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The growing adoption of smart grid systems presents significant advancements in the efficiency of energy distribution, along with enhanced monitoring and control capabilities. However, the interconnected and distributed nature of these systems also introduces critical security vulnerabilities that must be addressed. This study proposes a secure communication protocol specifically designed for smart grid environments, focusing on authentication, secret key establishment, symmetric encryption, and hash-based message authentication to provide confidentiality and integrity for communication in smart grid environments. The proposed protocol employs the Elliptic Curve Digital Signature Algorithm (ECDSA) for authentication, Elliptic Curve Diffie–Hellman (ECDH) for secure key exchange, and Advanced Encryption Standard 256 (AES-256) encryption to protect data transmissions. The protocol follows a structured sequence: (1) authentication—verifying smart grid devices using digital signatures; (2) key establishment—generating and securely exchanging cryptographic keys; and (3) secure communication—encrypting and transmitting/receiving data. An experimental framework has been established to evaluate the protocol’s performance under realistic operational conditions, assessing metrics such as time, throughput, power, and failure recovery. The experimental results show that the protocol completes one server–client request in 3.469 ms for a desktop client and 41.14 ms for a microcontroller client and achieves a throughput of 288.27 requests/s and 24.30 requests/s, respectively. Furthermore, the average power consumed by the protocol is 37.77 watts. The results also show that the proposed protocol is able to recover from transient network disruptions and sustain secure communication.

In present era demand for small and portable electronic devices which supports internet connectivity is increasing all around the world and across all industries. These IoT based devices are gaining popularity because of their ease of use. These devices also provide powerful interoperability and feasibility in almost all situation. We can use Internet of Things (IoT) as a great instrument for achieving the desire of connecting people and devices everywhere all the time and in all situations. IoT builds a network of different types of devices such as small handheld devices, vehicles, home appliances etc. that are connected together. This network is incorporated with small sensor nodes, wireless connectivity and actuators for collecting data from adjacent environment and transfer data seamlessly to the base station without any computer or human interaction. Though Internet of Things (IoT) is a primitive term which has greatly attracted the attentions of researchers and academicians it is now getting limited by a number of factors such as flexibility, interoperability, concurrency, scalability and addressing issues. In this paper we will discuss how to make approach when we will go for IoT based solutions considering several parameters such as cost effectiveness, profit generation, maintenance, security etc. In which projects IoT based solutions will be feasible considering. Its network infrastructure, architecture, processing power and scalability.

The increasing threat posed by quantum computing to contemporary cryptographic systems has prompted the urgent need for advanced quantum-resistant solutions in secure communication architectures. This thesis aims to address this concern within the context of a blockchain-based Vehicular Ad-hoc Network (VANET). Specifically, I investigate and develop a proof-of-concept of a new Post Quantum Cryptographic (PQC) solution, Diophantine Isogeny Key Exchange (DIKE), designed to ensure the security of VANET against potential quantum-based attacks. In the dynamic VANET scenario, there is a need for a resilient small key-based PQC solution, which requires less computational operations and storage. DIKE, unlike existing PQC methods, integrates algebraic and geometric properties, presenting a mathematically challenging problem for both classical and quantum computers. The study initially focuses on the development and implementation of a quantum-based attack model, utilizing quantum Shor’s algorithm, to illustrate the vulnerability of the existing VANET infrastructure and the necessity for a quantum-secured blockchain. Furthermore, the research aims to devise the DIKE PQC solution, leveraging Diophantine equations and isogenies to establish a robust key exchange mechanism resilient to quantum threats in dynamic VANET scenarios to acquire minimum latency.To evaluate the efficacy of the proposed quantum attack model, comprehensive simulations of a blockchain-based VANET, vehicle-to-everything (V2X) communication, and vehicular mobility are conducted using advanced simulation tools, including Objective Modular Network Testbed in C++(OMNET++), the extended INET library, Vehicles in Network Simulation (VEINS), and Simulation of Urban Mobility(SUMO). Additionally, quantum modules are integrated into IBM Qiskit, an open-source quantum software development kit, to demonstrate the potential vulnerabilities of the current cryptographic mechanisms in VANET architecture. The results of this investigation reveal the potential susceptibility of the trust-based blockchain scheme in a blockchain-based VANET to a quantum-based impersonation attack. On the other hand, the results and evaluations provide a detailed performance assessment of the DIKE scheme, affirming its efficiency, computational resilience, and suitability for post-quantum cryptography applications, thus underscoring its potential significance in the field. So, as a low-key size, PQC DIKE can be a viable option for secure communication in blockchain-based VANET. Consequently, this study emphasizes the critical importance of implementing quantum-secured protocols to safeguard VANET against quantum threats, thereby ensuring the integrity and security of communication within the connected transportation system.

AmphiKey, a dual-mode post-quantum/traditional (PQ/T) hybrid authenticated key exchange mechanism (AKEM) has been designed to secure smart grid communications against both classical and quantum threats. AmphiKey offers two distinct operational modes within a single framework: an Authenticated Mode and a Deniable Mode. The Authenticated Mode employs a blackbox approach, combining ephemeral ML-KEM-768 and X25519 with long-term Raccoon DSA keys to provide forward secrecy and strong, non-repudiable authenticity. This design achieves \"OR\" confidentiality, where security holds if either of the KEMs is unbroken, and robust \"AND\" authenticity. For the signature operation, it leverages the 'masking-friendly' Raccoon digital signature (DSA), which is specifically designed for side-channel attack resistance, though this protection is localized to the signing key and does not provide deniability. In contrast, Deniable Mode provides deniable authentication, preserving privacy. The protocol used ML-KEM-768 (AKEM-1), Ephemeral X25519 (AKEM-2), Raccoon-based DSA (Rac) (compared performance to ML-DSA-65), and the Ascon cipher to deliver its security guarantees. Key contributions include providing a flexible protocol with enhanced security, optional deniability, and efficiency adapted to the diverse needs of the smart grid infrastructure. We present a comprehensive performance evaluation on a heterogeneous testbed featuring a powerful server and client (AMD Ryzen 5) and a resource-constrained client (Raspberry Pi). In efficient Deniable mode, the full handshake completes in 0.15 ms on the server and 0.41 ms on the Raspberry Pi client. In contrast, the Authenticated Mode is bottlenecked by the client-side signature generation; the handshake takes 4.8 ms for the Raspberry Pi client to initiate and 0.84 ms for the server to verify.

AmphiKey, a dual-mode post-quantum/traditional (PQ/T) hybrid authenticated key exchange mechanism (AKEM) has been designed to secure smart grid communications against both classical and quantum threats. AmphiKey offers two distinct operational modes within a single framework: an Authenticated Mode and a Deniable Mode. The Authenticated Mode employs a blackbox approach, combining ephemeral ML-KEM-768 and X25519 with long-term Raccoon DSA keys to provide forward secrecy and strong, non-repudiable authenticity. This design achieves \"OR\" confidentiality, where security holds if either of the KEMs is unbroken, and robust \"AND\" authenticity. For the signature operation, it leverages the 'masking-friendly' Raccoon digital signature (DSA), which is specifically designed for side-channel attack resistance, though this protection is localized to the signing key and does not provide deniability. In contrast, Deniable Mode provides deniable authentication, preserving privacy. The protocol used ML-KEM-768 (AKEM-1), Ephemeral X25519 (AKEM-2), Raccoon-based DSA (Rac) (compared performance to ML-DSA-65), and the Ascon cipher to deliver its security guarantees. Key contributions include providing a flexible protocol with enhanced security, optional deniability, and efficiency adapted to the diverse needs of the smart grid infrastructure. We present a comprehensive performance evaluation on a heterogeneous testbed featuring a powerful server and client (AMD Ryzen 5) and a resource-constrained client (Raspberry Pi). In efficient Deniable mode, the full handshake completes in 0.15 ms on the server and 0.41 ms on the Raspberry Pi client. In contrast, the Authenticated Mode is bottlenecked by the client-side signature generation; the handshake takes 4.8 ms for the Raspberry Pi client to initiate and 0.84 ms for the server to verify.

In this paper, we validate the performance of the a sensor fusion-based Global Navigation Satellite System (GNSS) spoofing attack detection framework for Autonomous Vehicles (AVs). To collect data, a vehicle equipped with a GNSS receiver, along with Inertial Measurement Unit (IMU) is used. The detection framework incorporates two strategies: The first strategy involves comparing the predicted location shift, which is the distance traveled between two consecutive timestamps, with the inertial sensor-based location shift. For this purpose, data from low-cost in-vehicle inertial sensors such as the accelerometer and gyroscope sensor are fused and fed into a long short-term memory (LSTM) neural network. The second strategy employs a Random-Forest supervised machine learning model to detect and classify turns, distinguishing between left and right turns using the output from the steering angle sensor. In experiments, two types of spoofing attack models: turn-by-turn and wrong turn are simulated. These spoofing attacks are modeled as SQL injection attacks, where, upon successful implementation, the navigation system perceives injected spoofed location information as legitimate while being unable to detect legitimate GNSS signals. Importantly, the IMU data remains uncompromised throughout the spoofing attack. To test the effectiveness of the detection framework, experiments are conducted in Tuscaloosa, AL, mimicking urban road structures. The results demonstrate the framework's ability to detect various sophisticated GNSS spoofing attacks, even including slow position drifting attacks. Overall, the experimental results showcase the robustness and efficacy of the sensor fusion-based spoofing attack detection approach in safeguarding AVs against GNSS spoofing threats.

Blockchain-based Vehicular Ad-hoc Network (VANET) is widely considered as secure communication architecture for a connected transportation system. With the advent of quantum computing, there are concerns regarding the vulnerability of this architecture against cyber-attacks. In this study, a potential threat is investigated in a blockchain-based VANET, and a corresponding quantum cyber-attack is developed. Specifically, a quantum impersonation attack using Quantum-Shor algorithm is developed to break the Rivest-Shamir-Adleman (RSA) encrypted digital signatures of VANET and thus create a threat for the trust-based blockchain scheme of VANET. A blockchain-based VANET, vehicle-to-everything (V2X) communication, and vehicular mobility are simulated using OMNET++, the extended INET library, and vehicles-in-network simulation (VEINS) along with simulation of urban mobility (SUMO), respectively. A small key RSA based message encryption is implemented using IBM Qiskit, which is an open-source quantum software development kit. The findings reveal that the quantum cyber-attack, example, impersonation attack is able to successfully break the trust chain of a blockchain-based VANET. This highlights the need for a quantum secured blockchain.

Blockchain-based Vehicular Ad-hoc Network (VANET) is widely considered as secure communication architecture for a connected transportation system. With the advent of quantum computing, there are concerns regarding the vulnerability of this architecture against cyber-attacks. In this study, a potential threat is investigated in a blockchain-based VANET, and a corresponding quantum cyber-attack is developed. Specifically, a quantum impersonation attack using Quantum-Shor algorithm is developed to break the Rivest-Shamir-Adleman (RSA) encrypted digital signatures of VANET and thus create a threat for the trust-based blockchain scheme of VANET. A blockchain-based VANET, vehicle-to-everything (V2X) communication, and vehicular mobility are simulated using OMNET++, the extended INET library, and vehicles-in-network simulation (VEINS) along with simulation of urban mobility (SUMO), respectively. A small key RSA based message encryption is implemented using IBM Qiskit, which is an open-source quantum software development kit. The findings reveal that the quantum cyber-attack, example, impersonation attack is able to successfully break the trust chain of a blockchain-based VANET. This highlights the need for a quantum secured blockchain.

Heart disorder has just overtaken cancer as the world's biggest cause of mortality. Several cardiac failures, heart disease mortality, and diagnostic costs can all be reduced with early identification and treatment. Medical data is collected in large quantities by the healthcare industry, but it is not well mined. The discovery of previously unknown patterns and connections in this information can help with an improved decision when it comes to forecasting heart disorder risk. In the proposed study, we constructed an ML-based diagnostic system for heart illness forecasting, using a heart disorder dataset. We used data preprocessing techniques like outlier detection and removal, checking and removing missing entries, feature normalization, cross-validation, nine classification algorithms like RF, MLP, KNN, ETC, XGB, SVC, ADB, DT, and GBM, and eight classifier measuring performance metrics like ramification accuracy, precision, F1 score, specificity, ROC, sensitivity, log-loss, and Matthews' correlation coefficient, as well as eight classification performance evaluations. Our method can easily differentiate between people who have cardiac disease and those are normal. Receiver optimistic curves and also the region under the curves were determined by every classifier. Most of the classifiers, pretreatment strategies, validation methods, and performance assessment metrics for classification models have been discussed in this study. The performance of the proposed scheme has been confirmed, utilizing all of its capabilities. In this work, the impact of clinical decision support systems was evaluated using a stacked ensemble approach that included these nine algorithms