arXiv subjects

Muhammad Shahbaz Khan

Publications and source records attributed to Muhammad Shahbaz Khan.

16 recordsLinked to original sources

arXiv2026

Efficient IoT Intrusion Detection with an Improved Attention-Based CNN-BiLSTM Architecture

The ever-increasing security vulnerabilities in the Internet-of-Things (IoT) systems require improved threat detection approaches. This paper presents a compact and efficient approach to detect botnet attacks by employing an integrated approach that consists of traffic pattern analysis, temporal support learning, and focused feature extraction. The proposed attention-based model benefits from a hybrid CNN-BiLSTM architecture and achieves 99% classification accuracy in detecting botnet attacks utilizing the N-BaIoT dataset, while maintaining high precision and recall across various scenarios. The proposed model's performance is further validated by key parameters, such as Mathews Correlation Coefficient and Cohen's kappa Correlation Coefficient. The close-to-ideal results for these parameters demonstrate the proposed model's ability to detect botnet attacks accurately and efficiently in practical settings and on unseen data. The proposed model proved to be a powerful defense mechanism for IoT networks to face emerging security challenges.

cs.CR↗

arXiv2025

Quantum Agents

This paper explores the intersection of quantum computing and agentic AI by examining how quantum technologies can enhance the capabilities of autonomous agents, and, conversely, how agentic AI can support the advancement of quantum systems. We analyze both directions of this synergy and present conceptual and technical foundations for future quantum-agentic platforms. Our work introduces a formal definition of quantum agents and outlines potential architectures that integrate quantum computing with agent-based systems. As a proof-of-concept, we develop and evaluate three quantum agent prototypes that demonstrate the feasibility of our proposed framework. Furthermore, we discuss use cases from both perspectives, including quantum-enhanced decision-making, quantum planning and optimization, and AI-driven orchestration of quantum workflows. By bridging these fields, we aim to chart a path toward scalable, intelligent, and adaptive quantum-agentic ecosystems.

quant-ph↗

arXiv2025

A Novel Feature-Aware Chaotic Image Encryption Scheme For Data Security and Privacy in IoT and Edge Networks

The security of image data in the Internet of Things (IoT) and edge networks is crucial due to the increasing deployment of intelligent systems for real-time decision-making. Traditional encryption algorithms such as AES and RSA are computationally expensive for resource-constrained IoT devices and ineffective for large-volume image data, leading to inefficiencies in privacy-preserving distributed learning applications. To address these concerns, this paper proposes a novel Feature-Aware Chaotic Image Encryption scheme that integrates Feature-Aware Pixel Segmentation (FAPS) with Chaotic Chain Permutation and Confusion mechanisms to enhance security while maintaining efficiency. The proposed scheme consists of three stages: (1) FAPS, which extracts and reorganizes pixels based on high and low edge intensity features for correlation disruption; (2) Chaotic Chain Permutation, which employs a logistic chaotic map with SHA-256-based dynamically updated keys for block-wise permutation; and (3) Chaotic chain Confusion, which utilises dynamically generated chaotic seed matrices for bitwise XOR operations. Extensive security and performance evaluations demonstrate that the proposed scheme significantly reduces pixel correlation -- almost zero, achieves high entropy values close to 8, and resists differential cryptographic attacks. The optimum design of the proposed scheme makes it suitable for real-time deployment in resource-constrained environments.

cs.CR↗

arXiv2025

A Chaotic Image Encryption Scheme Using Novel Geometric Block Permutation and Dynamic Substitution

In this digital era, ensuring the security of digital data during transmission and storage is crucial. Digital data, particularly image data, needs to be protected against unauthorized access. To address this, this paper presents a novel image encryption scheme based on a confusion diffusion architecture. The diffusion module introduces a novel geometric block permutation technique, which effectively scrambles the pixels based on geometric shape extraction of pixels. The image is converted into four blocks, and pixels are extracted from these blocks using L-shape, U-shape, square-shape, and inverted U-shape patterns for each block, respectively. This robust extraction and permutation effectively disrupts the correlation within the image. Furthermore, the confusion module utilises bit-XOR and dynamic substitution techniques. For the bit-XOR operation, 2D Henon map has been utilised to generate a chaotic seed matrix, which is bit-XORed with the scrambled image. The resultant image then undergoes the dynamic substitution process to complete confusion phase. A statistical security analysis demonstrates the superior security of the proposed scheme, with being high uncertainty and unpredictability, achieving an entropy of 7.9974 and a correlation coefficient of 0.0014. These results validate the proposed scheme's effectiveness in securing digital images.

cs.CR↗

arXiv2025

X-Cross: Image Encryption Featuring Novel Dual-Layer Block Permutation and Dynamic Substitution Techniques

In this digital age, ensuring the security of digital data, especially the image data is critically important. Image encryption plays an important role in securing the online transmission/storage of images from unauthorized access. In this regard, this paper presents a novel diffusion-confusion-based image encryption algorithm named as X-CROSS. The diffusion phase involves a dual-layer block permutation. It involves a bit-level permutation termed Inter-Bit Transference (IBT) using a Bit-Extraction key, and pixel permutation with a unique X-crosspermutation algorithm to effectively scramble the pixels within an image. The proposed algorithm utilizes a resilient 2D chaotic map with non-linear dynamical behavior, assisting in generating complex Extraction Keys. After the permutation phase, the confusion phase proceeds with a dynamic substitution technique on the permuted images, establishing the final encryption layer. This combination of novel permutation and confusion results in the removal of the image's inherent patterns and increases its resistance to cyber-attacks. The close to ideal statistical security results for information entropy, correlation, homogeneity, contrast, and energy validate the proposed scheme's effectiveness in hiding the information within the image.

cs.CR↗

arXiv2025

Optimizing Fire Safety: Reducing False Alarms Using Advanced Machine Learning Techniques

Fire safety practices are important to reduce the extent of destruction caused by fire. While smoke alarms help save lives, firefighters struggle with the increasing number of false alarms. This paper presents a precise and efficient Weighted ensemble model for decreasing false alarms. It estimates the density, computes weights according to the high and low-density regions, forwards the high region weights to KNN and low region weights to XGBoost and combines the predictions. The proposed model is effective at reducing response time, increasing fire safety, and minimizing the damage that fires cause. A specifically designed dataset for smoke detection is utilized to test the proposed model. In addition, a variety of ML models, such as Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Nai:ve Bayes (NB), K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (ADAB), have also been utilized. To maximize the use of the smoke detection dataset, all the algorithms utilize the SMOTE re-sampling technique. After evaluating the assessment criteria, this paper presents a concise summary of the comprehensive findings obtained by comparing the outcomes of all models.

cs.LG↗

arXiv2025

Image Encryption Using DNA Encoding, Snake Permutation and Chaotic Substitution Techniques

Securing image data in IoT networks and other insecure information channels is a matter of critical concern. This paper presents a new image encryption scheme using DNA encoding, snake permutation and chaotic substitution techniques that ensures robust security of the image data with reduced computational overhead. The DNA encoding and snake permutation modules ensure effective scrambling of the pixels and result in efficient diffusion in the plaintext image. For the confusion part, the chaotic substitution technique is implemented, which substitutes the pixel values chosen randomly from 3 S-boxes. Extensive security analysis validate the efficacy of the image encryption algorithm proposed in this paper and results demonstrate that the encrypted images have an ideal information entropy of 7.9895 and an almost zero correlation coefficient of -0.001660. These results indicate a high degree of randomness and no correlation in the encrypted image.

cs.CR↗

arXiv2025

A Hybrid Neural Network with Smart Skip Connections for High-Precision, Low-Latency EMG-Based Hand Gesture Recognition

Electromyography (EMG) is extensively used in key biomedical areas, such as prosthetics, and assistive and interactive technologies. This paper presents a new hybrid neural network named ConSGruNet for precise and efficient hand gesture recognition. The proposed model comprises convolutional neural networks with smart skip connections in conjunction with a Gated Recurrent Unit (GRU). The proposed model is trained on the complete Ninapro DB1 dataset. The proposed model boasts an accuracy of 99.7\% in classifying 53 classes in just 25 milliseconds. In addition to being fast, the proposed model is lightweight with just 3,946 KB in size. Moreover, the proposed model has also been evaluated for the reliability parameters, i.e., Cohen's kappa coefficient, Matthew's correlation coefficient, and confidence intervals. The close to ideal results of these parameters validate the models performance on unseen data.

cs.CR↗

arXiv2025

Performance Evaluation of Threshold Signing Schemes in Cryptography

Threshold Signature Scheme (TSS) protocols have gained significant attention over the past ten years due to their widespread adoption in cryptocurrencies. The adoption is mainly boosted by Gennaro and Goldfedder's TSS protocol. Since then, various TSS protocols have been introduced with different features, such as security and performance, etc. Large organizations are using TSS protocols to protect many digital assets, such as cryptocurrency. However, the adoption of these TSS protocols requires an understanding of state-of-the-art research in threshold signing. This study describes the holistic view of TSS protocols, evaluates cutting-edge TSS protocols, highlights their characteristics, and compares them in terms of security and performance. The evaluation of these TSS protocols will help the researchers address real-world problems by considering the relevant merits of different TSS protocols.

cs.CR↗

arXiv2024

A Single Channel-Based Neonatal Sleep-Wake Classification using Hjorth Parameters and Improved Gradient Boosting

Sleep plays a crucial role in neonatal development. Monitoring the sleep patterns in neonates in a Neonatal Intensive Care Unit (NICU) is imperative for understanding the maturation process. While polysomnography (PSG) is considered the best practice for sleep classification, its expense and reliance on human annotation pose challenges. Existing research often relies on multichannel EEG signals; however, concerns arise regarding the vulnerability of neonates and the potential impact on their sleep quality. This paper introduces a novel approach to neonatal sleep stage classification using a single-channel gradient boosting algorithm with Hjorth features. The gradient boosting parameters are fine-tuned using random search cross-validation (randomsearchCV), achieving an accuracy of 82.35% for neonatal sleep-wake classification. Validation is conducted through 5-fold cross-validation. The proposed algorithm not only enhances existing neonatal sleep algorithms but also opens avenues for broader applications.

cs.LG↗

arXiv2024

RNA-TransCrypt: Image Encryption Using Chaotic RNA Encoding, Novel Transformative Substitution, and Tailored Cryptographic Operations

Given the security concerns of Internet of Things (IoT) networks and limited computational resources of IoT devices, this paper presents RNA-TransCrypt, a novel image encryption scheme that is not only highly secure but also efficient and lightweight. RNA-TransCrypt integrates the biocryptographic properties of RNA encoding with the non-linearity and unpredictability of chaos theory. This scheme introduces three novel contributions: 1) the two-base RNA encoding method, which transforms the image into RNA strands-like sequence, ensuring efficient scrambling; 2) the transformative substitution technique, which transforms the s-box values before replacing the pixel values, and is responsible for making the scheme lightweight; and 3) three mathematical cryptographic operations designed especially for image encryption that ensure the effective transformation of the s-box values, resulting in a new outcome even for the same input values. These modules are key-dependent, utilizing chaotic keys generated by the De Jong Fractal Map and the Van der Pol Oscillator. Extensive security analysis, including histogram analysis, correlation analysis, and the results of the statistical security parameters obtained from the Gray-Level Co-occurrence Matrix (GLCM) validate the efficacy of the proposed scheme in encrypting input images with close-to-ideal results of 7.997 entropy and 0.0006 correlation.

cs.CR↗

arXiv2023

PermutEx: Feature-Extraction-Based Permutation -- A New Diffusion Scheme for Image Encryption Algorithms

Traditional permutation schemes mostly focus on random scrambling of pixels, often neglecting the intrinsic image information that could enhance diffusion in image encryption algorithms. This paper introduces PermutEx, a feature-extraction-based permutation method that utilizes inherent image features to scramble pixels effectively. Unlike random permutation schemes, PermutEx extracts the spatial frequency and local contrast features of the image and ranks each pixel based on this information, identifying which pixels are more important or information-rich based on texture and edge information. In addition, a unique permutation key is generated using the Logistic-Sine Map based on chaotic behavior. The ranked pixels are permuted in conjunction with this unique key, effectively permuting the original image into a scrambled version. Experimental results indicate that the proposed method effectively disrupts the correlation in information-rich areas within the image resulting in a correlation value of 0.000062. The effective scrambling of pixels, resulting in nearly zero correlation, makes this method suitable to be used as diffusion in image encryption algorithms.

cs.CR↗

arXiv2023

Noise-Crypt: Image Encryption with Non-linear Noise, Hybrid Chaotic Maps, and Hashing

To secure the digital images over insecure transmission channels, a new image encryption algorithm Noise-Crypt is proposed in this paper. Noise-Crypt integrates non-linear random noise, hybrid chaotic maps, and SHA-256 hashing algorithm. The utilized hybrid chaotic maps are the logistic-tent and the logistic-sine-cosine map. The hybrid chaotic maps enhance the pseudorandom sequence generation and selection of substitution boxes, while the logistic-sine-cosine map induces non-linearity in the algorithm through random noise. This deliberate inclusion of noise contributes to increased resistance against cryptanalysis. The proposed scheme has been evaluated for several security parameters, such as differential attacks, entropy, correlation, etc. Extensive evaluation demonstrates the efficacy of the proposed scheme, with almost ideal values of entropy of 7.99 and correlation of -0.0040. Results of the security analysis validate the potency of the proposed scheme in achieving robust image encryption.

cs.CR↗

arXiv2023

CellSecure: Securing Image Data in Industrial Internet-of-Things via Cellular Automata and Chaos-Based Encryption

In the era of Industrial IoT (IIoT) and Industry 4.0, ensuring secure data transmission has become a critical concern. Among other data types, images are widely transmitted and utilized across various IIoT applications, ranging from sensor-generated visual data and real-time remote monitoring to quality control in production lines. The encryption of these images is essential for maintaining operational integrity, data confidentiality, and seamless integration with analytics platforms. This paper addresses these critical concerns by proposing a robust image encryption algorithm tailored for IIoT and Cyber-Physical Systems (CPS). The algorithm combines Rule-30 cellular automata with chaotic scrambling and substitution. The Rule 30 cellular automata serves as an efficient mechanism for generating pseudo-random sequences that enable fast encryption and decryption cycles suitable for real-time sensor data in industrial settings. Most importantly, it induces non-linearity in the encryption algorithm. Furthermore, to increase the chaotic range and keyspace of the algorithm, which is vital for security in distributed industrial networks, a hybrid chaotic map, i.e., logistic-sine map is utilized. Extensive security analysis has been carried out to validate the efficacy of the proposed algorithm. Results indicate that our algorithm achieves close-to-ideal values, with an entropy of 7.99 and a correlation of 0.002. This enhances the algorithm's resilience against potential cyber-attacks in the industrial domain.

cs.CR↗

arXiv2023

SRSS: A New Chaos-Based Single-Round Single S-Box Image Encryption Scheme for Highly Auto-Correlated Data

With the advent of digital communication, securing digital images during transmission and storage has become a critical concern. The traditional s-box substitution methods often fail to effectively conceal the information within highly auto-correlated regions of an image. This paper addresses the security issues presented by three prevalent S-box substitution methods, i.e., single S-box, multiple S-boxes, and multiple rounds with multiple S-boxes, especially when handling images with highly auto-correlated pixels. To resolve the addressed security issues, this paper proposes a new scheme SRSS-the Single Round Single S-Box encryption scheme. SRSS uses a single S-box for substitution in just one round to break the pixel correlations and encrypt the plaintext image effectively. Additionally, this paper introduces a new Chaos-based Random Operation Selection System-CROSS, which nullifies the requirement for multiple S-boxes, thus reducing the encryption scheme's complexity. By randomly selecting the operation to be performed on each pixel, driven by a chaotic sequence, the proposed scheme effectively scrambles even high auto-correlation areas. When compared to the substitution methods mentioned above, the proposed encryption scheme exhibited exceptionally well in just a single round with a single S-box. The close-to-ideal statistical security analysis results, i.e., an entropy of 7.89 and a correlation coefficient of 0.007, validate the effectiveness of the proposed scheme. This research offers an innovative path forward for securing images in applications requiring low computational complexity and fast encryption and decryption speeds.

cs.CR↗

arXiv2022

A DNA Based Colour Image Encryption Scheme Using A Convolutional Autoencoder

With the advancement in technology, digital images can easily be transmitted and stored over the Internet. Encryption is used to avoid illegal interception of digital images. Encrypting large-sized colour images in their original dimension generally results in low encryption/decryption speed along with exerting a burden on the limited bandwidth of the transmission channel. To address the aforementioned issues, a new encryption scheme for colour images employing convolutional autoencoder, DNA and chaos is presented in this paper. The proposed scheme has two main modules, the dimensionality conversion module using the proposed convolutional autoencoder, and the encryption/decryption module using DNA and chaos. The dimension of the input colour image is first reduced from N $\times$ M $\times$ 3 to P $\times$ Q gray-scale image using the encoder. Encryption and decryption are then performed in the reduced dimension space. The decrypted gray-scale image is upsampled to obtain the original colour image having dimension N $\times$ M $\times$ 3. The training and validation accuracy of the proposed autoencoder is 97% and 95%, respectively. Once the autoencoder is trained, it can be used to reduce and subsequently increase the dimension of any arbitrary input colour image. The efficacy of the designed autoencoder has been demonstrated by the successful reconstruction of the compressed image into the original colour image with negligible perceptual distortion. The second major contribution presented in this paper is an image encryption scheme using DNA along with multiple chaotic sequences and substitution boxes. The security of the proposed image encryption algorithm has been gauged using several evaluation parameters, such as histogram of the cipher image, entropy, NPCR, UACI, key sensitivity, contrast, etc. encryption.

cs.CR↗