Vehicular ad hoc Networks (VANETs) are emerged mainly to improve road safety, traffic efficiency, and passenger comfort. The performance of most VANET applications and services relies on the availability of accurate and recent mobility-information, so-called Cooperative Awareness Messages (CAM), shared by neighboring vehicles. However, misbehaving in terms of sharing false mobility information can disrupt any potential VANET application. Because cryptographic solutions in VANET are expensive, complicated, and vulnerable to internal misbehavior, security lapses are inevitable. Therefore, misbehavior detection is an important security component. Unfortunately, existing misbehavior detection solutions lack considering the high dynamicity of vehicular context which leads to low detection accuracy and high false alarms. The use of predefined and static security thresholds are the main drawbacks of the existing solutions. In this paper, a context-aware misbehavior detection scheme (CAMDS) is proposed using sequential analysis of temporal and spatial correlation of neighboring vehicles' mobility information. A dynamic context reference is constructed online and timely updated using statistical techniques. Firstly, the Kalman filter algorithm is used to track the mobility information received from neighboring vehicles. Then, the innovation errors of the Kalman filter are utilized to construct a temporal consistency assessment model for each neighboring vehicle using Box-plot. Then, the Hampel filter is used to construct a spatial consistency assessment model that represents the current context reference model. Similarly, plausibility assessment reference models are built online and timely updated using the Hampel filter and by utilizing the consistency assessment reference model of neighboring information. Finally, a message is classified as suspicious if its consistency and plausibility scores deviate much from the context reference model. The proposed context-aware scheme achieved a 73% reduction in false Alarm rate while it achieves a 37% improvement in the detection rate. This proves the effectiveness of the proposed scheme compared with the existing static solutions.

Generation of chaos from acousto-optic (A-O)Bragg cell modulators with an electronic feedback has been studied for over 3 decades. Since an acousto-optic Bragg cell with zeroth-and first-order feedback exhibits chaotic behavior past the threshold for bistability, such a system was recently examined for possible chaotic encryption of simple messages (such as a low-amplitude sinusoidal signal) applied via the bias input of the sound cell driver. Subsequent recovery of the message signal was carried out via a heterodyne-type strategy employing a locally generated chaotic carrier, with threshold parameters matched to the transmitting Bragg cell. In this paper, we present numerical results and detailed interpretations for signal encryption and recovery under hybrid A-O electronic feedback using a heterodyne strategy. Important features of this setup, such as the system robustness in terms of parameter matching (feedback gain, dc bias, and time delay) are also examined in some detail. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE). IntroductionIn an acousto-optic (A-O) modulator, an RF signal applied to a piezo-electric transducer, bonded to a suitable crystal, will generate an acoustic wave. It is well known that the acoustic wave acts like a "phase grating" that diffracts any incident laser beam into a number of diffracted orders. The Raman-Nath regime is characterized by multiple scattered orders while in the Bragg regime there are typically only two scattered orders (zeroth-and first-orders). 1 Around 1978, it was reported that A-O devices with positive feedback gain exhibit bistability characteristics. 2 In an A-O device, the amplitude of the diffracted fields that operate in the Bragg regime, i.e., the zeroth-and first-orders, which appear at the output of the Bragg cell, are related through a set of coupled differential equations. In Refs. 3 and 4, Chrostowski and co-workers present experimental results for A-O bistability and chaos using an equivalent circuit model of the Bragg cell with feedback. In a standard setup, a Bragg cell is driven by an ultrasonic sound wave from an RF generator at 40 MHz, and the resulting sound grating diffracts an incident He-Ne laser beam into the first Bragg order under Bragg condition. The first-order is then picked up by a linear photodetector, fed to an amplifier, and then returned to the bias input of the RF generator. The arrangement is shown in Fig. 1. Nominally, the scattered light beam is intrinsically frequency or phase modulated (with the acoustic frequency). In typical waveform sources, the external bias input amplitude modulates the RF waveform. A plot of the first order intensity (I 1 ) versus the bias inputα 0 yields the well known bistable and hysteretic behavior. 3,4 The bistability and hysteresis characteristics depend strongly on the feedback gain (β), the feedback time delay (TD), and the amplitude (I inc ) of the incident

Web applications have become ubiquitous for many business sectors due to their platform independence and low operation cost. Billions of users are visiting these applications to accomplish their daily tasks. However, many of these applications are either vulnerable to web defacement attacks or created and managed by hackers such as fraudulent and phishing websites. Detecting malicious websites is essential to prevent the spreading of malware and protect end-users from being victims. However, most existing solutions rely on extracting features from the website’s content which can be harmful to the detection machines themselves and subject to obfuscations. Detecting malicious Uniform Resource Locators (URLs) is safer and more efficient than content analysis. However, the detection of malicious URLs is still not well addressed due to insufficient features and inaccurate classification. This study aims at improving the detection accuracy of malicious URL detection by designing and developing a cyber threat intelligence-based malicious URL detection model using two-stage ensemble learning. The cyber threat intelligence-based features are extracted from web searches to improve detection accuracy. Cybersecurity analysts and users reports around the globe can provide important information regarding malicious websites. Therefore, cyber threat intelligence-based (CTI) features extracted from Google searches and Whois websites are used to improve detection performance. The study also proposed a two-stage ensemble learning model that combines the random forest (RF) algorithm for preclassification with multilayer perceptron (MLP) for final decision making. The trained MLP classifier has replaced the majority voting scheme of the three trained random forest classifiers for decision making. The probabilistic output of the weak classifiers of the random forest was aggregated and used as input for the MLP classifier for adequate classification. Results show that the extracted CTI-based features with the two-stage classification outperform other studies’ detection models. The proposed CTI-based detection model achieved a 7.8% accuracy improvement and 6.7% reduction in false-positive rates compared with the traditional URL-based model.

An acousto-optic Bragg cell with first-order feedback, which exhibits chaotic behavior past the threshold for bistability, was recently examined for possible chaotic encryption and recovery of simple messages (such as low-amplitude periodic signals) applied via the bias input of the sound cell driver. We carry out a thorough examination of the nonlinear dynamics of the Bragg cell under intensity feedback for (i) dc variations of the feedback gain (β) and the phase shift parameter (α 0 ) and (ii) ac variations ofα 0;total under signal encryption, investigating both from two different perspectives: (i) examining chaos in view of the so-called Lyapunov exponent derived recently by Ghosh and Verma and (ii) examining chaos in terms of the familiar bifurcation maps of intensity plotted against the feedback gain and the effective bias. It is shown that overall, the nonlinear dynamical results using the two approaches broadly agree, both for dc (fixed-parameter) analyses and, more importantly, when applied to the case of ac signal encryption cases. This affirms the effectiveness of the nonlinear dynamical theory in predicting and tracking the actual physical behavior of this system for message signal transmission and recovery under complex chaotic encryption.Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 02/20/2015 Terms of Use: http://spiedl.org/terms Al-Saedi and Chatterjee: Examination of the nonlinear dynamics of a chaotic acousto-optic Bragg modulator : : :Optical Engineering

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