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

Estimation of Joint Angle Based on Surface Electromyogram Signals Recorded at Different Load Levels

To control upper-limb exoskeletons and prostheses, surface electromyogram (sEMG) is widely used for estimation of joint angles. However, the variations in the load carried by the user can substantially change the recorded sEMG and consequently degrade the accuracy of joint angle estimation. In this paper, we aim to deal with this problem by training classification models using a pool of sEMG data recorded from all different loads. The classification models are trained as either subject-specific or subject-independent, and their results are compared with the performance of classification models that have information about the carried load. To evaluate the proposed system, the sEMG signals are recorded during elbow flexion and extension from three participants at four different loads (i.e. 1, 2, 4 and 6 Kg) and six different angles (i.e. 0, 30, 60, 90, 120, 150 degrees). The results show while the loads were assumed unknown and the applied training data was relatively small, the proposed joint angle estimation model performed significantly above the chance level in both the subject-specific and subject-independent models. However, transferring from known to unknown load in the subject-specific classifiers leads to 20% to 32% loss in the average accuracy

Weighted multi-task learning in classification domain for improving brain-computer interface

One of the major limitations of brain computer interface (BCI) is its long calibration time. Due to between sessions/subjects nonstationarity, typically a big amount of training data needs to be collected at the beginning of each session in order to tune the parameters of the system for the target user. In this paper, a number of novel weighted multi-task transfer learning algorithms are proposed in the classification domain to reduce the calibration time without sacrificing the classification accuracy of the BCI system. The proposed algorithms use data from other subjects and combine them to estimate the classifier parameters for the target subject. This combination is done based on how similar the data from each subject is to the few trials available from the target subject. The proposed algorithms are evaluated using dataset 2a from BCI competition IV. According to the results, the proposed algorithms lead to reduce the calibration time by 75% and enhance the average classification accuracy at the same time

Biological indicators, genetic polymorphism and expression in Aspergillus flavus under copper mediated stress

AbstractFungi are considered model organisms for studying stress response and metal adaptation for both biotechnological and environmental purposes. In a previous study, copper was added in concentrations 1 and 10mM to Aspergillus flavus to induce laccase production for bioremediation, but using high concentrations of copper resulted in laccase inhibition despite the increase in bioremediation. In this study, the same copper sulfate was added and some oxidative biomarkers and antioxidative defense enzymes were assessed for stressed cultures of both copper and gamma radiation which was used as a positive stress inducer. The increase in copper concentrations resulted in an increase in superoxide dismutase enzyme activity, lipid peroxidation and protein carbonylation. On the other hand, catalase was inhibited by the addition of both copper concentrations, but exposure to gamma radiation resulted in an increased copper production. Glutathione peroxidase showed variation under stress, while both reduced glutathione and mycelial growth decreased in copper amended cultures. There was an increase in total endogenous carbohydrates. The main location of copper at the end of the incubation period seemed to reside in the cytosolic fraction of the fungus as detected by atomic absorption spectrometry. Genetic polymorphism was evident in the presence of copper as detected by RAPD-PCR. The expression of both laccase and superoxide dismutase suggest that each has a specific role in bioremediation, depending on the added copper concentration

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