Making the data secure is more and more concerned in the communication era. This research is an attempt to make a more secured information message by using both encryption and steganography. The encryption phase is done with dynamic DNA complementary rules while DNA addition rules are done with secret key where both are based on the canny edge detection point of the cover image. The hiding phase is done after dividing the cover image into 8 blocks, the blocks that are used for hiding selected in reverse order exception the edge points. The experiments result shows that the method is reliable with high value in PSNR

In any security system, we need a high level of security, to maintain the secrecy of important data. Steganography is one of the security systems that are hiding secret information within a certain cover (video, image, sound, text), so that the adversary does not suspect the existence of such confidential information. In our proposed work will hide secret messages (Arabic or English) text in the Arabic cover text, we employed the RNA as a tool for encoding the secret information and used non-printed characters to hide these codes. Each character (English or Arabic) is represented by using only six bits based on secret tables this operation has provided a good compression since each Arabic character needs 16 bits and each English characte

Data hiding (Steganography) is a method used for data security purpose and to protect the data during its transmission. Steganography is used to hide the communication between two parties by embedding a secret message inside another cover (audio, text, image or video). In this paper a new text Steganography method is proposed that based on a parser and the ASCII of non-printed characters to hide the secret information in the English cover text after coding the secret message and compression it using modified Run Length Encoding method (RLE). The proposed method achieved a high capacity ratio for Steganography (five times more than the cover text length) when compared with other methods, and provides a 1.0 transparency by depending on som

In recent years, the world witnessed a rapid growth in attacks on the internet which resulted in deficiencies in networks performances. The growth was in both quantity and versatility of the attacks. To cope with this, new detection techniques are required especially the ones that use Artificial Intelligence techniques such as machine learning based intrusion detection and prevention systems. Many machine learning models are used to deal with intrusion detection and each has its own pros and cons and this is where this paper falls in, performance analysis of different Machine Learning Models for Intrusion Detection Systems based on supervised machine learning algorithms. Using Python Scikit-Learn library KNN, Support Ve

Audio classification is the process to classify different audio types according to contents. It is implemented in a large variety of real world problems, all classification applications allowed the target subjects to be viewed as a specific type of audio and hence, there is a variety in the audio types and every type has to be treatedcarefully according to its significant properties.Feature extraction is an important process for audio classification. This workintroduces several sets of features according to the type, two types of audio (datasets) were studied. Two different features sets are proposed: (i) firstorder gradient feature vector, and (ii) Local roughness feature vector, the experimentsshowed that the results are competitive to

User confidentiality protection is concerning a topic in control and monitoring spaces. In image, user's faces security in concerning with compound information, abused situations, participation on global transmission media and real-world experiences are extremely significant. For minifying the counting needs for vast size of image info and for minifying the size of time needful for the image to be address computationally. consequently, partial encryption user-face is picked. This study focuses on a large technique that is designed to encrypt the user's face slightly. Primarily, dlib is utilizing for user-face detection. Susan is one of the top edge detectors with valuable localization characteristics marked edges, is used to extract

Mammography is at present one of the available method for early detection of masses or abnormalities which is related to breast cancer. The most common abnormalities that may indicate breast cancer are masses and calcifications. The challenge lies in early and accurate detection to overcome the development of breast cancer that affects more and more women throughout the world. Breast cancer is diagnosed at advanced stages with the help of the digital mammogram images. Masses appear in a mammogram as fine, granular clusters, which are often difficult to identify in a raw mammogram. The incidence of breast cancer in women has increased significantly in recent years.
This paper proposes a computer aided diagnostic system for the extracti

Several Intrusion Detection Systems (IDS) have been proposed in the current decade. Most datasets which associate with intrusion detection dataset suffer from an imbalance class problem. This problem limits the performance of classifier for minority classes. This paper has presented a novel class imbalance processing technology for large scale multiclass dataset, referred to as BMCD. Our algorithm is based on adapting the Synthetic Minority Over-Sampling Technique (SMOTE) with multiclass dataset to improve the detection rate of minority classes while ensuring efficiency. In this work we have been combined five individual CICIDS2017 dataset to create one multiclass dataset which contains several types of attacks. To prove the eff

Heart disease is a significant and impactful health condition that ranks as the leading cause of death in many countries. In order to aid physicians in diagnosing cardiovascular diseases, clinical datasets are available for reference. However, with the rise of big data and medical datasets, it has become increasingly challenging for medical practitioners to accurately predict heart disease due to the abundance of unrelated and redundant features that hinder computational complexity and accuracy. As such, this study aims to identify the most discriminative features within high-dimensional datasets while minimizing complexity and improving accuracy through an Extra Tree feature selection based technique. The work study assesses the efficac

In the present work, an image compression method have been modified by combining The Absolute Moment Block Truncation Coding algorithm (AMBTC) with a VQ-based image coding. At the beginning, the AMBTC algorithm based on Weber's law condition have been used to distinguish low and high detail blocks in the original image. The coder will transmit only mean of low detailed block (i.e. uniform blocks like background) on the channel instate of transmit the two reconstruction mean values and bit map for this block. While the high detail block is coded by the proposed fast encoding algorithm for vector quantized method based on the Triangular Inequality Theorem (TIE), then the coder will transmit the two reconstruction mean values (i.e. H&L)