arXiv subjects

Kazi Hassan Shakib

Publications and source records attributed to Kazi Hassan Shakib.

5 recordsLinked to original sources

arXiv2026

HDKEM: Authentication Allocation for Deniable Hybrid Key Exchange

Deniable authenticated key exchange (DAKE) establishes a shared key without leaving transferable evidence that a party participated. We study DAKE protocols that authenticate each fresh session with one ring signature and show that their deniability mechanisms fall into three authentication allocations: sharing an authentication witness, hiding the signing witness under a fixed ring, or varying the verified ring between real and simulated executions. For fixed-ring protocols, witness hiding yields a necessary relation between the ring signature's witness-indistinguishability gap and the protocol's $(μ,δ)$ deniability parameters. We instantiate witness hiding in HDKEM, a three-slot hybrid that combines ML-KEM-768, X25519, and an optional pairwise information-theoretic source through a split-key PRF. HDKEM binds all public session values, including a QKD key identifier when present, into authentication and key derivation. Under the stated component models, it achieves eCK key indistinguishability, forward secrecy, and strong local deniability. We evaluate HDKEM with FalconRS and Gandalf. Across 2,000 primitive operations, median signing/verification times are 9.20/1.02 ms for FalconRS and 18.61/0.97 ms for Gandalf in our reference environment. In source-backed mode, the corresponding TCP-application costs are 8,607 B and 8,555 B per handshake. A separate 500-session server experiment exercises the complete FalconRS source-backed path and reports a median server time of 16.29 ms including local I/O.

cs.CR↗

arXiv2026

AmphiKey: A Dual-Mode Secure Authenticated Key Encapsulation Protocol for Smart Grid

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.

cs.CR↗

arXiv2025

Quantum Cyber-Attack on Blockchain-based VANET

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.

cs.CR↗

arXiv2024

Experimental Validation of Sensor Fusion-based GNSS Spoofing Attack Detection Framework for Autonomous Vehicles

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.

cs.CR↗

arXiv2023

An Improved Heart Disease Prediction Using Stacked Ensemble Method

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

cs.LG↗