Daniel Villanueva

Security in cognitive wireless sensor networks. Challenges and open problems

A cognitive wireless sensor network (CWSN) is an emerging technology with great potential to avoid traditional wireless problems such as reliability. One of the major challenges CWSNs face today is security. A CWSN is a special network which has many constraints compared to a traditional wireless network and many different features compared to a traditional wireless sensor network. While security challenges have been widely tackled in traditional networks, this is a novel area in CWSNs. This article discusses a wide variety of attacks on CWSNs, their taxonomy and different security measures available to handle the attacks. Also, future challenges to be faced are proposed.

Improving Security for SCADA Sensor Networks with Reputation Systems and Self-Organizing Maps.

The reliable operation of modern infrastructures depends on computerized systems and Supervisory Control and Data Acquisition (SCADA) systems, which are also based on the data obtained from sensor networks. The inherent limitations of the sensor devices make them extremely vulnerable to cyberwarfare/cyberterrorism attacks. In this paper, we propose a reputation system enhanced with distributed agents, based on unsupervised learning algorithms (self-organizing maps), in order to achieve fault tolerance and enhanced resistance to previously unknown attacks. This approach has been extensively simulated and compared with previous proposals.

Improving security in WMNs with reputation systems and self-organizing maps

One of the most important problems of WMNs, that is even preventing them from being used in many sensitive applications, is the lack of security. To ensure security of WMNs, two strategies need to be adopted: embedding security mechanisms into the network protocols, and developing efficient intrusion detection and reaction systems. To date, many secure protocols have been proposed, but their role of defending attacks is very limited. We present a framework for intrusion detection in WMNs that is orthogonal to the network protocols. It is based on a reputation system, that allows to isolate ill-behaved nodes by rating their reputation as low, and distributed agents based on unsupervised learning algorithms (self-organizing maps), that are able to detect deviations from the normal behavior. An additional advantage of this approach is that it is quite independent of the attacks, and therefore it can detect and confine new, previously unknown, attacks. Unlike previous approaches, and due to the inherent insecurity of WMN nodes, we assume that confidentiality and integrity cannot be preserved for any single node.

Using Reputation Systems and Non-Deterministic Routing to Secure Wireless Sensor Networks

Security in wireless sensor networks is difficult to achieve because of the resource limitations of the sensor nodes. We propose a trust-based decision framework for wireless sensor networks coupled with a non-deterministic routing protocol. Both provide a mechanism to effectively detect and confine common attacks, and, unlike previous approaches, allow bad reputation feedback to the network. This approach has been extensively simulated, obtaining good results, even for unrealistically complex attack scenarios.

Bio-inspired enhancement of reputation systems for intelligent environments

Providing security to the emerging field of ambient intelligence will be difficult if we rely only on existing techniques, given their dynamic and heterogeneous nature. Moreover, security demands of these systems are expected to grow, as many applications will require accurate context modeling. In this work we propose an enhancement to the reputation systems traditionally deployed for securing these systems. Different anomaly detectors are combined using the immunological paradigm to optimize reputation system performance in response to evolving security requirements. As an example, the experiments show how a combination of detectors based on unsupervised techniques (self-organizing maps and genetic algorithms) can help to significantly reduce the global response time of the reputation system. The proposed solution offers many benefits: scalability, fast response to adversarial activities, ability to detect unknown attacks, high adaptability, and high ability in detecting and confining attacks. For these reasons, we believe that our solution is capable of coping with the dynamism of ambient intelligence systems and the growing requirements of security demands.

SORU: A Reconfigurable Vector Unit for Adaptable Embedded Systems

In this article we describe SORU, a reconfigurable instruction set processor architecture (RISP) specially designed for run-time self-adaptation in environments with tight resource and power restrictions. It allows to accelerate computationally intensive multimedia processing on portable/embedded devices while maintaining a low energy consumption. The experimental results show a mean speedup of 4 with half the energy consumption. The main datapath can be left in a hibernate state during more than 75% of the execution time in our experiments, what leads also to a significant reduction of energy consumption in the I-cache and the main datapath, including the register file.

Image Processing Based Services for Ambient Assistant Scenarios

Guaranteeing ubiquity and appropriateness of security and monitoring services provision to the users constitutes a priority issue for the authorities. This paper presents an innovative Wireless Personal Area Network architecture that takes advantage of some of the features provided by Intelligent Environments -large number of devices, heterogeneous networks and mobility enhancement- in order to adapt and personalise ambient conditions to the user profile. This system is based on image processing and its main aim is to provide an AAL solution that is integrated with other control devices for the home to make everyday tasks easier for users.

Cognitive Wireless Sensor Network Device for AAL Scenarios

Cognitive Networks appear as solution to avoid AAL scenarios problems like wireless interferences, secure a reliable communications, power consumption or different Quality of Service application needs. In this paper a prototype for Cognitive Wireless Sensor Networks is presented. This node is a hardware solution that will help to overcome problems in AAL Sensors Networks. The prototype, with three different radio interfaces, is looking for creating a network which can auto-optimize communications in real time related to different application needs. This solution allows developers to implement new applications and services for AAL scenarios.

Detecting and Confining Sybil Attack in Wireless Sensor Networks Based on Reputation Systems Coupled with Self-organizing Maps

The Sybil attack is one of the most aggressive and evasive attacks in sensor networks that can affect on many aspects of network functioning. Thus, its efficient detection is of highest importance. In order to resolve this issue, in this work we propose to couple reputation systems with agents based on self-organizing map algorithm trained for detecting outliers in data. The response of the system consists in assigning low reputation values to the compromised node rendering them isolated from the rest of the network. The main improvement of this work consists in the way of calculating reputation, which is more flexible and discriminative in distinguishing attacks from normal behavior. Self-organizing map algorithm deploys feature space based on sequences of sensor outputs. Our solution offers many benefits: scalable solution, fast response to adversarial activities, ability to detect unknown attacks, high adaptability and low consumption. The testing results demonstrate its high ability in detecting and confining Sybil attack.

Using clustering techniques for intelligent camera-based user interfaces

The area of Human-Machine Interface is growing fast due to its high importance in all technological systems. The basic idea behind designing human-machine interfaces is to enrich the communication with the technology in a natural and easy way. Gesture interfaces are a good example of transparent interfaces. Such interfaces must identify properly the action the user wants to perform, so the proper gesture recognition is of the highest importance. However, most of the systems based on gesture recognition use complex methods requiring high-resource devices. In this work, we propose to model gestures capturing their temporal properties, which significantly reduce storage requirements, and use clustering techniques, namely self-organizing maps and unsupervised genetic algorithm, for their classification. We further propose to train a certain number of algorithms with different parameters and combine their decision using majority voting in order to decrease the false positive rate. The main advantage of the approach is its simplicity, which enables the implementation using devices with limited resources, and therefore low cost. The testing results demonstrate its high potential.