Abstract: Phishing is one among the luring strategies utilized by phishing artist in the aim of abusing the personal details of unsuspected clients. Phishing website is a counterfeit website with similar appearance, but changed destination. The unsuspected client post their information thinking that these websites originate from trusted financial institutions. New antiphishing techniques rise continuously, yet phishers come with new strategy by breaking all the antiphishing mechanisms. Hence there is a need for productive mechanism for the prediction of phishing website. This paper described comparison in classification of phishing websites using different Machinelearning algorithms. Random Forest (RF), C4.5, REP Tree, Decision Stump, Hoeffding Tree, Rotation Forest and MLP were used to determine which method provides the best results in phishing websites classification. All instances are categorized as 1 for “Legitimate”, 0 for “Suspicious” and 1 for “Phishy”. Results show that RF with REP Tree show the best performance on this dataset for classification of phishing websites.

E-mail still proves to be very popular and an efficient communication tool. Due to its misuse, however, managing e-mails problem for organizations and individuals. Spam, known as unwanted message, is an example of misuse. Specifically, spam is defined as the arrival of unwelcomed bulk email not being requested for by recipients. This paper compares different Machine Learning Techniques classification of spam e-mails. Random Forest (RF), C4.5 and Artificial Neural Network (ANN) were tested to determine which method provides the best results in spam e-mail classification. Our results show that RF is the best technique applied on dataset Labs, indicating that ensemble methods may have an edge in spam detection effective susceptible to is spam, also is defined messages not istaken with or religious he most email by a . Furthermore, by spam. around (which makes (Grant, 2003; Every e-mail user in America received an average of 2200 pieces of spam e-mails in 2002. In 2007 it reached 3600 pieces of spam e-mails due to increase rate of 2% per month conducted a survey revealing that a Chinese spam e-mails weekly. Due to spam e enterprises lose up to 9 billion yearly reveal that spam e-mails take about 60% of the incomin in a corporate network. With inappropriate or no countermeasures, the situation will worsen and, in the end, spam e-mails may destruct the usage of e countries are slowly starting to use anti (Gaikwad & Halkarnikar, 2014). The main argument supporting spam increase is the fact that spammers do not have any costs for it: “Because email technology allows spammers to shift the costs almost entirely to third parties, there is no incentive for the spammers to reduce the volume” (Hann, Hui, Lai, Lee, & Png, 2006) issue for spam is the annoying content they carry significant amount of spam contains some offensive materials (Maria & Ng, 2009). In China, some specialists suggest spam email measure as early as possible. However, because of 1210 Sarajevo,

In this paper, we developed a model for classification of EEG signals. The aim of the study is to determine whether this model can be used for epileptic seizure prediction if “pre-ictal” stages were successfully detected. We analyzed long-term Freiburg EEG data. Each of 21 patients contains datasets called “ictal” (seizure) and “inter-ictal” (seizure-free). We extracted 4096-samples (or 16 seconds) long segments from both datasets of each patient. These segments were decomposed into time-frequency representations using Discrete Wavelet Transform (DWT). The statistical features from the DWT sub-bands of EEG segments were calculated and fed as inputs to Multilayer Perceptron (MLP) and Radial Basis Function (RBF) network classifiers using 10-fold cross validation. We also applied multiscale PCA (MSPCA) de-noising method to determine if it can further enhance the classifiers’ performance. MLP-based approach outperformed RBF classifier with or without MSPCA, which significantly improved the classification accuracy of both classifiers. The proposed MLP-approach with MSPCAachieved a classification accuracy of 95.09%. We showed that a high classification accuracy of EEG signals can be accomplished in cases when additional “pre-ictal” class is introduced. Therefore, the proposed approach may become an efficient tool to predict epileptic seizures from EEG recordings. Keywords: Electroencephalogram (EEG); Epileptic seizure; Discrete Wavelet Transform (DWT); Multilayer Perceptron (MLP); Radial Basis Function (RBF) network; Multiscale PCA (MSPCA); Machine learning.

This paper presents the practical implementation of the motor imagery BCI system using MATLAB GUI. EEG signals were recorded using Mindwave Mobile Headset from one subject for two motor imagery tasks: right hand and left hand. The offline analysis showed decent performance of the combination between