| Title | Using Temporal Conceptual Graphs and Neural Networks for Big Data-Based Attack Scenarios Reconstruction |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Djemaiel, Yacine, Fessi, Boutheina A., Boudriga, Noureddine |
| Conference Name | 2019 IEEE Intl Conf on Parallel Distributed Processing with Applications, Big Data Cloud Computing, Sustainable Computing Communications, Social Computing Networking (ISPA/BDCloud/SocialCom/SustainCom) |
| Date Published | dec |
| Keywords | Attack Graphs, Attack Scenario Graph, Big Data, big data-based attack scenarios reconstruction, complex attack scenarios, complex attacks, composability, Elman, Elman network, global attack reconstruction process, graph theory, high speed networks, hybrid neural network, investigation, Neural networks, possible attack scenarios, potential attack scenario, Predictive Metrics, probable attack scenario, pubcrawl, radial basis function networks, RBF, RBF networks, Resiliency, security of data, temporal conceptual graph |
| Abstract | The emergence of novel technologies and high speed networks has enabled a continually generation of huge volumes of data that should be stored and processed. These big data have allowed the emergence of new forms of complex attacks whose resolution represents a big challenge. Different methods and tools are developed to deal with this issue but definite detection is still needed since various features are not considered and tracing back an attack remains a timely activity. In this context, we propose an investigation framework that allows the reconstruction of complex attack scenarios based on huge volume of data. This framework used a temporal conceptual graph to represent the big data and the dependency between them in addition to the tracing back of the whole attack scenario. The selection of the most probable attack scenario is assisted by a developed decision model based on hybrid neural network that enables the real time classification of the possible attack scenarios using RBF networks and the convergence to the most potential attack scenario within the support of an Elman network. The efficiency of the proposed framework has been illustrated for the global attack reconstruction process targeting a smart city where a set of available services are involved. |
| DOI | 10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00143 |
| Citation Key | djemaiel_using_2019 |
Using Temporal Conceptual Graphs and Neural Networks for Big Data-Based Attack Scenarios Reconstruction