Application of feast (Feature Selection Toolbox) in ids (Intrusion detection Systems)

Security in computer networks has become a critical point for many organizations, but keeping data integrity demands time and large economic investments, in consequence there has been several solution approaches between hardware and software but sometimes these has become inefficient for attacks detection. This paper presents research results obtained implementing algorithms from FEAST, a Matlab Toolbox with the purpose of selecting the method with better precision results for different attacks detection using the least number of features. The Data Set NSL-KDD was taken as reference. The Relief method obtained the best precision levels for attack detection: 86.20%(NORMAL), 85.71% (DOS), 88.42% (PROBE), 93.11%(U2R), 90.07(R2L), which makes it a promising technique for features selection in data network intrusions

A novel ensemble modeling for intrusion detection system

Vast increase in data through internet services has made computer systems more vulnerable and difficult to protect from malicious attacks. Intrusion detection systems (IDSs) must be more potent in monitoring intrusions. Therefore an effectual Intrusion Detection system architecture is built which employs a facile classification model and generates low false alarm rates and high accuracy. Noticeably, IDS endure enormous amounts of data traffic that contain redundant and irrelevant features, which affect the performance of the IDS negatively. Despite good feature selection approaches leads to a reduction of unrelated and redundant features and attain better classification accuracy in IDS. This paper proposes a novel ensemble model for IDS based on two algorithms Fuzzy Ensemble Feature selection (FEFS) and Fusion of Multiple Classifier (FMC). FEFS is a unification of five feature scores. These scores are obtained by using feature-class distance functions. Aggregation is done using fuzzy union operation. On the other hand, the FMC is the fusion of three classifiers. It works based on Ensemble decisive function. Experiments were made on KDD cup 99 data set have shown that our proposed system works superior to well-known methods such as Support Vector Machines (SVMs), K-Nearest Neighbor (KNN) and Artificial Neural Networks (ANNs). Our examinations ensured clearly the prominence of using ensemble methodology for modeling IDSs. And hence our system is robust and efficient

The trust management framework for peer-to-peer networks

Popularity of peer-to-peer (P2P) networks exposed a number of security vulnerabilities. Among those is a problem of finding reliable communication partners. In this thesis, we present an integrated trust framework for peer-to-peer networks that quantifies the trustworthiness of a peer via reputation-based trust mechanism and anomaly detection techniques. As opposed to other known techniques in P2P networks, our trust management schema is fully decentralized and does not rely on the co-operation of peers. Furthermore, the reputation computation is based on traffic coming from other peers. We also describe an anomaly detection procedure that analyses peer activity on the network and flags potentially malicious behavior by detecting deviation from peer profile. We present integration of our anomaly detection to trust management scheme and study the performance of reputation-based approach using implementation and performance of trust framework through simulation

Importance of Machine Learning Techniques to Improve the Open Source Intrusion Detection Systems

Nowadays, it became difficult to ensure data security because of the rapid development of information technology according to the Vs of Big Data. To secure a network against malicious activities and to ensure data protection, an intrusion detection system played a very important role. The main objective was to obtain a high-performance solution capable of detecting different types of attacks around the system. The main aim of this paper is to study the lacks of traditional and open source Intrusion Detection Systems and the Machine Learning techniques commonly used to overcome these lacks. A comparison of some existing works by Intrusion Detection System type, detection method, algorithm and accuracy was provided

Automating Cyberdeception Evaluation with Deep Learning

A machine learning-based methodology is proposed and implemented for conducting evaluations of cyberdeceptive defenses with minimal human involvement. This avoids impediments associated with deceptive research on humans, maximizing the efficacy of automated evaluation before human subjects research must be undertaken. Leveraging recent advances in deep learning, the approach synthesizes realistic, interactive, and adaptive traffic for consumption by target web services. A case study applies the approach to evaluate an intrusion detection system equipped with application-layer embedded deceptive responses to attacks. Results demonstrate that synthesizing adaptive web traffic laced with evasive attacks powered by ensemble learning, online adaptive metric learning, and novel class detection to simulate skillful adversaries constitutes a challenging and aggressive test of cyberdeceptive defenses

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