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Cyber Physical Systems (CPS) security within industrial fields have enforced itself, due to its deployment critical infrastructure. The complexity, and diversity are evolved with these CPS systems. While connectivity demands for these systems to communicate with each other increases, their attack surface expands. The impact of cybersecurity has on business continuity increase. ICS can run real time critical function, where firewall inspection delay can fail the process. Hence a special firewall consideration needs to be implemented. The Uptime requirements for these ICS is extremely high, which means the normal maintenance or security patches is out of discussion. Beside a modification on any ICS due to firmware update, can trigger revalidations for all interconnected ICS. Perimeter defense firewall is one of the common strategies to protect these ICS systems. The firewall will inspect and detect ingress traffic. Internal firewall will be more enhanced way to protect also from internal attacks within the network. Hence, a need for more efficient ways to detect these attacks, based on Deep Learning (DL) approach with a good source of (Industrial Internet of Things) IIoT dataset. This conference paper evaluates Deep learning approach using Bi-directional Long Short-Term Memory (BLSTM) on resent publicly available dataset “Edge-IIoTset”. This dataset has realistic dataset of IIoT applications. With more than 10 types of sensors/devices uses in ICS systems. With fourteen attacks including DoS/DDoS attack. In this research paper, we consider utilizing deep learning algorithms (BLSTM) to detect and protect the service availability of Critical Infrastructure (CI) and Industrial Control Systems (ICS) from Denial of Service (DoS)attack. The research proposal considers most recent dataset with packet compared with flow format to train our module. The benchmarking with common metrics is used as baseline to compare algorithm efficiency, where accuracy of 99.877 was achieved and validation time of 18millisconds.
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