Machine learning based botnet identification traffic

The continued growth of the Internet has resulted in the increasing sophistication of toolkit and methods to conduct computer attacks and intrusions that are easy to use and publicly available to download, such as Zeus botnet toolkit. Botnets are responsible for many cyber-attacks, such as spam, distributed denial-of-service (DDoS), identity theft, and phishing. Most of existence botnet toolkits release updates for new features, development and support. This presents challenges in the detection and prevention of bots. Current botnet detection approaches mostly ineffective as botnets change their Command and Control (C&C) server structures, centralized (e.g., IRC, HTTP), distributed (e.g., P2P), and encryption deterrent. In this paper, based on real world data sets we present our preliminary research on predicting the new bots before they launch their attack. We propose a rich set of features of network traffic using Classification of Network Information Flow Analysis (CONIFA) framework to capture regularities in C&C communication channels and malicious traffic. We present a case study of applying the approach to a popular botnet toolkit, Zeus. The experimental evaluation suggest that it is possible to detect effectively botnets during the botnet C&C communication generated from new updated Zeus botnet toolkit by building the classifier using machine learning from an earlier version and before they launch their attacks using traffic behaviors. Also, show that there is similarity in C&C structures various Botnet toolkit versions and that the network characteristics of botnet C&C traffic is different from legitimate network traffic. Such methods could reduce many different resources needed to identify C&C communication channels and malicious traffic

Weighted transfer learning for improving motor imagery-based brain-computer interface

One of the major limitations of motor imagery (MI)-based brain-computer interface (BCI) is its long calibration time. Due to between sessions/subjects variations in the properties of brain signals, typically a large amount of training data needs to be collected at the beginning of each session to calibrate the parameters of the BCI system for the target user. In this paper, we propose a novel transfer learning approach on the classification domain to reduce the calibration time without sacrificing the classification accuracy of MI-BCI. Thus, when only few subject-specific trials are available for training, the estimation of the classification parameters is improved by incorporating previously recorded data from other users. For this purpose, a regularization parameter is added to the objective function of the classifier to make the classification parameters as close as possible to the classification parameters of the previous users who have feature spaces similar to that of the target subject. In this study, a new similarity measure based on the kullback leibler divergence (KL) is used to measure similarity between two feature spaces obtained using subject-specific common spatial patterns (CSP). The proposed transfer learning approach is applied on the logistic regression classifier and evaluated using three datasets. The results showed that compared to the subject-specific classifier, the proposed weighted transfer learning classifier improved the classification results particularly when few subject-specific trials were available for training (p<0.05). Importantly, this improvement was more pronounced for users with medium and poor accuracy. Moreover, the statistical results showed that the proposed weighted transfer learning classifier performed significantly better than the considered comparable baseline algorithms

Robust common spatial pattern estimation using dynamic time warping to improve BCI systems

Common spatial patterns (CSP) is one of the most popular feature extraction algorithms for brain-computer interfaces (BCI). However, CSP is known to be very sensitive to artifacts and prone to overfitting. This paper proposes a novel dynamic time warping (DTW)-based approach to improve CSP covariance matrix estimation and hence improve feature extraction. Dynamic time warping is widely used for finding an optimal alignment between two time-dependent signals under predefined conditions. The proposed approach reduces within class temporal variations and non-stationarity by aligning the training trials to the average of the trials from the same class. The proposed DTW-based CSP approach is applied to the support vector machines (SVM) classifier and evaluated using one of the publicly available motor imagery datasets. The results showed that the proposed approach, when compared to the classical CSP, improved the classification accuracy from 78% to 83% on average. Importantly, for some subjects, the improvement was around 10%

Zooplankton stresnih područja uzduž obale Damietta (Egipat) u Sredozemnom moru

The spatial pattern of zooplankton communities at Damietta coast, southeastern Mediterranean was studied to assess the impact of human activities on the abundance and community structure. Twenty-five stations from five different stressed sites were sampled in June-July 2014. Thirty-four zooplankton taxa were recorded, in addition to the larvae of copepods and meroplankton. Copepoda was the most abundant group among which, Oithona nana, Euterpina acutifrons, and Parvocalanus cirrostratus were the most frequent. The calanoid copepod Pseudodiaptomus trihamatus is a new record for the Mediterranean Sea that may have been introduced via ballast water. Multivariate/Univariate analyses demonstrated that 1) the environmental variables and zooplankton communities represented significant differences among five sites; 2) the spatial variations of community structure were undoubtedly due to land-based effluents; and 3) among all environmental variables, salinity and phytoplankton biomass had the major determining effects on the spatial patterns of zooplankton categories. The results indicates that not only the discharged water makes the Damietta coast at risk, but also the ballast water is not less dangerous. Hence, we emphasize the need for activation of the ballast water management to reduce the risk of future species invasions.Istraživana je prostorna struktura zooplanktonskih zajednica na obali Damietta (Egipat, jugoistočni Mediteran) kako bi se procijenio utjecaj ljudskih aktivnosti na obilje i strukturu zajednice. U lipnju i srpnju 2014. uzorkovano je na dvadeset pet postaja s pet različitih mjesta izloženih zagađenju. Uz larve veslonožaca i meroplanktona zabilježene su 34 zooplanktonske vrste.Veslonošci se bili najbrojnija skupina među kojima su najčešći Oithona nana, Euterpina acutifrons i Parvocalanus cirrostratus. Kalanoidni veslonožac Pseudodiaptomus trihamatus je novi nalaz za Sredozemno more, a vjerojatno je da je možda unesen putem balastnih voda. Multivarijatne / jednosmjerne analize pokazale su da 1) varijable okoliša i zooplanktonske zajednice predstavljaju značajne razlike između pet mjesta; 2) prostorne varijacije strukture zajednice nedvojbeno su posljedica tehnoloških otpadnih voda (pročišćenih i nepročišćenih) sa kopna; i 3) između svih varijablia okoliša, saliniteta i biomase fitoplanktona imali su glavne utjecaje na prostorne obrasce kategorija zooplanktona. Rezultati pokazuju da je samo ne ispuštena voda rizična za obalu Damiette, već i balastna voda, koja nije nimalo manje opasna. Stoga se naglašava potreba za aktivacijom upravljanja balastnim vodama kako bi se smanjio rizik unosa invazivnih vrsta

A robust uniform B-spline collocation method for solving the generalized PHI-four equation

In this paper, we develop a numerical solution based on cubic B-spline collocation method. By applying Von-Neumann stability analysis, the proposed technique is shown to be unconditionally stable. The accuracy of the presented method is demonstrated by a test problem. The numerical results are found to be in good agreement with the exact solution

Hepatoprotective effect of basil (Ocimum basilicum L.) on CCl4-induced liver fibrosis in rats

The hepatoprotective effect of basil (Ocimum basilicum) extract against liver fibrosis-induced by carbon tetrachloride (CCl4) was studied in rats. Rats were allocated into five groups: Group I (control group); Group II [CCl4 group; rats were injected subcutaneously with CCl4 (1 ml/kg b.w.) twice weekly for 4 weeks (phenobarbital, 350 mg/L, was added to the drinking water throughout the experiment)]; Group III received daily oral doses of basil extract of 200 mg/kg b.w. along with CCl4 and phenobarbital for 6 weeks; Groups IV and V rats were treated with phenobarbital and CCl4 for 6 weeks then treated daily with oral dose of 200 mg/kg b.w basil extract, or by 300 mg/kg b.w dimethyl diphenyl bicarboxylate (DDB), respectively for 6 weeks. Basil-treatment significantly reduced the liver content of hydroxyproline and significantly increased the activity of hyaluronidase (HAase). The hepatic activity of superoxide dismutase (SOD) was stimulated while the lipid peroxidation was significantly reduced by the effect of basil extract. Treatment with CCl4 significantly increased the activities of transaminases [aspartate aminotransferase (AST), alanine aminotransferase (ALT)], and alkaline phosphatase (ALP). These activities were significantly decreased by basil extract. The higher levels of serum urea and creatinine in CCl4 group were significantly guarded by the protection of basil.Key words: Carbon tetrachloride, liver fibrosis, antioxidant, Ocimum basilicum, dimethyl diphenyl bicarboxylate

Linking continuum mechanics and 3D discrete dislocation simulations

A technique is developed for linking the methods of discrete dislocation dynamics simulation and finite element to treat elasto-plasticity problems. The overall formulation views the plastically deforming crystal as an elastic crystal with continuously changing dislocation microstructure which is tracked by the numerical dynamics simulation. The FEM code needed in this regard is based on linear elasticity only. This formulation presented here is focused on a continuous updating of the outer shape of the crystal, for possible regeneration of the FEM mesh, and adjustment of the surface geometry, in particular the surface normal. The method is expected to be potentially applicable to the nano- indentation experiments, where the zone around the indenter-crystal contact undergoes significant permanent deformation, the rigorous determination of which is very important to the calculation of the indentation print area and in turn, the surface hardness. Furthermore, the technique is expected to account for the plastic history of the surface displacement under the indenter. Other potential applications are mentioned in the text

A probabilistic multi-objective approach for FACTS devices allocation with different levels of wind penetration under uncertainties and load correlation

This study presents a probabilistic multi-objective optimization approach to obtain the optimal locations and sizes of static var compensator (SVC) and thyristor-controlled series capacitor (TCSC) in a power transmission network with large level of wind generation. In this study, the uncertainties of the wind power generation and correlated load demand are considered. The uncertainties are modeled in this work using the points estimation method (PEM). The optimization problem is solved using the Multi-objective particle swarm optimization (MOPSO) algorithm to find the best position and rating of the flexible AC transmission system (FACTS) devices. The objective of the problem is to maximize the system loadability while minimizing the power losses and FACTS devices installation cost. Additionally, a technique based on fuzzy decision-making approach is employed to extract one of the Pareto optimal solutions as the best compromise one. The proposed approach is applied on the modified IEEE 30-bus system. The numerical results evince the effectiveness of the proposed approach and shows the economic benefits that can be achieved when considering the FACTS controller