Data, especially image data, is transmitted at an incredible rate due to the exponential growth in the number of connected devices brought about by the fast development of consumer electronics. Data transmission security remains fundamental in effective communication systems, especially with the emergence of the quantum era. Quantum advancements challenge traditional asymmetric cryptography, necessitating the requirement for new encryption schemes. The multimedia encryption schemes pose further challenges due to its large size, structural complexity, real-time requirements, and unique attack vectors specific to multimedia data. The current classical image encryption algorithms do not fully address the challenges posed by Quantum technology for securing the multimedia transmission. This paper proposes a hybrid framework combining Quantum Key Distribution (QKD), chaos-based encryption, and Post-Quantum Cryptography (PQC) to secure multimedia data, particularly images, in IoT consumer electronic networks. Integrating 2D-Logistic Sine Coupling Map (2D-LSCM), initialized with the Keys generated from QKD, and NTRU algorithm, this scheme achieves robust security and efficient performance. This new architecture improves processing speeds and enhances specificity and sensitivity values, fortifying chaotic map performance. Our experimental results and comprehensive security analysis demonstrate the security and performance of the framework. Correlation Analysis using Person correlation coefficient, with a nearing zero value, ensures the confidentiality of the encrypted image.
Data, especially image data, is transmitted at an incredible rate due to the exponential growth in the number of connected devices brought about by the fast development of consumer electronics. Data transmission security remains fundamental in effective communication systems, especially with the emergence of the quantum era. Quantum advancements challenge traditional asymmetric cryptography, necessitating the requirement for new encryption schemes. The multimedia encryption schemes pose further challenges due to its large size, structural complexity, real-time requirements, and unique attack vectors specific to multimedia data. The current classical image encryption algorithms do not fully address the challenges posed by Quantum technology for securing the multimedia transmission. This paper proposes a hybrid framework combining Quantum Key Distribution (QKD), chaos-based encryption, and Post-Quantum Cryptography (PQC) to secure multimedia data, particularly images, in IoT consumer electronic networks. Integrating 2D-Logistic Sine Coupling Map (2D-LSCM), initialized with the Keys generated from QKD, and NTRU algorithm, this scheme achieves robust security and efficient performance. This new architecture improves processing speeds and enhances specificity and sensitivity values, fortifying chaotic map performance. Our experimental results and comprehensive security analysis demonstrate the security and performance of the framework. Correlation Analysis using Person correlation coefficient, with a nearing zero value, ensures the confidentiality of the encrypted image.
With the rapid growth of multimedia communication over the internet, ensuring the security and confidentiality of digital images and videos has become a critical challenge.Traditional encryption techniques such as AES, DES, and RSA are primarily designed for textual data and may not always be efficient for large multimedia files due to high computational complexity and data redundancy.To address this issue, this paper presents a quantum-inspired multimedia encryption system based on the Generalized Quantum Image Representation (GQIR) and Qiskit-based key generation.The proposed system converts digital images into quantum image representation using GQIR, where pixel positions and colour values are encoded using quantum bits.A quantum-inspired keystream is generated using Qiskit quantum circuits, which is then applied through an XOR-based encryption mechanism to secure images and video frames.Security evaluation is performed using Shannon entropy, histogram distribution, and pixel correlation coefficient analysis.The system is deployed as a Flask-based web application, enabling users to upload multimedia files, perform encryption and decryption, and visualize analytical results.The results demonstrate high entropy values (7.8-7.99),near-zero pixel correlation, and uniform histogram distribution in encrypted outputs, confirming strong randomness and resistance to statistical attacks.
The emergence of quantum computing has introduced unprecedented security challenges to conventional cryptographic systems, particularly in the domain of optical communications. This research addresses these challenges by innovatively combining quantum key distribution (QKD), specifically the E91 protocol, with logistic chaotic maps to establish a secure image transmission scheme. Our approach utilizes the unpredictability of chaotic systems alongside the robust security mechanisms inherent in quantum entanglement. The scheme is further fortified with an eavesdropping detection mechanism based on CHSH inequality, thereby enhancing its resilience against unauthorized access. Through quantitative simulations, we demonstrate the effectiveness of this scheme in encrypting images, achieving high entropy and sensitivity to the original images. The results indicate a significant improvement in encryption and decryption efficiency, showcasing the potential of the scheme as a viable solution against the vulnerabilities posed by quantum computing advancements. Our research offers a novel perspective in secure optical communications, blending the principles of chaos theory with QKD to create a more robust cryptographic framework.
The emergence of quantum computing seriously threatens the security of legacy cryptographic protocols in consumer electronics devices based on the Internet of Things (IoT). These are essential components of mission-critical infrastructures, such as industrial control systems, smart grids, and healthcare. In particular, this work examines how to strengthen the resistance of the IoT to classical and quantum-based cyberattacks by combining deep learning methods with post-quantum cryptography (PQC). To protect key exchange, encryption, and authentication processes in IoT-consumer devices, we will continue to use quantum-resistant cryptographic algorithms, including lattice-based, hash-based, and code-based cryptography. To provide real-time anomaly detection and response capabilities, we suggest a hybrid security framework combining PQC with deep learning-based intrusion detection systems. Our methodology ensures secure communications and data exchanges linked to quantum attacks in the future within IoT systems while leveraging continuous learning over time to allow Intrusion Detection System (IDS) models to adapt to new attack vectors. We suggest optimized algorithms for PQC solutions that are lightweight and suitable for deployment in edge computing nodes and Internet of Things devices, considering the resource-constrained IoT situations where PQC implementation would be difficult. Through comparative studies and simulations, we demonstrate how our integrated PQC-deep learning framework offers an improved IoT security solution that could safeguard vital systems in a world powered by quantum technology. The results shows the key generation time which is 12.5ms, encryption/decryption time 25.3ms, latency 18.7ms, throughput 5000 operations/sec and energy consumption 2.4mJ.
The rapid growth of IoT networks has developed connectivity and automation, leading to significant advancements across various domains. However, this expansion is challenging but rather seriously making with the introduction of quantum computing, as it is currently a strong threat to all the established cryptography-based applications. To address these related drawbacks, this study proposes a novel quantum-resistant cryptographic framework called QRC_BIKAES for secure IoT communication. The proposed QRC_BIKAES framework integrates Bit-Flipping Key Encapsulation (BIKE), a Post-Quantum Cryptographic (PQC) algorithm, with Advanced Encryption Standard (AES-128) for its lightweight and efficient data encryption. BIKE ensures robust, quantum-resistant key generation and exchange, while AES-128 provides fast and resource-efficient data encryption suitable for constrained IoT devices. By combining these complementary techniques, the framework effectively addresses quantum security threats while maintaining scalability and performance in resource-constrained IoT environments. This approach presents a significant advancement in IoT security and establishes a resilient and future-ready communication infrastructure.
Secure image transmission is increasingly vital in the digital era, especially against emerging quantum threats. This study proposes a hybrid image encryption scheme that integrates Quantum Key Distribution (QKD) using the BB84 protocol with a combination of 7-dimensional (7D) and 2-dimensional (2D) hyperchaotic systems to achieve robust security. The BB84 protocol facilitates quantum-assisted key exchange, ensuring resistance to eavesdropping, while the hyperchaotic systems provide high entropy and complex randomness, utilized in a layered permutation-substitution encryption framework. The initial seeds for chaotic sequences are derived using a SHA-512 hash of both the input image and quantum-generated key, ensuring uniqueness and sensitivity. Experimental validation was conducted using several benchmark images. The information entropy values of the ciphered images reached up to 7.9993, indicating excellent randomness. Differential analysis showed high resistance to small perturbations, with NPCR exceeding 99.61% and UACI averaging around 33.47%, which meet standard security thresholds. Histogram and chi-square tests confirmed the uniform pixel distribution, with chi-square values below 280, satisfying the randomness criterion for 8-bit images. Furthermore, correlation coefficients of adjacent pixels dropped to near zero, evidencing effective decorrelation. The encryption scheme also demonstrated robustness to data loss, as shown by the successful decryption of partially corrupted cipher images. Robustness testing under partial data loss (200×200-pixel blocks) also demonstrated visual recoverability and algorithm resilience. Overall, the proposed BB84-assisted dual-hyperchaotic encryption scheme offers a secure and computationally effective solution for protecting sensitive image data, making it suitable for post-quantum secure communications.
With the advancement of quantum computing, the long-term security of IoT systems is increasingly challenged, particularly due to the vulnerability of traditional public-key cryptography. This study integrates artificial intelligence (AI) with a hybrid strategy that involves Post-Quantum Cryptography (PQC) and Quantum Key Distribution (QKD) to address this challenge. Incoming traffic from the TON-IoT dataset is processed by AI using XGBoost, Random Forest, and One-Class SVM as anomaly-detection filters. Only traffic classified as normal is forwarded to the encryption phase. Postquantum key establishment is performed using Kyber-512, and the resulting shared secret is combined with simulated BB84based QKD key material via a key-derivation function (KDF) to derive a 256-bit session key, which is then used with AES-256GCM for authenticated IoT data encryption. Experimental results demonstrate that the hybrid algorithm, along with the AI, can effectively improve IoT security. Upon optimization of the parameterβ(=1.0), the Delivery Purity is 0.92, the Data Delivery rate is 0.80, and the Secure DeliveryFβscore is 0.94 compared to Delivery Purity of 0.77, Delivery Rate of 1.0, and Secure Delivery Fβ score of 0.87, respectively, without AI.