Roberto Magán-Carrión

Optimal relay placement in multi-hop wireless networks

Relay node placement in wireless environments is a research topic recurrently studied in the specialized literature. A variety of network performance goals, such as coverage, data rate and network lifetime, are considered as criteria to lead the placement of the nodes. In this work, a new relay placement approach to maximize network connectivity in a multi-hop wireless network is presented. Here, connectivity is defined as a combination of inter-node reachability and network throughput. The nodes are placed following a two-step procedure: (i) initial distribution, and (ii) solution selection. Additionally, a third stage for placement optimization is optionally proposed to maximize throughput. This tries to be a general approach for placement, and several initialization, selection and optimization algorithms can be used in each of the steps. For experimentation purposes, a leave-one-out selection procedure and a PSO related optimization algorithm are employed and evaluated for second and third stages, respectively. Other node placement solutions available in the literature are compared with the proposed one in realistic simulated scenarios. The results obtained through the properly devised experiments show the improvements achieved by the proposed approach.

A model of data forwarding in MANETs for lightweight detection of malicious packet dropping

This work introduces a model of data forwarding in MANETs which is used for recognizing malicious packet dropping behaviors. First, different legitimate packet discard situations are modeled, such as those generated by collisions, channel errors or mobility related droppings. Second, we propose an anomaly-based IDS system based on an enhanced windowing method to carry out the collection and analysis of selected cross-layer features. Third, a real deployment of the IDS is also considered by suggesting a methodology for the collection of the selected features in a distributed manner. We evaluate our proposal in a simulation framework and the experimental results show a considerable enhancement in detection results when compared with other approaches in the literature. For instance, our scheme shows a 22% improvement in terms of true positives rate and a remarkable 83% improvement in terms of false positives rate when compared to previous well-known statistical solutions. Finally, it is notable the simplicity and lightweightness of the proposal.

Multivariate statistical approach for anomaly detection and lost data recovery in wireless sensor networks

Data loss due to integrity attacks or malfunction constitutes a principal concern in wireless sensor networks (WSNs). The present paper introduces a novel data loss/modification detection and recovery scheme in this context. Both elements, detection and data recovery, rely on a multivariate statistical analysis approach that exploits spatial density, a common feature in network environments such as WSNs. To evaluate the proposal, we consider WSN scenarios based on temperature sensors, both simulated and real. Furthermore, we consider three different routing algorithms, showing the strong interplay among (a) the routing strategy, (b) the negative effect of data loss on the network performance, and (c) the data recovering capability of the approach. We also introduce a novel data arrangement method to exploit the spatial correlation among the sensors in a more efficient manner. In this data arrangement, we only consider the nearest nodes to a given affected sensor, improving the data recovery performance up to 99%. According to the results, the proposed mechanisms based on multivariate techniques improve the robustness of WSNs against data loss.

NETA: Evaluating the Effects of NETwork Attacks. MANETs as a Case Study

This work introduces NETA, a novel framework for the simulation of communication networks attacks. It is built on top of the INET framework and the OMNET++ simulator, using the generally accepted implementations of many different protocols, as well as models for mobility, battery consumption, channel errors, etc. NETA is intended to become an useful framework for researchers focused on the network security field. Its flexible design is appropriate for the implementation and evaluation of many types of attacks, doing it accurate for the benchmarking of current defense solutions under same testing conditions or for the development of new defense techniques. As a proof of concept, three different attacks have been implemented in NETA. The capabilities of NETA are exhibited by evaluating the performance of the three implemented attacks under different MANET deployments.

A Multiagent Self-healing System against Security Incidents in MANETs

Few proposals exist in the literature where security in networks and communications is studied from a pro-active perspective. One of them is MARS, a self-healing system intended to mitigate the malicious effects of common threats in MANETs. MARS makes use of special agent nodes to recover the loss of connectivity due to the operation of malicious nodes in the environment. Despite the general good performance of MARS, this paper shows some situations in which it does not work properly. This is caused by an inappropriate behavior of the optimization objective function considered in MARS. To overcome this limitation, a couple of alternative functions are designed and evaluated. The effectiveness of the new proposals is validated through extensive experiments. The new optimization functions lead to an increase in the resilience and tolerance of the network against security threats, improving network survivability.

Tampered Data Recovery in WSNs through Dynamic PCA and Variable Routing Strategies

Wireless sensor networks (WSNs) are highly sensible to data integrity attacks, which have an important impact on a number of relevant deployments and services. This paper introduces a tolerance approach to fight against data modification attacks in WSNs, which is based on a missing data imputation scheme. The proposal relies on two principal contributions: (1) a multivariate statistical technique where the dynamics of the sensor measurements for the monitored area are captured through the use of dynamic PCA (DPCA), and (2) a variable routing strategy that improves the recovering performance by spreading the effects of the data tampering attack. On the other hand, a complementary multivariate statistical anomaly detection module is implemented to determine the occurrence of data tampering attacks and trigger the subsequent reaction procedure to recover the affected data. As shown by the results obtained, the proposed tolerance approach improves the robustness of a WSN against data tampering attacks, and so its survivability and normal operation over time.

DRNS: Dynamical Relay Node Placement Solution

Relay node (RN) placement is a challenging problem, specially in mobile ad hoc networks (MANETs) environments where the locations of the nodes are continuously changing. In this context we propose here a novel, modular and adaptable RN placement solution based on the joint maximization of the network connectivity and throughput. For that, we use a combination of particle swarm optimization (PSO) and model predictive control (MPC) inspired methodologies to drive the RN movements. In order to corroborate the validity and applicability of the approach we implement, deploy and test the proposed solution in a real MANET environment where the nodes are mobile robots.

Networkmetrics: multivariate big data analysis in the context of the internet: Networkmetrics: multivariate big data analysis

Multivariate problems are found in all areas of knowledge. In chemistry and related disciplines, the chemometric community was developed in a joint effort to understand and solve problems mainly from a multivariate and exploratory perspective. This perspective is, indeed, of broader applicability, even in areas of knowledge far from chemistry. In this paper, we focus on the Internet: the net of devices that allow an interconnected world where all types of data can be shared and unprecedented communication services can be provided. Problems in the Internet or in general in networking are not very different from chemometric problems. Building on this parallelism, we review four classes of problems in networking: estimation, anomaly detection, optimization, and classification. We present an illustrative set of problems and show how a multivariate perspective may lead to significant improvements from state‐of‐the‐art techniques. In the absence of a better name, we call the approach of treating these problems from that multivariate perspective networkmetrics. Networkmetric problems have their own specificities, mainly, their typical Big Data nature and the presence of unstructured data. We argue that multivariate analysis is, indeed, useful to tackle these specificities. Copyright

A Security Response Approach Based on the Deployment of Mobile Agents

This paper introduces a response mechanism to improve the tolerance against security threats in MANET environments. The mechanism is started after detecting the existence of nodes with malicious behavior, and is based on the use of one or more mobile agents to improve the connectivity of the network. This way, in the event of the detection of a malicious node (e.g. a selfish node or a dropper node), an agent is employed to maximize the overall connectivity of the network. Every agent acts as a relaying node within the MANET and it is automatically positioned according to a particle swarm optimization (PSO) process. This paper represents a work in progress. However, the promising results obtained show the good suitability of the approach to improve the survivability of the network from a security perspective.

Network-wide intrusion detection supported by multivariate analysis and interactive visualization

In this paper, we introduce a new visualization tool for network-wide intrusion detection. It is based in multivariate anomaly detection with a combination between Principal Component Analysis (PCA) and a new variant called Group-wise PCA (GPCA). Combining these methodologies with the capabilities of interactive visualization, the resulting tool is a highly flexible and intuitive interface that allows the user to navigate through the enormous amount of data collected in the network, in order to find anomalous or unexpected behaviors. We use a real case study to illustrate the capability of the tool to unveil the complex mixture of information that can be found in network security/traffic data and identify and diagnose anomalies in it.