mLysPTMpred: Multiple Lysine PTM Site Prediction Using Combination of SVM with Resolving Data Imbalance Issue

Post-translational modification (PTM) increases the functional diversity of proteins by introducing new functional groups to the side chain of amino acid of a protein. Among all amino acid residues, the side chain of lysine (K) can undergo many types of PTM, called K-PTM, such as “acetylation”, “crotonylation”, “methylation” and “succinylation” and also responsible for occurring multiple PTM in the same lysine of a protein which leads to the requirement of multi-label PTM site identification. However, most of the existing computational methods have been established to predict various single-label PTM sites and a very few have been developed to solve multi-label issue which needs further improvement. Here, we have developed a computational tool termed mLysPTMpred to predict multi-label lysine PTM sites by 1) incorporating the sequence-coupled information into the general pseudo amino acid composition, 2) balancing the effect of skewed training dataset by Different Error Cost method, and 3) constructing a multi-label predictor using a combination of support vector machine (SVM). This predictor achieved 83.73% accuracy in predicting the multi-label PTM site of K-PTM types. Moreover, all the experimental results along with accuracy outperformed than the existing predictor iPTM-mLys. A user-friendly web server of mLysPTMpred is available at http://research.ru.ac.bd/mLysPTMpred/.

Feature Selection for Intrusion Detection Using Random Forest

An intrusion detection system collects and analyzes information from different areas within a computer or a network to identify possible security threats that include threats from both outside as well as inside of the organization. It deals with large amount of data, which contains various ir-relevant and redundant features and results in increased processing time and low detection rate. Therefore, feature selection should be treated as an indispensable pre-processing step to improve the overall system performance significantly while mining on huge datasets. In this context, in this paper, we focus on a two-step approach of feature selection based on Random Forest. The first step selects the features with higher variable importance score and guides the initialization of search process for the second step whose outputs the final feature subset for classification and in-terpretation. The effectiveness of this algorithm is demonstrated on KDD’99 intrusion detection datasets, which are based on DARPA 98 dataset, provides labeled data for researchers working in the field of intrusion detection. The important deficiency in the KDD’99 data set is the huge number of redundant records as observed earlier. Therefore, we have derived a data set RRE-KDD by eliminating redundant record from KDD’99 train and test dataset, so the classifiers and feature selection method will not be biased towards more frequent records. This RRE-KDD consists of both KDD99Train+ and KDD99Test+ dataset for training and testing purposes, respectively. The experimental results show that the Random Forest based proposed approach can select most im-portant and relevant features useful for classification, which, in turn, reduces not only the number of input features and time but also increases the classification accuracy.

Support Vector Machine and Random Forest Modeling for Intrusion Detection System (IDS)

The success of any Intrusion Detection System (IDS) is a complicated problem due to its nonlinearity and the quantitative or qualitative network traffic data stream with many features. To get rid of this problem, several types of intrusion detection methods have been proposed and shown different levels of accuracy. This is why the choice of the effective and robust method for IDS is very important topic in information security. In this work, we have built two models for the classification purpose. One is based on Support Vector Machines (SVM) and the other is Random Forests (RF). Experimental results show that either classifier is effective. SVM is slightly more accurate, but more expensive in terms of time. RF produces similar accuracy in a much faster manner if given modeling parameters. These classifiers can contribute to an IDS system as one source of analysis and increase its accuracy. In this paper, KDD’99 Dataset is used and find out which one is the best intrusion detector for this dataset. Statistical analysis on KDD’99 dataset found important issues which highly affect the performance of evaluated systems and results in a very poor evaluation of anomaly detection approaches. The most important deficiency in the KDD’99 dataset is the huge number of redundant records. To solve these issues, we have developed a new dataset, KDD99Train+ and KDD99Test+, which does not include any redundant records in the train set as well as in the test set, so the classifiers will not be biased towards more frequent records. The numbers of records in the train and test sets are now reasonable, which make it affordable to run the experiments on the complete set without the need to randomly select a small portion. The findings of this paper will be very useful to use SVM and RF in a more meaningful way in order to maximize the performance rate and minimize the false negative rate.