Iraqi Journal of Information and communication technology

The RC4 stream cipher has known security weaknesses due to its weak keystream distribution and
biased key scheduling. In this article, an extended RC4X is proposed to enhance the security of the original
RC4 stream cipher by combining sophisticated mixing methods into its Key Scheduling Algorithm (KSA-X) and
Pseudo-Random Generation Algorithm (PRGA-X) process. The proposed KSA-X uses two full-shuffle operations
to eliminate predictable key-byte relationships: a first RC4-style permutation and a nonlinear mixing function that
performs bitwise shifts and XOR operations. The internal state becomes more unpredictable through an additional
permutation phase, which diffuses the state elements and increases the randomness. The PRGA-X update mechanism
performs state-value rotations to reduce linear dependencies and minimize distribution-related weaknesses in the
generated keystream. The experimental results show that RC4X produces keystreams with improved higher-order
correlation properties, enhanced resistance to key-recovery attacks, and fast operation and efficient memory usage.
The study proves the fact that RC4X is a lightweight, secure substitute of RC4, which is utilized where maximum
efficiency and security are needed and minimum resources are at hand. The experimental findings indicate that RC4X
provides security against many known security weaknesses and offers equivalent performance as the traditional
stream ciphers. The distance-equalities statistical test will assist us in determining structural flaws in algorithmic
keystreams created by similar algorithms such as RC4. This test has been done to identify the statistical bias in
RC4. The largest RC4 bias is subsequently fixed by

The Internet of Things (IoT) is rapidly expanding into critical healthcare, industrial, and commercial domains, yet its resource-constrained devices remain vulnerable to cyberattacks. IoT devices have resource constraints, making it challenging to execute standard security algorithms. To address these limitations, the National Institute of Standards and Technology (NIST) selected 10 Lightweight Cryptographic (LWC) finalist algorithms in 2023 to provide suitable confidentiality for constrained environments. This review focuses exclusively on these finalists and highlights their importance in modern IoT security. A systematic search was conducted across IEEE Xplore,
ScienceDirect, Springer, and the Cryptology ePrint Archive, using the PRISMA methodology, defined keywords, and strict inclusion/exclusion criteria. In the initial 2118 retrieved studies, 40 high-quality contributions were selected after title, abstract, and full-text screening. The selected works were categorized into four themes: performance evaluation across hardware and software platforms, cryptanalytic and security assessments, algorithmic optimization, and integration of LWC algorithms into existing systems and communication protocols. Performance analysis research indicates that TinyJambu is the most energy-efficient among the NIST block-cipher-based algorithms. Xoodyak and ASCON demonstrated the best energy efficiency among permutation-based algorithms. On the other hand, the set of Elephant, ISAP, and Grain128-AEAD was the least energy-efficient, consuming up to 10 to 25 times more energy than the most efficient set, TinyJambu. In particular, the first reported cryptanalytic break of the 7-round Xoodyak, presented in a recent article, substantially expands the threat model for the NIST LWC finalist. Some experimental reports indicate a full key-recovery attack with success rates exceeding 90%. In contrast, adapted variants of the attack have proven effective against multiple Elephant-family ciphers, illustrating the importance of updated security assessments and implementation countermeasures. Finally, this study identifies critical research gaps that require further investigation, emphasizing the importance of addressing these challenges through targeted research efforts and developing adaptive solutions in future studies.

Nowadays, data is the most valuable content in the world. Millions of data are generated every day in the form of text, images, videos, etc. Among them, images are widely used in daily communication. Due to the vulnerabilities of many data, it is difficult to transmit such images in a secure way. For this reason, a chaos-based data encryption algorithm is proposed where the image pixels are encrypted to generate a blurry image. In this paper, it is explained how to encrypt images using a 3D logistic chaotic map by generating chaotic keys where the image undergoes pixel scrambling and then XOR operation with the previously generated chaotic keys. To enhance the security and privacy of data, the proposed scheme uses different image sets to evaluate performance metrics such as Number of Pixel Change Rate (NPCR), Average Variable Intensity (UACI), correlation coefficients, and entropy in different attack scenarios. The proposed method achieved superior performance results in entropy (7.9993), key space, encryption pixel correlations, histogram contrast, UACI (35.2041%), and NPCR (99.6333%). The achieved results show that the proposed method can be used for image encryption with a high level of confidentiality.

The heavy reliance on the internet for secure data transmission require strong and efficient methods to ensure confidentiality. This study suggests an enhanced steganographic method that embedded secret messages into grayscale images using a four slice Two Bit Plane Slicing (2-BPS) technique, random key generation and XOR-based embedding. The proposed method minimize complexity by dividing the cover image into four segments, thereby improving efficiency and security, unlike the traditional bit-plane slicing methods that depend on eight planes. The method was analyzed using the metrics performance like: Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), entropy, correlation, and histogram analysis. The results show high degree of non-perceptually, low deformation and flexibility against statistical and visual steganalysis. Furthermore, execution time tests confirm the method’s computational efficiency, making it suitable for real-time applications. This approach provides a practical balance between visual quality, data security, and processing speed, with potential extensions to color and highresolution

This paper presents a flexible and adjustable antenna design that reduces interference in different practical applications. The antenna, shaped like a square with slots and integrated pin diodes, enables frequency adjustment by connecting or disconnecting components. Made of certain substrate material, the antenna operates at five frequency bands commonly used in 5G sub-6 GHz applications. Along with improving the connection with the Metamaterial (MTM) structure by using four PIN diodes without altering the radiation pattern, the new design replaces the via with a fractal technique, which cuts down on energy losses and boosts performance while also lowering costs. Simulation results demonstrate excellent impedance matching, multiple adjustable bands, and significant gain(>10 dBi). The antenna exhibits radiation patterns and achieves an efficiency greater than 75%. Analysis under various conditions shows the antenna’s performance on curved surfaces, making it suitable for adaptable electronic systems. A comparison with prior studies highlights the antenna’s potential within the specified frequency ranges.

This study addresses beam squint mitigation in millimeter-Wave (mmWave) systems using a 1024- element Reconfigurable Intelligent Surface (RIS) optimized via gradient descent. The proposed approach achieves ±0.2° squint correction and over 90% accuracy within 200 iterations, with gain variation maintained below 0.5 dB. A supervised Machine Learning (ML) model, trained on simulation data, demonstrates an 83% reduction in training error and a 60% drop in validation error over 1000 epochs, converging by epoch 700. The integration of early stopping and L2 regularization is suggested to further reduce generalization error. These results indicate that RIS, combined with ML optimization, offers a scalable and effective solution for wideband mmWave systems, paving the way for real-time beamforming in next-generation wireless networks.

Modern transportation systems are severely hampered by urban traffic congestion, which causes delays and fuel consumption. Proactive control techniques and intelligent traffic management depend on accurate congestion prediction. In order to predict congestion across urban edge networks, current study presents a deep learning-based framework that combines an attention mechanism with a bidirectional Long Short-Term Memory (LSTM) network with custom learnable attention layer, flowed by focal loss for addressing the data imbalance. A numerous dataset was generated by using SUMO, therefore over 2 million sequence was generated, including 12 spatiotemporal features that were extracted from the dataset. A large scale map was used and the prediction was based on edge level. The model can efficiently learn temporal dependencies and spatial patterns thanks to our preprocessing pipeline, which consists of temporal windowing, edge ID encoding, and cyclical time transformations. Trained with a 30-step sliding window, the model achieved low error metrics (MAE: 0.0744, RMSE: 0.2728), an F1-score of 0.90, and a classification accuracy of 92.56%. Our architecture performs better at detecting congestion events than recent state-of-the-art models. Thus the potential for scalable implementation in urban traffic forecasting systems of deep spatiotemporal learning models trained on realistic but synthetic simulation data.