimizes the ratio between the number of UAVs deployed and energy efficiency in UAVs, hence providing the layout that requires fewer UAVs per hour of service. Solutions provide the number and position of UAVs to be deployed, and are found using well-known heuristic search methods such as genetic algorithms (used for the initial deployment of UAVs), or particle swarm optimization (used for the periodical update of the positions). We examine two communication services: (a) one bidirectional VoWiFi channel per user; (b) single broadcast VoWiFi channel for announcements. For these services, we study the results obtained for an increasing number of users confined in a small area of 100 m² as well as in a large area of 10,000 m². Results show that the drone turnover rate is related to both users’ sparsity and the number of users served by each UAV. For the unicast service, the ratio of UAVs per hour of service tends to increase with user sparsity and the power of radio communication represents 14–16% of the total UAV energy consumption depending on ground user density. In large areas, solutions tend to locate UAVs at higher altitudes seeking increased coverage, which increases energy consumption due to hovering. However, in the VoWiFi broadcast communication service, the traffic is scarce, and solutions are mostly constrained only by coverage. This results in fewer UAVs deployed, less total power consumption (between 20% and 75%), and less sensitivity to the number of served users.