A novel method for Network Intrusion Detection System (NIDS) has been proposed, based on the concept of how DNA sequence detects disease as both domains have similar conceptual method of detection. Three important steps have been proposed to apply DNA sequence for NIDS: convert the network traffic data into a form of DNA sequence using Cryptography encoding method; discover patterns of Short Tandem Repeats (STR) sequence for each network traffic attack using Teiresias algorithm; and conduct classification process depends upon STR sequence based on Horspool algorithm. 10% KDD Cup 1999 data set is used for training phase. Correct KDD Cup 1999 data set is used for testing phase to evaluate the proposed method. The current experiment results sh

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

An Auto Crop method is used for detection and extraction signature, logo and stamp from the document image. This method improves the performance of security system based on signature, logo and stamp images as well as it is extracted images from the original document image and keeping the content information of cropped images. An Auto Crop method reduces the time cost associated with document contents recognition. This method consists of preprocessing, feature extraction and classification. The HSL color space is used to extract color features from cropped image. The k-Nearest Neighbors (KNN) classifier is used for classification. 

HM Al-Dabbas, RA Azeez, AE Ali, IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2023

This study proposed a biometric-based digital signature scheme proposed for facial recognition. The scheme is designed and built to verify the person’s identity during a registration process and retrieve their public and private keys stored in the database. The RSA algorithm has been used as asymmetric encryption method to encrypt hashes generated for digital documents. It uses the hash function (SHA-256) to generate digital signatures. In this study, local binary patterns histograms (LBPH) were used for facial recognition. The facial recognition method was evaluated on ORL faces retrieved from the database of Cambridge University. From the analysis, the LBPH algorithm achieved 97.5% accuracy; the real-time testing was done on thirty subj

Natural gas and oil are one of the mainstays of the global economy. However, many issues surround the pipelines that transport these resources, including aging infrastructure, environmental impacts, and vulnerability to sabotage operations. Such issues can result in leakages in these pipelines, requiring significant effort to detect and pinpoint their locations. The objective of this project is to develop and implement a method for detecting oil spills caused by leaking oil pipelines using aerial images captured by a drone equipped with a Raspberry Pi 4. Using the message queuing telemetry transport Internet of Things (MQTT IoT) protocol, the acquired images and the global positioning system (GPS) coordinates of the images' acquisition are

A nonlinear filter for smoothing color and gray images
corrupted by Gaussian noise is presented in this paper. The proposed
filter designed to reduce the noise in the R,G, and B bands of the
color images and preserving the edges. This filter applied in order to
prepare images for further processing such as edge detection and
image segmentation.
The results of computer simulations show that the proposed
filter gave satisfactory results when compared with the results of
conventional filters such as Gaussian low pass filter and median filter
by using Cross Correlation Coefficient (ccc) criteria.

In this paper an accurate Indian handwritten digits recognition system is
proposed. The system used three proposed method for extracting the most effecting
features to represent the characteristic of each digit. Discrete Wavelet Transform
(DWT) at level one and Fast Cosine Transform (FCT) is used for features extraction
from the thinned image. Besides that, the system used a standard database which is
ADBase database for evaluation. The extracted features were classified with KNearest
Neighbor (KNN) classifier based on cityblock distance function and the
experimental results show that the proposed system achieved 98.2% recognition
rate.