Ensemble and Deep-Learning Methods for Two-Class and Multi-Attack Anomaly Intrusion Detection: An Empirical Study
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2019, Vol 10, Issue 9
Abstract
Cyber-security, as an emerging field of research, involves the development and management of techniques and technologies for protection of data, information and devices. Protection of network devices from attacks, threats and vulnerabilities both internally and externally had led to the development of ceaseless research into Network Intrusion Detection System (NIDS). Therefore, an empirical study was conducted on the effectiveness of deep learning and ensemble methods in NIDS, thereby contributing to knowledge by developing a NIDS through the implementation of machine and deep-learning algorithms in various forms on recent network datasets that contains more recent attacks types and attackers’ behaviours (UNSW-NB15 dataset). This research involves the implementation of a deep-learning algorithm–Long Short-Term Memory (LSTM)–and two ensemble methods (a homogeneous method–using optimised bagged Random-Forest algorithm, and a heterogeneous method–an Averaged Probability method of Voting ensemble). The heterogeneous ensemble was based on four (4) standard classifiers with different computational characteristics (Naïve Bayes, kNN, RIPPER and Decision Tree). The respective model implementations were applied on the UNSW_NB15 datasets in two forms: as a two-classed attack dataset and as a multi-attack dataset. LSTM achieved a detection accuracy rate of 80% on the two-classed attack dataset and 72% detection accuracy rate on the multi-attack dataset. The homogeneous method had an accuracy rate of 98% and 87.4% on the two-class attack dataset and the multi-attack dataset, respectively. Moreover, the heterogeneous model had 97% and 85.23% detection accuracy rate on the two-class attack dataset and the multi-attack dataset, respectively.
Authors and Affiliations
Adeyemo Victor Elijah, Azween Abdullah, NZ Jhanjhi, Mahadevan Supramaniam, Balogun Abdullateef O
Keywords
Related Articles
- EP ID EP646212
- DOI 10.14569/IJACSA.2019.0100969
- Views 78
- Downloads 0
Software Design Principles to Enhance SDN Architecture
SDN as a network architecture emerged on top of existing technologies and knowledge. Through defining the controller as a software program, SDN made a strong connection between networking and software engineering. Tradit...
Performance Analysis of Machine Learning Techniques on Software Defect Prediction using NASA Datasets
Defect prediction at early stages of software development life cycle is a crucial activity of quality assurance process and has been broadly studied in the last two decades. The early prediction of defective modules in d...
ASSA: Adaptive E-Learning Smart Students Assessment Model
Adaptive e-learning can be improved through measured e-assessments that can provide accurate feedback to instructors. E-assessments can not only provide the basis for evaluation of the different pedagogical methods used...
Power Management of a Stand-Alone Hybrid (Wind/Solar/Battery) Energy System: An Experimental Investigation
In this manuscript, a hybrid wind/solar/battery energy system is proposed for a stand-alone applications. Wind-solar energy sources are used as power generation source in the proposed hybrid energy system (HES), whereas...
On Algebraic Spectrum of Ontology Evaluation
Ontology evaluation remains an important open problem in the area of its application. The ontology structure evaluation framework for benchmarking the internal graph structures was proposed. The framework was used in tra...