  Advancements in computer networks and communication technologies like
software defined networks (SDN), Internet of things (IoT), microservices
architecture, cloud computing and network function virtualization (NFV) have
opened new fronts and challenges for security experts to combat against modern
cyberattacks. Relying on perimeter defense and signature-based network security
solutions like Intrusion Detection and Prevention Systems (IDS/IPS) have failed
to deliver adequate level of security against new attack vectors such as
advance persistent threats, zero days, ransomware, botnets and other forms of
targeted attacks. Recent developments in machine learning and cognitive
computing have shown great potential to detect unknown and new intrusion events
where legacy misuse and anomaly based intrusion detection systems usually fail.
In this research study we applied state of the art machine learning algorithms
on UNSW-NB15 dataset for potential applicability to detect new attacks. We also
proposed a novel wrapper based feature selection technique TS-RF using
metaheuristic Tabu Search (TS) algorithm and Random Forest (RF) ensemble
classifier. Results obtained by applying proposed feature selection technique
i.e. TS-RF on UNSW-NB15 dataset show improvement in overall intrusion detection
accuracy while it reduces computation complexity as it removes more than 60%
features.
