Vulnerabilities detection using attack recognition technique in multi-factor authentication

Authentication is one of the essentials components of information security. It has become one of the most basic security requirements for network communication. Today, there is a necessity for a strong level of authentication to guarantee a significant level of security is being conveyed to the application. As such, it expedites challenging issues on security and efficiency. Security issues such as privacy and data integrity emerge because of the absence of control and authority. In addition, the bigger issue for multi-factor authentication is on the high execution time that leads to overall performance degradation. Most of existing studies related to multi-factor authentication schemes does not detect weaknesses based on user behavior. Most recent research does not look at the efficiency of the system by focusing only on improving the security aspect of authentication. Hence, this research proposes a new multi-factor authentication scheme that can withstand attacks, based on user behavior and maintaining optimum efficiency. Experiments have been conducted to evaluate this scheme. The results of the experiment show that the processing time of the proposed scheme is lower than the processing time of other schemes. This is particularly important after additional security features have been added to the scheme

Study of Tree Base Data Mining Algorithms for Network Intrusion Detection

Internet growth has increased rapidly due to which number of network attacks have been increased. This emphasis importance of network intrusion detection systems (IDS) for securing the network. It is the process of monitoring and analyzing network traffic for detecting security violations many researcher suggested data mining technique such as classification, clustering ,pattern matching and rule induction for developing an effective intrusion detection system. In order to detect the intrusion, the network traffic can be classified into normal and anomalous. In this paper we have evaluated tree base classification algorithms namely J48, Hoeffding tree, Random Forest, Random Tree, REPTree. The comparison of these tree based classification algorithms is presented in this paper based upon their performance metrics using 10 fold cross validation and KDD- CUP test dataset. This study shows that random forest and J48 are the best suitable tree base algorithms

A NOVEL EVALUATION APPROACH TO FINDING LIGHTWEIGHT MACHINE LEARNING ALGORITHMS FOR INTRUSION DETECTION IN COMPUTER NETWORK

Building practical and efficient intrusion detection systems in computer network is important in industrial areas today and machine learning technique provides a set of effective algorithms to detect network intrusion. To find out appropriate algorithms for building such kinds of systems, it is necessary to evaluate various types of machine learning algorithms based on specific criteria. In this paper, we propose a novel evaluation formula which incorporates 6 indexes into our comprehensive measurement, including precision, recall, root mean square error, training time, sample complexity and practicability, in order to find algorithms which have high detection rate, low training time, need less training samples and are easy to use like constructing, understanding and analyzing models. Detailed evaluation process is designed to get all necessary assessment indicators and 6 kinds of machine learning algorithms are evaluated. Experimental results illustrate that Logistic Regression shows the best overall performance

Graduates employment classification using data mining approach

Data Mining is a platform to extract hidden knowledge in a collection of data.This study investigates the suitable classification model to classify graduates employment for one of the MARA Professional College (KPM) in Malaysia.The aim is to classify the graduates into either as employed, unemployed or further study.Five data mining algorithms offered in WEKA were used; Naïve Bayes, Logistic regression, Multilayer perceptron, k-nearest neighbor and Decision tree J48.Based on the obtained result, it is learned that the Logistic regression produces the highest classification accuracy which is at 92.5%. Such result was obtained while using 80% data for training and 20% for testing.The produced classification model will benefit the management of the college as it provides insight to the quality of graduates that they produce and how their curriculum can be improved to cater the needs from the industry

Intrusion detection by machine learning = Behatolás detektálás gépi tanulás által

Since the early days of information technology, there have been many stakeholders who used the technological capabilities for their own benefit, be it legal operations, or illegal access to computational assets and sensitive information. Every year, businesses invest large amounts of effort into upgrading their IT infrastructure, yet, even today, they are unprepared to protect their most valuable assets: data and knowledge. This lack of protection was the main reason for the creation of this dissertation. During this study, intrusion detection, a field of information security, is evaluated through the use of several machine learning models performing signature and hybrid detection. This is a challenging field, mainly due to the high velocity and imbalanced nature of network traffic. To construct machine learning models capable of intrusion detection, the applied methodologies were the CRISP-DM process model designed to help data scientists with the planning, creation and integration of machine learning models into a business information infrastructure, and design science research interested in answering research questions with information technology artefacts. The two methodologies have a lot in common, which is further elaborated in the study. The goals of this dissertation were two-fold: first, to create an intrusion detector that could provide a high level of intrusion detection performance measured using accuracy and recall and second, to identify potential techniques that can increase intrusion detection performance. Out of the designed models, a hybrid autoencoder + stacking neural network model managed to achieve detection performance comparable to the best models that appeared in the related literature, with good detections on minority classes. To achieve this result, the techniques identified were synthetic sampling, advanced hyperparameter optimization, model ensembles and autoencoder networks. In addition, the dissertation set up a soft hierarchy among the different detection techniques in terms of performance and provides a brief outlook on potential future practical applications of network intrusion detection models as well

A Study on Sequential Classifiers Combination using Supervised Machine Learning Approach for Intrusion Detection System

電気通信大学201

Multikonferenz Wirtschaftsinformatik (MKWI) 2016: Technische Universität Ilmenau, 09. - 11. März 2016; Band I

Übersicht der Teilkonferenzen Band I: • 11. Konferenz Mobilität und Digitalisierung (MMS 2016) • Automated Process und Service Management • Business Intelligence, Analytics und Big Data • Computational Mobility, Transportation and Logistics • CSCW & Social Computing • Cyber-Physische Systeme und digitale Wertschöpfungsnetzwerke • Digitalisierung und Privacy • e-Commerce und e-Business • E-Government – Informations- und Kommunikationstechnologien im öffentlichen Sektor • E-Learning und Lern-Service-Engineering – Entwicklung, Einsatz und Evaluation technikgestützter Lehr-/Lernprozess