In today’s world of connected systems, protecting those systems is imperative. Intrusion Detection Systems (IDS) have an important function to perform to protect these systems by detecting and monitoring malicious activity and policy breaches. This project develops an intelligent IDS based on Artificial Intelligence (AI) and Machine Learning (ML) to identify potential security threats online. The system applies sound ML methods to examine the traffic on a system and separate benign and malicious behavior. A variety of ML methods / Algorithms exist to differentiate bad and good data, and among those methods are supervised and unsupervised learning models to enhance known and unknown threat detection. Benchmark data sets are used to train the system so that it can detect a variety of attacks, including distributed denial of service (DDoS), probing, and remote-to-local (R2L) attacks efficiently. There is also use of feature selection and dimensionality reduction methods to achieve maximum computational performance and efficiency. The suggested AI and ML-based IDS provides scalable and flexible security through continuous learning to respond to emerging threats. It is a potent choice for securing networks of today because it supports scalability and real-time analytics.

The Mission-critical IIoT (MC-IIoT) networks are highly interconnected and distributed to ensure real-time monitoring and control, where security, reliability, and availability are paramount. These features make MC-IIoT networks vulnerable to Distributed Denial of Service (DDoS) attacks that must be mitigated while combating single-point of failure of attack detection systems, limited scalability of industrial control systems, high latency and low reliability. In this paper, we propose a Blockchain-based DDoS attack mitigation framework for MC-IIoT networks which persuades these requirements. Initially, a machine learning-based cascaded model consisting of an autoencoder-enhanced decision tree model and a lightweight isolation forest algorithm performs attack detection and trust score evaluation of IIoT devices. A consortium Blockchain network of IIoT gateways on which this cascaded model is installed performs attack mitigation using a chaincode based on previously evaluated trust scores. The proposed model is evaluated on Hyperledger Fabric and the performance is analyzed with Hyperledger caliper benchmarking. The comparative analysis of the proposed framework with state-of-the-art proves its effectiveness in terms of attack detection accuracy (99.93%), CPU utilization (30%), decreased latency (12.5 ms), increased throughput (21%), chaincode execution time (3 ms) and attack detection rate (99.6%).

With the rapid development of the Internet of Things (IoT), modern Maritime Transportation Systems (MTS) have played a crucial role in the transport industry. The various potential privacy and security concerns (such as vessels’ location tracking, message tampering, unauthorized access to data, etc.) have increased substantially in IoT-enabled MTS. Considering the above issues, we propose a secure, robust, and efficient authentication scheme for MTS based on Elliptic-Curve Cryptography (ECC) and Hash-based Message Authentication Code (HMAC) called PSAS-MTS. The proposed PSAS-MTS scheme not only monitors the vessel’s condition but also ensures protection against collisions in route management and provides safety to the vessel’s passengers. The scheme uses simple XOR, a cryptographic one-way hash, an ECC cryptosystem, and HMAC to achieve a high level of security in MTS. The formal security analysis is carried out using a well-known Real-Or-Random (ROR) model, which ensures the security harness features. A rigorous informal security analysis is also done, which ensures various privacy and security features such as mutual authentication, anonymity, forward and backward secrecy, etc. The proposed PSAS-MTS scheme resists various possible well-known active and passive security attacks. The performance analysis shows that the proposed PSAS-MTS scheme is more efficient in terms of computation and communication overheads when compared to other competitive schemes.

The real challenge in Wireless Body Area Network (WBAN) lies in ensuring the security of health-related sensitive data and diagnostic information, preventing unauthorized access by malicious users. Given that information exchange takes place between the WBAN client and the Application Provider (AP), there is a critical need for thorough attention to mutual authentication and secure key generation. Many of the available research works lack the addressing of essential security features such as strong anonymity, key confidentiality, and key-escrow resilience. The primary focus of this research work is to develop a certificateless anonymous mutual authentication scheme that will be computationally efficient and memory-optimized, while maintaining uncompromising security features. Our scheme excels over other schemes due to its utilization of a hybrid approach, which integrates cryptographic operations related to the Discrete Logarithm Problem (DLP), Elliptic Curve Discrete Logarithm Problem (ECDLP), and bi-linear pairings. This approach maintains strong security features and also enhances the performance of the system. The proposed scheme improves computational cost, storage cost, and communication cost by at least 15%, 41%, and 10%, respectively, compared to the best available schemes. We have also verified our protocol using formal and informal approaches. Formal logic analysis and simulation of our scheme ensure its potential to significantly enhance user privacy and strengthen mutual authentication in Smart healthcare systems.