فاطمة راضي حمادي حمادي

Abstract Distributed Denial of Service (DDoS) attacks have recently emerged as one of the most destructive threats to network systems. This paper aims to develop a technique that efficiently identifies DDoS attacks in networked systems by leveraging improved clustering techniques and machine learning algorithms. This methodology employs a Modified Fuzzy C-Means (MFCM) clustering algorithm to partition the available DDoS attack dataset and integrate a classification algorithm to accurately detect attacks and classify data based on specific network characteristics derived from the transformed data packets. The clustering algorithm predominantly relies on distance measurements derived from fuzzy coefficients, significantly limiting its ability to identify and classify emerging attack scenarios. The current study introduces the integration of the MFCM clustering algorithm with sophisticated classification techniques to enhance accuracy and minimize errors. The efficacy of the modified clustering algorithm was evaluated using the entropy criterion, and a value of 0.99 was attained, demonstrating superior performance relative to traditional algorithms. The training algorithm was rigorously evaluated utilizing established performance metrics, such as accuracy, detection rate, and false positive rate. The results indicate that the accuracy improved consistently across all classification algorithms applied, contributing to an enhanced attack detection rate. © (2025), (Engineering and Technology Publishing). All rights reserved.

data and infrastructure as digital network usage rises rapidly and reliance on technology spreads over all spheres. From distributed denial of service (DDoS) attacks to brute force attacks to long-term targeted attacks (APTs), these attacks call for creating intelligent and strong systems to identify these hazards properly. A hybrid model using LSTM and CNN models with an attention mechanism to improve the performance of detecting cyberattacks has been introduced. CNN model aims to extract spatial features from network data, and LSTM is used to check temporal patterns associated with attack activities. Attention mechanism has a significant role in the proposed model in classifying difficult attacks by improving the model's accuracy, which enhances concentration on the key aspects of the input data. The proposed hybrid model (CNN-LSTM-Attention

Abstract: In this research, we propose two new image steganography techniques focusing on increasing image-embedding capacity. The methods will encrypt and hide secret information in the edge area. We utilized two hybrid methods for the edge detection of the images. The first method combines the Laplacian of Gaussian (LoG) with the wavelet transform algorithm and the second method mixes the LOG and Canny. The Combining was performed using addWeighted. The text message will be encrypted using the GIFT cipher method for further security and low computation. For the effectiveness evaluation of the proposed method, various evaluation metrics were used such as embedding capacity, PSNR, MSE, and SSIM. The obtained results indicate that the proposed method has a greater embedding capacity in comparison with other methods, while still maintaining high levels of imperceptibility in the cover image.

With the development of electronic communications RADG devices it has become increasingly important to protect data from violation, especially, large data. The main way to achieve this protection is using cryptography algorithms. Cryptography is the science of data encryption with the goal of reducing the availability of information to an analyst’s cipher. In this study, we propose a novel design, i.e., block cipher keys by attempting to develop one such algorithm, i.e., develop the RADG method to the BRADG block cipher key while keeping the RADG attributes. BRADG (Block cipher Reaction Automata Direct Graph) is used in protecting wireless networks. BRADG processes data blocks of B bits with key length of B bits and gives cipher text of size B bits where B is 64, 128, 512, ... , bits. BRADG is based on the unbalanced feistel structure in both encryption and decryption. In comparison to the previous design, ciphering of a new design is a faster and more efficient way to encrypt large data.

This paper proposed a new secret key random block cipher as a candidate for Reaction Automata Direct Graph (RADG method). A new secret key consists of a 64-bit block size and a 128-bit key length. A new algorithm based in design on Generalized Unbalanced Feistel Network and uses a new S-boxes generated from a DES S-boxes, a bijective F function, bitwise operations and a carefully key schedule design. The new algorithm uses in protected wireless network and comparing with the old system (without encryption key), the proposed designs are designed for security purposes as characterized highly efficient encrypting large data. As for the analysis for the proposed block cipher design proves terms of privacy, data integrity, authentication and non-repudiation.