Due to increase in internet based services, the size of network traffic data has become so large and complex that it is very difficult to process with the traditional data processing tools. Fast and efficient cyber security intrusion detection is a very challenging problem due to big and complex nature of network traffic data. A realistic cyber security intrusion detection system should be able to process large size of network traffic data as fast as possible in order to detect the malicious traffic as early as possible. This paper used Apache Spark, a big data processing tool for processing the large size of network traffic data. In this paper, we have proposed a framework in which first a well-known feature selection algorithm is employed for selecting the most important features and then classification based intrusion detection method is used for fast and efficient detection of intrusion in the massive network traffic. In this work, we have used two well-known feature selection algorithm, namely, correlation based feature selection and Chi-squared feature selection and five well known classification based intrusion detection methods, namely, Logistic regression, Support vector machines, Random forest, Gradient Boosted Decision trees & Naive Bayes. A real time DARPA's KDD'99 data set is used to validate the proposed framework and performance comparison of classification based intrusion detection schemes are evaluated in terms of training time, prediction time, accuracy, sensitivity and specificity.
Collision strengths for transitions between the 3s23p4 P2, 1, 03, 1D2, and 1S0 levels and from these levels to the 3s3p5 P02, 1, 03 and 1P01 levels of Fe XI are calculated using a semirelativistic R-matrix approach. The 38 fine-structure levels arising from the 20 LS 3s23p4 P3, 1D, 1S, 3s3p5 P03, 1P0, 3s23p3(4S0)3d D03, 3s23p3(2P0)3d P01,3, 1, 3D0, 1, 3F0, 3s23p3(2D0)3d S01,3, 1, 3P0, 1, 3D0, 1, 3F0 states are included in our calculation. These target levels are represented by configuration-interaction wave functions. The relativistic effects are considered in the Breit-Pauli approximation by including one-body mass correction, Darwin term, and spin-orbit terms in the scattering equations. Complicated resonance structures are found to enhance the collision strengths significantly for many transitions in the threshold energy regions. Our collision strengths are compared with the available results of Bhatia & Doschek at 8.0, 16.0, and 24.0 ryd. The collision strengths are integrated over a Maxwellian distribution of electron energies to give effective collision strengths over a wide temperature range.
Mössbauer spectra of polycrystalline [Fe(OEP)(2-MeHIm)]ClO4 ⋅ CHCl3 (OEP=octaethylporphyrinate; 2-meHIm=2-methylimidazole) were recorded over a range of temperatures (1.54 to 195 K) and applied magnetic fields (0–6 T). Magnetic susceptibility was measured at both 0.2 and 1 T in the temperature range 6–300 K. Mössbauer parameters δ=0.40 mm/s and ΔE=1.39 mm/s at 4.2 K, and the high temperature susceptibility Neff=6 at 300 K indicated high spin iron, but no simple spin Hamiltonian would reproduce the rapid decrease of Neff below 50 K. We interpret the data by assuming a zero field splitting D=12 cm−1 and invoking an antiferromagnetic exchange interaction J=−0.8 cm−1 between the five-coordinate iron atoms of closely spaced face-to-face heme pairs in the crystal. Slow spin relaxation at low temperature permits observation of level crossing and confirms the interpretation. It also permits the detection, through low field Mössbauer measurements, of a small rhombic distortion with E=0.024 cm−1. Mössbauer spectra of the same compound but as a dibromomethane solvate, [Fe(OEP)(2-MeHIm)]ClO4 ⋅ CH2Br2, yield generally similar parameter values, but suggest a slightly larger coupling, J=−0.85 cm−1, consistent with the fact that the paired iron atoms are known to be closer in this material.
Accounting for both the long- and short-wavelength density gratings by considering the polarizations of all four electromagnetic waves parallel to each other and a finite tilt angle between the signal and pump waves, the steady-state phase conjugation in the low reflectivity regime by nearly degenerate four-wave mixing in a homogeneous plasma is investigated. The response of the density gratings caused by the beating of the signal and pump waves is considered from the theory of stimulated Brillouin scattering in a two-component plasma. The expression for the power reflectivity of the conjugate wave for the arbitrary difference frequency between the signal and pump waves and that for resonant four-wave mixing are obtained. Numerical results have been discussed in light of the experiment reported in the literature. It is noted that the measured values of the difference frequency corresponding to either density grating resonance, and the corresponding maximum reflectivity of the conjugate wave, provide a diagnostic tool for the electron temperature and the electron–ion temperature ratio, respectively.
Internet of Things (IoT) offers the capability to connect and integrate both digital and physical objects to the internet and to enable machine-to-machine and machine-to-human communication or interactions services. The real-time adoptions and deployments of such systems for different applications such as smart cities, smart grids, smart homes, or smart environments require guaranteed security and privacy-enabled IoT services. This is due to fact that devices in the IoT generate, process, and exchange huge amounts of safety-critical data as well as privacy-sensitive information. In order to ensure secure and safe operation and to avoid cyber-attacks on such systems, it is crucial to incorporate security and privacy measures to countermeasure the different possible attacks. This chapter presents different security and privacy requirements and a taxonomy of security threats in the context of the IoT. In addition, the authors survey the most relevant defense strategies available in the literature related to IoT security with their merits and demerits.
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