Abstract. Medium Access Control (MAC) address spoofing is considered as an important first step in a hacker's attempt to launch a variety of attacks on 802.11 wireless networks. Unfortunately, MAC address spoofing is hard to detect. Most current spoofing detection systems mainly use the sequence number (SN) tracking technique, which has drawbacks. Firstly, it may lead to an increase in the number of false positives. Secondly, such techniques cannot be used in systems with wireless cards that do not follow standard 802.11 sequence number patterns. Thirdly, attackers can forge sequence numbers, thereby causing the attacks to go undetected. We present a new architecture called WISE GUARD (Wireless Security Guard) for detection of MAC address spoofing on 802.11 wireless LANs. It integrates three detection techniques -SN tracking, Operating System (OS) fingerprinting & tracking and Received Signal Strength (RSS) fingerprinting & tracking. It also includes the fingerprinting of Access Point (AP) parameters as an extension to the OS fingerprinting for detection of AP address spoofing. We have implemented WISE GUARD on a test bed using off-the-shelf wireless devices and open source drivers. Experimental results show that the new design enhances the detection effectiveness and reduces the number of false positives in comparison with current approaches.
Acoustic emission (AE) and vibration signal are significant criteria of damage identification in structural health monitoring (SHM) engineering. Multidisciplinary knowledge and synergistic parameter effects are technical challenges for damage assessment modelling. This study proposes a structural damage cause-and-effect analysis method based on parameter information entropy. Monitoring data is used to form a time-domain feature wave (TFW). The structural strength degradation factor (DF) would be used to define structural damage information entropy (SDIE) vector. The structural damage cause and effect model is developed in a probability sense. A fatigue index is adopted for damage assessment, and a causal strength index is proposed to locate the most likely damage cause. A sandstone-truss structure experiment was conducted to show that the proposed method is effective for damage evaluation and the experimental results provide strong support. This is a statistical damage identification method based on causal logic uncertainty, meaning a complicated mechanics calculation can be avoided. INDEX TERMS Structural health monitoring, acoustic emission, cause-and-effect analysis, parameter information entropy.
Featured Application: This study proposed an automatic AE feature parameters selection method. The potential application is the preprocessing of AE signal before low-permeability sandstone moisture identification.Abstract: Moisture is a vital factor in the structural stability of sandstone, which is the main component of low-permeability reservoir rocks. Hence, studies into moisture identification are crucial. Diverse information about rock, such as its structural and mechanical parameters, can be obtained from the acoustic emission (AE) signal. However, the types of AE parameters are varied, and the rock information that is represented by them is different. Traditional methods of parameter selection are mostly based on the correlation between parameters and the experience of researchers, which are not accurate when the correlation between parameters is fuzzy and does not meet automation requirements. In this study, a method of signal feature selection based on a data fluctuation rule and clustering analysis is proposed. This method takes the fluctuation law of the signal itself and the correlation degree of cluster labels as the basis, and the selection step is divided into two steps. An experimental platform is established, and uniaxial compression on sandstones with different moisture contents is carried out to verify the efficiency of this method. The selected feature parameters are used for moisture classification combined with a support vector machine (SVM) classifier, and the identification results verify the efficiency of energy security monitoring in low-permeability rocks. produced in a stress concentration region releases energy in the form of a transient elastic wave to achieve a steady state [4,5]. The excitation mode of AE is diverse. Besides the solid medium, AE can also be generated through liquid medium, and its application in detection engineering is extensive. For example, AE can be generated by cavitation phenomena and turbulent flows associated with fluid leaks [6]. Indeed, even if some drawbacks with respect to other methods may be identified, AE analysis has been successfully adopted for leak detection in pipelines [7,8]. In the field of rock engineering, AE stems from the particle slip and crack propagation in rock, and the frequency bands are mostly between 20-200 K, which are imperceptible to humans [9]. The signal feature parameters can be extracted through signal acquisition and processing technology; thus, the relation between the signal and rock damage state can be analyzed, and this method is called parametric analysis [10][11][12][13]. This method has been used successfully by many scholars to conduct structural health monitoring. Static tensile loading was conducted on aluminum plates in aerospace systems by Z. Kral et al., and the identification of the damaged part was realized with a combination of artificial neural networks [14]. H.Y. Sim et al. [15] realized the valve abnormalities detected in a reciprocating compressor by means of AE technology and wave packet transform. B.A....
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