Cleonilson Protásio de Souza

A comparative study of use of shannon, rényi and tsallis entropy for attribute selecting in network intrusion detection

The selection of optimal attributes from the set of all possible attributes of a network traffic is the first step to detect network intrusions. However, in order to optimize the effectiveness of intrusion detection procedure and decrease its complexity, it is still a challenge to select an optimal attribute subset. In this context, the primary problem of attribute selection is the criterion to evaluate a given attribute subset. In this work, it is presented an evaluation of Rényi and Tsallis entropy performances compared with Shannon entropy in order to obtain an optimal attribute subset that increase the capability of the Intrusion Detection System to classify the traffic as normal or as suspicious. In the experimental results, the detection accuracy and the false alarm rate almost remains the same or even becomes better depending on the attack category (e.g. in the DoS and Probing attack) when small attribute subsets are used compared when all attributes are used.

A comparative study of use of Shannon, Rényi and Tsallis entropy for attribute selecting in network intrusion detection

The selection of optimal attributes from the set of all possible attributes of a network traffic is the first step to detect network intrusions. However, in order to optimize the effectiveness of intrusion detection procedure and decrease its complexity, it is still a challenge to select an optimal attribute subset. In this context, the primary problem of attribute selection is the criterion to evaluate a given attribute subset. In this work, it is presented an evaluation of Renyi and Tsallis entropy performances compared with Shannon entropy in order to obtain an optimal attribute subset that increase the capability of the Intrusion Detection System to classify the traffic as normal or as suspicious. In the experimental results, the detection accuracy and the false alarm rate almost remains the same or even becomes better depending on the attack category (e.g. in the DoS and Probing attack) when small attribute subsets are used compared when all attributes are used.

Decision Tree Based on Shannon, Rényi and Tsallis Entropies for Intrusion Tolerant Systems

This paper describes a comparative study of the use of Shannon, Renyi and Tsallis entropies for designing Decision Tree, with goal to find more efficient alternatives applied to Intrusion Tolerant Systems. Decision Tree has been used in classification model problems related to intrusion detection in networks, presenting good results. A very used decision tree is the C4.5 one that applies the Shannon entropy in order to choose the attributes that better divide data intoclasses. However, other ways to measure entropy, e.g., Tsallis and Renyi entropy, may be applied aiming at guaranteeing better generalization related to split criteria. Experimental results demonstrate that Tsallis and Renyi entropy can be used to construct more compact and efficient decision trees compared with Shannon entropy and these models can to provide more accurate intrusion tolerante systems.

An automatic thermal cycling based test platform for thermoelectric generator testing

Thermoelectric (TE) modules are solid-state devices that convert directly thermal energy in electrical energy. However, they can undergo performance degradation due to thermal cycling. In the present study, a control-based test platform that is capable to apply specific thermal cycling pattern at periodic intervals is presented in order to evaluate performance degradation that influence the TE module lifetime. Using this test platform, some parameters and the figure of merit ZT of a commercial TE module are measured before and after thermal cycling application. An important feature of the proposed platform is that the applied thermal cycling is bipolar, that is, it is possible to apply positive or negative temperature difference and, through this, performance degradation would be observed after only 548 thermal cycles much lower than previous works.

An artificial immune system-based anomaly detection method applied on a temperature control system

Artificial immune systems are algorithms inspired on biological immune systems and are being considered as one of the most promising nature-inspired strategies for optimisation and anomaly detection. In another way, the most common form of feedback in all areas, where automatic control is necessary, is the proportional-integral-derivative (PID) control. In this way, it is important to design methods in order to monitor PID controllers against failures, since a faulty PID controller may give rise to malfunctions or even breakdown, damages or accidents. In this paper, an immune system-based anomaly detection system applied to PID controller is presented. It has developed a complete PID controller based on Arduino architecture in order to control temperature of a heater. This system is interconnected to a LabVIEWTM-based tool, running on a personal computer (PC), which executes all phases of the proposed immune-based anomaly detection system. In this way, faults occurring in the system can be detected online. Experimental results show the efficiency of the proposed immune detection system.

Energy harvesting using magnetic induction considering different core materials

Energy harvesting is an emerging area with a wide range of low-energy applications and consists of capturing very small amounts of energy from one or more naturally-occurring energy sources (e.g. solar, thermal, wind, kinetic, etc.), accumulating and storing them. Recently, energy harvesting based on magnetic induction is gaining more and more attention because of their promising performance in power grid since energy can be harvested by the wasted magnetic field around power line using the principle of electrical transformer. However, the performance of the induction-based harvester presents very different results according to the magnetic core material used. In this work, it is presented experimental results on induction-based harvester considering the power density achieved by different magnetic core materials.

BMA-based reseeding technique

This paper presents a new scheme of Linear-Feedback Shift-Register (LFSR) reseeding technique based on Berlekamp-Massey Algorithm (BMA). It is proposed an LFSR-based TPG which its feedback polynomial is programmable by a BMA-synthesized LFSR that, in its turn, generates specific patterns to change the LFSR-based TPGs feedback polynomial. Using the proposed method, high fault coverage is achieved with a small number of test patterns. Experimental results have been shown the effectively of the proposed method.

A thermoelectric module thermal-cycling testing platform with automated measurement capabilities

Thermoelectric Modules (TEM) are used in areas such as precision temperature control applications, cooling system, energy harvesting, etc. The fully characterization of TEM is very important, mainly with respect to its degradation. In order to evaluate degradation, thermal cycling application is one of the most used methods. In this work it is described a complete thermal-cycling testing platform of TEM that is capable of measuring parameters such as Seebeck coefficient (α), thermal conductivity (λ), Figure of Merit (ZT) and others in an automatic way. Experimental results show the effectivity of the proposed testing platform.

On Harvesting Energy from Tree Trunks for Environmental Monitoring

This work describes an experimental study on the possibilities of harvesting energy from tree trunks in order to power sensor nodes for environmental monitoring, particularly in wild forests. As the trunk of a living tree can be divided into isothermal subvolumes, which are generally referred to as annual rings, and the trunk is a good heat storage material, depending on the tree dimensions and its species, it can potentially offer different temperature gradients according to the tree trunk depths. The hypothesis is to consider the application of this temperature gradient on the faces of a Peltier cell to obtain electrical energy. In order to evaluate this hypothesis, a wireless sensor network was developed for measuring internal temperature of trunks from different trees. The experimental results show that it is possible to obtain a sufficient temperature gradient to harvest energy from tree trunks. Additionally, it is also shown that it is possible to harvest thermal energy during the day and during the night while photovoltaic cell only works under sunlight.

Measurement equipment for characterization of ferromagnetic materials

Ferromagnetic materials have magnetic properties which determine its magnetic field conductivity characteristics. Such characteristics are extremely important for the proper selection and use of these materials in measurement processes, for example. Therefore, this paper presents an equipment for the characterization of ferromagnetic materials constructed in a laboratory. The experimental results of this equipment are acquired and compared with the experimental results obtained with an industrially manufactured apparatus called hysteresisgraph, designed to provide magnetic characterization of magnetic materials. The results of the comparison demonstrate that the equipment presented has low relative error and can be applied in academic projects which require magnetic characterization of ferromagnetic materials developed in the laboratory.