David Fraga

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.

Distributed intrusion detection system for wireless sensor networks based on a reputation system coupled with kernel self-organizing maps

Security of sensor networks is a complicated task, mostly due to the limited resources of sensor units. The first line of defense, i.e. encryption and authentication, is useless if an attacker has entered the system, and it is also vulnerable to side-channel attacks. Thus, a second line of defense, known as Intrusion Detection, must be added in order to detect and eliminate attacks. In the recent past, various solutions for detecting intrusions have been proposed. Most of them are able to detect only a limited number of attacks. Further, the solutions that deploy machine learning techniques exhibit higher level of flexibility and adaptability. Yet, these techniques consume significant power and computational resources. In this work we propose a distributed intrusion detection system organized as a reputation system where the reputation of each node is assigned by self-organizing maps (SOM) trained for detecting intrusions. The response of the system consists in assigning low reputation values to the compromised nodes rendering them isolated from the rest of the network. Further, we propose the implementation of SOM algorithm using the energy-efficient SORU (Stream Oriented Reconfigurable Unit) co-processor developed by our research group. Our solution offers many benefits: scalable solution, fast response to adversarial activities, ability to detect unknown attacks, high adaptability and energy efficiency. The testing results demonstrate its high potential.

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.

Detecting bad-mouthing attacks on reputation systems using self-organizing maps

It has been demonstrated that rating trust and reputation of individual nodes is an effective approach in distributed environments in order to improve security, support decision-making and promote node collaboration. Nevertheless, these systems are vulnerable to deliberate false or unfair testimonies. In one scenario the attackers collude to give negative feedback on the victim in order to lower or destroy its reputation. This attack is known as bad mouthing attack, and it can significantly deteriorate the performances of the network. The existing solutions for coping with bad mouthing are mainly concentrated on prevention techniques. In this work we propose a solution that detects and isolates the abovementioned attackers, impeding them in this way to further spread their malicious activity. The approach is based on detecting outliers using clustering, in this case self-organizing maps. An important advantage of this approach is that we have no restrictions on training data, and thus there is no need for any data pre-processing. Testing results demonstrates the capability of the approach in detecting bad mouthing attack in various 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.

A Taxonomy of Trust and Reputation System Attacks

Trust and reputation have been suggested as an effective security mechanism for open and distributed environments. However, even though there exists a high number of identified attacks against Trust and Reputation Systems (TRS), a generic security framework to identify all of them in a holistic way has not yet been proposed. This work presents a TRS attack taxonomy based on an TRS architectural model and a set of well-known security topics. Based on this taxonomy, two main deficiencies of the state-of-the-art in TRS security literature are detected: the existence of TRS attacks that have received few attention from the TRS community despite its importance, and potential threats that have not been previously identified. Finally, a real-life TRS scenario is analyzed as a proof-of-concept of the proposed taxonomy.

A Methodology for Developing Accessible Mobile Platforms over Leading Devices for Visually Impaired People

Mobile user interfaces are moving to new touchscreen technologies setting new barriers for the blind. Many solutions and designs have been proposed but none is complete for the vast heterogeneous variety of devices.

Improving Social Odometry Robot Networks with Distributed Reputation Systems for Collaborative Purposes

The improvement of odometry systems in collaborative robotics remains an important challenge for several applications. Social odometry is a social technique which confers the robots the possibility to learn from the others. This paper analyzes social odometry and proposes and follows a methodology to improve its behavior based on cooperative reputation systems. We also provide a reference implementation that allows us to compare the performance of the proposed solution in highly dynamic environments with the performance of standard social odometry techniques. Simulation results quantitatively show the benefits of this collaborative approach that allows us to achieve better performances than social odometry.

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.