Evaluating Sequential Combination of Two Light-Weight Genetic Algorithm based Solutions to Intrusion Detection

In this work we have presented a genetic algorithm approach for classifying normal connections and intrusions. We have created a serial combination of two light-weight genetic algorithm-based intrusion detection systems where each of the systems exhibits certain deficiency. In this way we have managed to mitigate the deficiencies of both of them. The model was verified on KDD99 intrusion detection dataset, generating a solution competitive with the solutions reported by the state-ofthe- art, while using small subset of features from the original set that contains forty one features. The most significant features were identified by deploying principal component analysis and multi expression programming. Furthermore, our system is adaptable since it permits retraining by using new data

Modular Environment for Development and Characterization of Tunable Energy Harvesting Systems

This paper presents the design and development process for an electromagnetic self-tuned vibrational energy harvester prototype. Most state-of-the-art publications present non-tunable or manually tunable vibrational energy harvesters, even the market provides some commercial models of these categories for specific applications. On the other hand, self-tuned energy harvesters are yet rarely seen on the research community. The presented work follows the complete process of designing a prototype to work as a second-order oscillatory system in the form of a cantilever. Three different approaches to tune the resonant frequency of the harvester were considered, each based in changing a property of the cantilever that modifies its resonant frequency. Firstly, it was changed the effective vibrating length of the cantilever. Secondly it was introduced an axial load to the system. Then, the use of a dual cantilever wishbone structure was studied as it allows changing the equivalent stiffness of the system. Finally a prototype based on the first strategy was built and tested, including control algorithms for the maximum electrical energy harvesting point tracking which are presented

Unsupervised Genetic Algorithm Deployed for Intrusion Detection

This paper represents the first step in an on-going work for designing an unsupervised method based on genetic algorithm for intrusion detection. Its main role in a broader system is to notify of an unusual traffic and in that way provide the possibility of detecting unknown attacks. Most of the machinelearning techniques deployed for intrusion detection are supervised as these techniques are generally more accurate, but this implies the need of labeling the data for training and testing which is time-consuming and error-prone. Hence, our goal is to devise an anomaly detector which would be unsupervised, but at the same time robust and accurate. Genetic algorithms are robust and able to avoid getting stuck in local optima, unlike the rest of clustering techniques. The model is verified on KDD99 benchmark dataset, generating a solution competitive with the solutions of the state-of-the-art which demonstrates high possibilities of the proposed method

Data Privacy in Smart Electricity Networks

Smart Grids are amongst the most promising future developments to manage and control the energy consumption in the next decades. However, the integration and interdependencies that will evolve between the electricity power grid, telecommunication networks and ICT enable new threats and vulnerabilities to this critical infrastructure which must be addressed adequately with the right kind of security controls, balanced risk mitigation strategies and a continuous attention towards security, privacy and regulation aspects. It is an emerging area where new data privacy problems arise as mass rollout of smart meters is already happening

Evaluation, energy optimization, and spectrum analysis of an artificial noise technique to improve CWSN security

This paper presents the security evaluation, energy consumption optimization, and spectrum scarcity analysis of artificial noise techniques to increase physical-layer security in Cognitive Wireless Sensor Networks (CWSNs). These techniques introduce noise into the spectrum in order to hide real information. Nevertheless, they directly affect two important parameters in Cognitive Wireless Sensor Networks (CWSNs), energy consumption and spectrum utilization. Both are affected because the number of packets transmitted by the network and the active period of the nodes increase. Security evaluation demonstrates that these techniques are effective against eavesdropper attacks, but also optimization allows for the implementation of these approaches in low-resource networks such as Cognitive Wireless Sensor Networks. In this work, the scenario is formally modeled and the optimization according to the simulation results and the impact analysis over the frequency spectrum are presented

PUE attack detection in CWSN using collaboration and learning behavior

Cognitive Wireless Sensor Network (CWSN) is a new paradigm which integrates cognitive features in traditional Wireless Sensor Networks (WSNs) to mitigate important problems such as spectrum occupancy. Security in Cognitive Wireless Sensor Networks is an important problem because these kinds of networks manage critical applications and data. Moreover, the specific constraints of WSN make the problem even more critical. However, effective solutions have not been implemented yet. Among the specific attacks derived from new cognitive features, the one most studied is the Primary User Emulation (PUE) attack. This paper discusses a new approach, based on anomaly behavior detection and collaboration, to detect the PUE attack in CWSN scenarios. A nonparametric CUSUM algorithm, suitable for low resource networks like CWSN, has been used in this work. The algorithm has been tested using a cognitive simulator that brings important results in this area. For example, the result shows that the number of collaborative nodes is the most important parameter in order to improve the PUE attack detection rates. If the 20% of the nodes collaborates, the PUE detection reaches the 98% with less than 1% of false positives

A game theory based strategy for reducing energy consumption in cognitive WSN

Wireless sensor networks (WSNs) are one of the most important users of wireless communication technologies in the coming years and some challenges in this area must be addressed for their complete development. Energy consumption and spectrum availability are two of the most severe constraints of WSNs due to their intrinsic nature. The introduction of cognitive capabilities into these networks has arisen to face the issue of spectrum scarcity but could be used to face energy challenges too due to their new range of communication possibilities. In this paper a new strategy based on game theory for cognitive WSNs is discussed. The presented strategy improves energy consumption by taking advantage of the new change-communication-channel capability. Based on game theory, the strategy decides when to change the transmission channel depending on the behavior of the rest of the network nodes. The strategy presented is lightweight but still has higher energy saving rates as compared to noncognitive networks and even to other strategies based on scheduled spectrum sensing. Simulations are presented for several scenarios that demonstrate energy saving rates of around 65% as compared to WSNs without cognitive techniques

Computer workstation vetting by supply current monitoring

It is our goal within this project to develop a powerful electronic system capable to claim, with high certainty, that a malicious software is running (or not) along with the workstations’ normal activity. The new product will be based on measurement of the supply current taken by a workstation from the grid. Unique technique is proposed within these proceedings that analyses the supply current to produce information about the state of the workstation and to generate information of the presence of malicious software running along with the rightful applications. The testing is based on comparison of the behavior of a fault-free workstation (established i advance) and the behavior of the potentially faulty device

Fast and Accurate Computation of the Round-Off Noise of LTI Systems

From its introduction in the last decade, affine arithmetic (AA) has shown beneficial properties to speed up the time of computation procedures in a wide variety of areas. In the determination of the optimum set of finite word-lengths of the digital signal processing systems, the use of AA has been recently suggested by several authors, but the existing procedures provide pessimistic results. The aim is to present a novel approach to compute the round-off noise (RON) using AA which is both faster and more accurate than the existing techniques and to justify that this type of computation is restricted to linear time-invariant systems. By a novel definition of AA-based models, this is the first methodology that performs interval-based computation of the RON. The provided comparative results show that the proposed technique is faster than the existing numerical ones with an observed speed-up ranging from 1.6 to 20.48, and that the application of discrete noise models leads to results up to five times more accurate than the traditional estimations

A security scheme for wireless sensor networks

Security is critical for wireless sensor networks (WSN)deployed in hostile environments since many types of attacks could reduce the trust on the global functioning of any WSN. Many solutions have been proposed to secure communications for WSNs and most of them rely on a centralized component which behaves as a certificate authority. We propose in this paper a distributed solution able to ensure authentication of nodes at any time without having any on-line access to a certificate authority. Each node will be equipped with a Trusted Platform Module (TPM) which is able to store keys with security. Each node will have its own public key and private key pair in the TPM and a certificate of the public key. The certificate is issued off-line when setting-up the node. When a node communicates with another, it has to sign the message with its own private key (done securely by the TPM) and sends the message, the signature and the certificate of the public key. The evaluation of the solution has been done using simulation and the overhead added by integrating authentication does not exceed 15% of energy consumption