http://hdl.handle.net/123456789/13170 2026-04-04T12:14:13Z http://hdl.handle.net/123456789/19598 Optimized Software Implementations for NIST’s Post Quantum Cryptography Candidates Using NEON Based Instructions Anjum Ashraaf, 01-247231-005 The development of quantum computers is a serious threat to traditional cryptography methods that use well-known algorithms like RSA and ECC. These algorithms are secure from classical computers, but they rely on mathematical problems like integer factorization and discrete logarithm problems, which quantum computers can solve effectively. To counter this new threat, cryptographers throughout the world are actively developing a set of quantum-resistant cryptographic protocols called Post-Quantum Cryptography (PQC). The computational effectiveness and performance of PQC algorithms on low-resource devices, however, pose a serious barrier to their widespread use. This research aims to optimize post-quantum cryptographic algorithms, specifically NTRU and Kyber, for ARM-based systems. The study focuses on implementing these algorithms on Android (Google Pixel 2) and Raspberry Pi while leveraging NEON SIMD optimizations to enhance efficiency. A key focus is optimizing polynomial multiplication, a core operation in lattice-based encryption, by evaluating various strategies and selecting the most effective for NEON acceleration. Performance improvements are systematically analyzed across different versions of Kyber and NTRU, with benchmarking against NIST PQC reference implementations to assess speedup and efficiency gains. This research presents the first NEON-optimized implementations of post-quantum cryptographic algorithms on Android, achieving significant performance gain of 2.6x in encryption and 3.1x in decryption of kyber on Android. Similarly in case of NTRU the performance gain is 2.6x in encryption and 5.5x in decryption when compared with the reference implementation. Additionally, a new multiplication design is also presented for kyber on Intel processors, which gives performance gain of 1.17x in encryption and 1.19x in decrypiton suggesting similar optimization techniques could enhance PQC performance on ARM architectures. A novel variant of the NTRU cryptosystem, called M-NTRU, is introduced. NTRU relies on polynomial arithmetic over Z[x] and the hardness of the closest and shortest vector problems, with security based on solving polynomial equations modulo unrelated moduli. In M-NTRU, the polynomial ring Z[x] is replaced with the matrix ring Zp[x]/⟨Xn+1⟩, operating in a non-commutative matrix ring within a Galois field. This enhances security by expanding the key space and mitigating lattice attacks due to highdimensional structures and non-commutativity. While maintaining the conventional NTRU framework, M-NTRU modifies polynomial representation, arithmetic operations, and key generation to strengthen resistance against cryptanalysis. These optimizations will benefit greatly in adoption of the PQC algorithms specially on the resource constrained ARM devices. Supervised by Dr. Asim Ali 2025-01-01T00:00:00Z http://hdl.handle.net/123456789/19597 A Hybrid Authentication Approach (LPCAP-IoD) To Enhance Security in Internet of Drones (IoD) Muhammad Imran Khan, 01-247231-008 The Internet of Drones (IoD) is a field where a lot of research is underway due to the ever growing demand and applications in all domain of life such as miliary (for Information sharing, search & rescue, surveillance, imagery, payload transfer etc), socio-economic development, agriculture, shipment/ delivery services, disaster management and fire fighting. However, the open and dynamic nature of IoD environments makes them vulnerable to various security attacks/ threats such as replay attacks, impersonation attacks, eavesdropping, drone hijacking, physical attacks, quantum computing threats etc. Various schemes have been proposed by the researcher to enhance security aspects related to authentication protocol of IoD. However, all have their short coming due to a trade off between security features and computation/ communication costs. This thesis proposes a hybrid authentication protocol for IoD that combines Lattice-based Cryptography, Chebyshev Chaotic Maps and Physical Unclonable Function (PUF) and provide resistance against advanced security attacks/ threats including quantum computing threats. The proposed scheme has been termed as LPCAP-IoD (Lattice, PUF and Chaotic Maps based Authentication Protocol - Internet of Drones). The formal and informal comprehensive security testing of proposed protocol has been undertaken by utilizing tools like ProVerif an NS-3. Furthermore, efficiency of the protocol has been determined by calculating the desired computational and communication costs and comparing them with the existing authentication schemes. The results of our formal and informal security testing demonstrate that the protocol is resistant to Quantum Computing Attacks as well as against a wide range of advance attacks and is suitable for IoD environment where security is paramount. Future research directions are also discussed to further enhance the security and scalability of our proposed protocol. Supervised by Dr. Muhammad Khurram Ehsan 2025-01-01T00:00:00Z http://hdl.handle.net/123456789/19599 Network Attacks Classification and Prediction: Exploring Advanced Strategies for Enhanced Network Security Furqan Aziz Qureshi, 01-247231-006 In the age of increasing cyber attacks, strong network security is an ongoing challenge. This research uses the UNSW-NB15 dataset to create and test advanced machine learning methods for the efficient classification and prediction of network attacks. The study begins with careful data preprocessing, which involves outlier detection and mitigation, as well as the use of Borderline SMOTE to solve class imbalance. With a primary focus on binary classification, the study performs comparative analysis of some ensemble techniques. The Random Forest classifier was found to be the best performer with an accuracy of 93% and an F1-score of 0.93, thus surpassing past benchmarks where its accuracy was stated at 90%. Furthermore, multi-class classification experiments were conducted to further confirm the method and prove its usability in different attack scenarios. These results highlight the power of sophisticated classification methods in strengthening network defense mechanisms and offer significant insights for future studies in the field of cyberattack prediction and prevention. Supervised by Dr. Moazam Ali 2025-01-01T00:00:00Z http://hdl.handle.net/123456789/19600 Multimodal Intrusion Detection Approach for IoT Devices: Enhancing Security through Anomaly Detection and Behavioral Analysis Anam Mumtaz, 01-247231-004 Cybersecurity threats have become more developed and are increasing with the growing adoption of IoT devices. So it is necessary to develop the effective Intrusion Detection Systems (IDS). This research focuses on designing an IDS that uses both machine learning and deep learning techniques to enhance detection accuracy and also mitigate cyber threats in IoT environments. Various machine and deep learning models, including Decision Tree, XGBoost, SVM, CNN, DNN, and LSTM were tested on different feature sets and sampling rates to evaluate their performance.Among the models, the Decision Tree classifier demonstrated the highest accuracy of 97%, followed closely by XGBoost at 95%. Deep learning models, particularly CNN, DNN, and LSTM, performed significantly better when trained on larger datasets, reaching an accuracy of 96% at higher sampling rates. In contrast, SVM struggled with complex attack patterns and achieved a maximum accuracy of 89%. The study also explored hybrid approaches, where a combination of traditional and deep learning models improved detection performance. The Hybrid Traditional Model attained 94% accuracy, while the Hybrid Deep Learning Model reached 92% accuracy, indicating the potential of integrated techniques for improving IoT security. Beyond model performance, this research underscores the importance of dataset size, feature selection, and sampling strategies in optimizing IDS efficiency. The results suggest that incorporating real-time anomaly detection, adversarial training, and ensemble learning could further enhance security measures against emerging threats. Future research should focus on real-world deployment, improving model adaptability, and optimizing feature engineering techniques to strengthen IoT network defenses. This research contributes to the ongoing effort to develop more intelligent and resilient security solutions for IoT ecosystems. Supervised by Dr. Moazam Ali 2025-01-01T00:00:00Z