7,064 research outputs found

    A Predictive Model for Network Intrusion Detection System Using Deep Neural Network

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    Network Intrusion Detection System (NIDS) is an important part of Cyber safety and security. It plays a key role in all networked ICT systems in detecting rampant attacks such as Denial of Service (DoS) and ransom ware attacks. Existing methods are inadequate in terms of accuracy detection of attacks. However, the requirement for high accuracy detection of attacks using Deep Neural Network requires expensive computing resources which in turn make most organisations, and individuals shy away from it. This study therefore aims at designing a predictive model for network intrusion detection using deep neural networks with very limited computing resources. The study adopted Cross Industry Standard Process for Data Mining (CRISP-DM) as one of the formal methodologies and python was used for both testing and training, using crucial parameters such as the learning rate, number of epochs, neurons and hidden layers which greatly determined the accuracy level of the DNN algorithm. These parameters were experimented with values that are lesser compared to previous studies, training and evaluation were also done on the KDD99 data-set. The varying values of accuracy obtained from this study on four models with different numbers of layers of 50-epochs and learning rate of 0.01 achieved competitive results in comparison with the previous research of 100-1000 epochs and learning rate of 0.1. Therefore, the model with two layers attained same accuracy of 0.955 as the model with three layers from the previous study out of the four models tested in this study. Also, the models with three and four layers in this study attained an accuracy of 0.956, which is 0.001greater than the previous study's models. Keywords: Network-Based IDS, Host-Based IDS, Deep Neural Network, Denial of Service, Knowledge Discovery Datase

    AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

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    This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

    Poseidon: a 2-tier Anomaly-based Network Intrusion Detection System

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    We present Poseidon, a new anomaly based intrusion detection system. Poseidon is payload-based, and presents a two-tier architecture: the first stage consists of a Self-Organizing Map, while the second one is a modified PAYL system. Our benchmarks on the 1999 DARPA data set show a higher detection rate and lower number of false positives than PAYL and PHAD

    Poseidon: a 2-tier Anomaly-based Intrusion Detection System

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    We present Poseidon, a new anomaly based intrusion detection system. Poseidon is payload-based, and presents a two-tier architecture: the first stage consists of a Self-Organizing Map, while the second one is a modified PAYL system. Our benchmarks on the 1999 DARPA data set show a higher detection rate and lower number of false positives than PAYL and PHAD

    Artificial intelligence in the cyber domain: Offense and defense

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    Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41

    TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-based Intrusion Detection System

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    Intrusion detection systems (IDS) play a pivotal role in computer security by discovering and repealing malicious activities in computer networks. Anomaly-based IDS, in particular, rely on classification models trained using historical data to discover such malicious activities. In this paper, an improved IDS based on hybrid feature selection and two-level classifier ensembles is proposed. An hybrid feature selection technique comprising three methods, i.e. particle swarm optimization, ant colony algorithm, and genetic algorithm, is utilized to reduce the feature size of the training datasets (NSL-KDD and UNSW-NB15 are considered in this paper). Features are selected based on the classification performance of a reduced error pruning tree (REPT) classifier. Then, a two-level classifier ensembles based on two meta learners, i.e., rotation forest and bagging, is proposed. On the NSL-KDD dataset, the proposed classifier shows 85.8% accuracy, 86.8% sensitivity, and 88.0% detection rate, which remarkably outperform other classification techniques recently proposed in the literature. Results regarding the UNSW-NB15 dataset also improve the ones achieved by several state of the art techniques. Finally, to verify the results, a two-step statistical significance test is conducted. This is not usually considered by IDS research thus far and, therefore, adds value to the experimental results achieved by the proposed classifier
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