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Underwater Wireless Sensor Networks (UWSNs) are the type of WSNs that transmit the data through water medium and monitor the oceanic conditions, water contents, under-sea habitations, underwater beings and military objects. Unlike air medium, water channel creates stronger communication barriers. In addition, the malicious data injection and other network attacks create security problems during data communication. Protecting the vulnerable UWSN channel is not an easy task under critical water conditions. Many research works proposed in the literature used cryptography principles and intelligent intrusion detection systems to secure the network activities from malicious nodes. However, the need for Machine Learning (ML) and Deep Learning (DL) associated Medium Access Control (MAC) principles is expected for handling the barriers in uncertain UWSN. In this regard, this article proposes a new Intrusion detection system with Integrated Secure MAC principles and Long Short-Term Memory (LSTM) architectures for organizing real-time neighbor monitoring tasks. The proposed system implements Generative Adversarial Network (GAN) driven UWSN channel assessment models and Secure LSTM-MAC principles to protect the data communication. In this regard, the proposed model creates the Intrusion Detection System (IDS) using trained distributed agents. These agents run in each legitimate sensor node contain novel LSTM-MAC engine, intrusion dataset, rule-based monitoring techniques, Secure Hashing Algorithm-3 (SHA-3), Two Fish algorithm and packet filtering tools. The proposed LSTM and agent-based model drives adaptive MAC channel operations to avoid malicious traffics in to legitimate nodes. In addition, this work implements neighbor-based packet monitoring, signal jamming and alert messaging procedures to build reliable security services against different types of attacks. The experiments and the observations reveal the performance of proposed techniques is proved to be 5% to 10% higher than existing techniques in various aspects measured with different metrics. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) contain multiple wireless sensor nodes deployed around the geographical locations. The WSN used in military applications need more security and hence the deployment of trustworthy nodes and links in WSN provides more secure data transmission in Decentralized Military Sensor Networks (DMSNs). Moreover, the DMSNs work with different set of significance constraints including higher security requirements. The design of DMSNs targets surveillance tasks, intruder tracking tasks, army resource maintenance tasks and communication security requirements. Therefore, building a secure and dynamic DMSN against multiple threats is a challenging task. In addition, security principles developed for DMSN cause excessive energy consumption. Moreover, DMSN has completely open distributed architecture without having any base stations. Under this situation, the need for effective and secured data communication can be achieved with the help of a secure routing protocol. Block chains are generally used for making secure financial transactions. However, the routing protocols used in DMSN can support autonomous routing transactions from one node to other node. In this situation, block chain enabled routing procedures can ensure the trustworthiness of any data that is forwarded through different sensor nodes. Hence, a new Generative Adversarial Networks (GAN) based Block Chain enabled secured Routing Protocol (GBCRP) is proposed in this paper which authenticates and validates the ongoing routing procedures of DMSN. Moreover, a new intrusion detection system is also proposed in this work using GAN which is deployed in the nodes of the DMSN for enhancing the security of communication. Since block chain based routing protocols do not provide security, the GBCRP works for creating volatile block chains using decentralized authentication principles and effective intrusion detection. The proposed system uses a Fully Decentralized Generative Adversarial Network (FDGAN) for monitoring the secure routing transactions by the development of an intrusion detection system. The results obtained from this work show that the proposed GBCRP providing better secured routing compared to the existing systems with respect to security, energy consumption and routing metrics. [ABSTRACT FROM AUTHOR]