Detecting and Classifying Darknet Traffic Using Deep Network Chains

The anonymity of the darknet makes it attractive to secure communication lines from censorship. The analysis, monitoring, and categorization of Internet network traffic are essential for detecting darknet traffic that can generate a comprehensive characterization of dangerous users and assist in tracing malicious activities and reducing cybercrime. Furthermore, classifying darknet traffic is essential for real-time applications such as the timely monitoring of malware before attacks occur. This paper presents a two-stage deep network chain for detecting and classifying darknet traffic. In the first stage, anonymized darknet traffic, including VPN and Tor traffic related to hidden services provided by darknets, is detected. In the second stage, traffic related to VPNs and Tor services is classified based on their respective applications. The methodology of this paper was verified on a benchmark dataset containing VPN and Tor traffic. It achieved an accuracy of 96.8% and 94.4% in the detection and classification stages, respectively. Optimization and parameter tuning were performed in both stages to achieve more accurate results, enabling practitioners to combat alleged malicious activities and further detect such activities after outbreaks. In the classification stage, it was observed that the misclassifications were due to the audio and video streaming commonly used in shared real-time protocols. However, in cases where it is desired to distinguish between such activities accurately, the presented deep chain classifier can accommodate additional classifiers. Furthermore, additional classifiers could be added to the chain to categorize specific activities of interest further.     

Hybrid digital-optical correlation employing a chirp-encoded simulated-annealing-based rotation-invariant and distortion-tolerant filter

The simulated annealing (SA) algorithm based on entropy optimization is a technique of synthesizing distortion-invariant matched filters capable of discriminating very similar images. The synthesis of rotation-invariant filters using modified SA-based filter equations and their tolerance to distortions are studied. The filters are trained with true class images rotated in-plane at 3 degrees intervals between 0 degrees and 360 degrees . A total of seven filters are required over the whole range for both CCD or thermal images. Optical correlation in a hybrid digital-optical correlator results in an unwanted zero-order dc along with two first-order (+/-1) correlation peaks. A chirp function multiplied with the filter separates out the three peaks to three different planes, and only one peak in focus is captured in a camera. The performance of the modified SA-based filter has been studied in comparison to the conventional SA filter as well as with other filters.     

High molecular weight waxes from Short Path Distillates of vacuum residue

Short Path Distillates of vacuum residue (boiling above 545 °C) is taken as feedstock for this study. Wax from this fraction is separated by solvent extraction method using methyl iso-butyl ketone (MIBK) as solvent. Both wax and the feedstock are characterised with the help of ASTM and IP procedures. Separated wax is fractionated at different temperatures, say 0–30 °C using MIBK as solvent. High temperature gas chromatography (HTGC) technique is used to study the distribution of alkane carbon number in all the fractions. It is observed that the wax contains very high molecular weight hydrocarbons as high as C₆₇H₁₃₆. HTGC technique as well as the Differential Scanning Calorimetry (DSC) indicates that all the fractions of the wax contain two types of hydrocarbons, one having high molecular weight alkanes (> 600) and another having low mol. wt. alkanes (~ 400). Thermal analysis by DSC technique further indicates that the wax is microcrystalline in nature having a low degree of crystallinity, 17%, as evidenced by XRD studies. Both high and low molecular weight waxes can also be separated based on their solubility characteristics.     

A systematic literature review on wearable health data publishing under differential privacy

International Journal of Information Security - Wearable devices generate different types of physiological data about the individuals. These data can provide valuable insights for medical...     

A circuit simulation compatible surface acoustic wave interdigital transducer macro-model

In the present work, a surface acoustic wave (SAW) interdigital transducer (IDT) has been modeled as a resistance-inductance-capacitance (RLC) network. The model includes the effects of metal shorting and energy storage at metal discontinuities, as well as an arbitrary polarity sequence of fingers. A C++ program generates the equivalent electrical circuit, which is directly simulated by Simulation Program with Integrated Circuit Emphasis (SPICE), a popular electrical circuit simulator. Simulations compare well with reported experimental results, validating the proposed model.     

Identification of ECG beats from cross-spectrum information aided learning vector quantization

This work describes the development of a computerized medical diagnostic tool for heart beat categorization. The main objective is to achieve an accurate, timely detection of cardiac arrhythmia for providing appropriate medical attention to a patient. The proposed scheme employs a feature extractor coupled with an Artificial Neural Network (ANN) classifier. The feature extractor is based on cross-correlation approach, utilizing the cross-spectral density information in frequency domain. The ANN classifier uses a Learning Vector Quantization (LVQ) scheme which classifies the ECG beats into three categories: normal beats, Premature Ventricular Contraction (PVC) beats and other beats. To demonstrate the generalization capability of the scheme, this classifier is developed utilizing a small training dataset and then tested with a large testing dataset. Our proposed scheme was employed for 40 benchmark ECG files of the MIT/BIH database. The system could produce classification accuracy as high as 95.24% and could outperform several competing algorithms.     

A new self-tuned PID-type fuzzy controller as a combination of two-term controllers

The present paper is a venture into the domain of proportional-integral-derivative (PID) -type adaptive fuzzy logic controllers (FLC's) and proposes a new algorithm which is realized by a self-tuned PI-type FLC (in velocity form) in parallel with a self-tuned PD-type FLC (in position form). Each of these PI/PD controllers implements a supervisory static FLC for adaptive online modification of the output scaling factor (SF) of a static PI/PD FLC. The proposed scheme is developed with a view to having a PID-type FLC with an architecture, simple enough for practical implementation, which at the same time has substantially satisfactory performance for a wide class of processes. Simulation studies on a range of processes reveal that the proposed controller has better performance compared to many of its existing counterparts.     

Faster Algorithms for Stable Allocation Problems

We consider a high-multiplicity generalization of the classical stable matching problem known as the stable allocation problem, introduced by Baïou and Balinski in 2002. By leveraging new structural properties and sophisticated data structures, we show how to solve this problem in O(mlog?n) time on a bipartite instance with n vertices and m edges, improving the best known running time of O(mn). Building on this algorithm, we provide an algorithm for the non-bipartite stable allocation problem running in O(mlog?n) time with high probability. Finally, we give a polynomial-time algorithm for solving the “optimal” variant of the bipartite stable allocation problem, as well as a 2-approximation algorithm for the NP-hard “optimal” variant of the non-bipartite stable allocation problem.     

Asymmetric hydrogenation of methyl pyruvate using platinum carbonyl cluster supported on an anion exchanger as the catalyst

Anionic platinum carbonyl clusters supported on quaternary amine functionalized cross-linked polystyrene, i.e., anion exchangers, are effective precatalysts for the hydrogenation of methyl pyruvate to methyl lactate. Kinetic data has been obtained for anion exchangers with different quaternary groups. Highest observed rate constant and enantioselectivity are obtained with cinchonine funtionalized resin. The kinetic data also indicates saturation kinetics. The decarbonylated used catalyst could be recarbonylated to give a material spectroscopically (infrared) equivalent to freshly anchored cluster.