Intrusion detection systems detect attacks inside computers and networks, where the detection of the attacks must be in fast time and high rate. Various methods proposed achieved high detection rate, this was done either by improving the algorithm or hybridizing with another algorithm. However, they are suffering from the time, especially after the improvement of the algorithm and dealing with large traffic data. On the other hand, past researches have been successfully applied to the DNA sequences detection approaches for intrusion detection system; the achieved detection rate results were very low, on other hand, the processing time was fast. Also, feature selection used to reduce the computation and complexity lead to speed up the system. A new features selection method is proposed based on DNA encoding and on DNA keys positions. The current system has three phases, the first phase, is called pre-processing phase, which is used to extract the keys and their positions, the second phase is training phase; the main goal of this phase is to select features based on the key positions that gained from pre-processing phase, and the third phase is the testing phase, which classified the network traffic records as either normal or attack by using specific features. The performance is calculated based on the detection rate, false alarm rate, accuracy, and also on the time that include both encoding time and matching time. All these results are based on using two or three keys, and it is evaluated by using two datasets, namely, KDD Cup 99, and NSL-KDD. The achieved detection rate, false alarm rate, accuracy, encoding time, and matching time for all corrected KDD Cup records (311,029 records) by using two and three keys are equal to 96.97, 33.67, 91%, 325, 13 s, and 92.74, 7.41, 92.71%, 325 and 20 s, respectively. The results for detection rate, false alarm rate, accuracy, encoding time, and matching time for all NSL-KDD records (22,544 records) by using two and three keys are equal to 89.34, 28.94, 81.46%, 20, 1 s and 82.93, 11.40, 85.37%, 20 and 1 s, respectively. The proposed system is evaluated and compared with previous systems and these comparisons are done based on encoding time and matching time. The outcomes showed that the detection results of the present system are faster than the previous ones.
In this article, Convolution Neural Network (CNN) is used to detect damage and no damage images form satellite imagery using different classifiers. These classifiers are well-known models that are used with CNN to detect and classify images using a specific dataset. The dataset used belongs to the Huston hurricane that caused several damages in the nearby areas. In addition, a transfer learning property is used to store the knowledge (weights) and reuse it in the next task. Moreover, each applied classifier is used to detect the images from the dataset after it is split into training, testing and validation. Keras library is used to apply the CNN algorithm with each selected classifier to detect the images. Furthermore, the performa
... Show MoreAs an important resource, entanglement light source has been used in developing quantum information technologies, such as quantum key distribution(QKD). There are few experiments implementing entanglement-based deterministic QKD protocols since the security of existing protocols may be compromised in lossy channels. In this work, we report on a loss-tolerant deterministic QKD experiment which follows a modified “Ping-Pong”(PP) protocol. The experiment results demonstrate for the first time that a secure deterministic QKD session can be fulfilled in a channel with an optical loss of 9 dB, based on a telecom-band entangled photon source. This exhibits a conceivable prospect of ultilizing entanglement light source in real-life fiber-based
... Show MoreCalcifying epithelial odontogenic tumour (CEOT) is a benign odontogenic neoplasm of epithelial origin that secretes an amyloid‐like protein tending towards calcification. This study aims to describe a case series from Iraq of one of the rarest odontogenic tumours.
Clinical and histopathological analysis of Calcifying epithelial odontogenic tumour cases that are archived at the oral pathology laboratory of the college of dentistry (Baghdad University) from 2000 to 2019.
Six cases of CEOT were regi
This case series aims to evaluate patients affected with post COVID‐19 mucormycosis from clinical presentation to surgical and pharmacological treatment to improve the disease prognosis.
This case series was conducted at a specialized surgery hospital in Baghdad Medical City for over 10 months. Fifteen cases who had mild to severe COVID‐19 infections followed by symptoms similar to aggressive periodontitis, such as mobility and bone resorption around the multiple maxillary teeth, were included in this case series.
The dramatic personality represents the most important nerve in the cinematographic discourse due to what it represents of a permanent presence in various types of TV and cinematic stories. That is what makes the cinematographic discourse always seek to introduce the human personality in multiple methods. The psychopathic personality is a type of the personalities that got distinguished presence in the TV and cinematic productions. This is what drew the researcher's attention for the topic of the research for which she used title: structural features of the psychopathic personality in the cinematographic discourse.
The theoretical framework included the research problem which is determined by the following question: what are the stru
The features of strategic thought transformation in Russia based on attempt confirm the Russian influenced on Region of Eastern Europe through series of common defense treaties and mutual security between USSR and another states this treaties emphasis size on direct Russian supervision on these states in addition, Russia in trusted to formation communism regimes in its vital space, Russia inherited the status of former USSR in political and economic levels.
Shortly we can see that Russia witnessed large Transformation in political strategic thought as a result of change in internal strategic environment and change of nature understanding which influenced on Russia. This
... Show MoreImage texture is an important part of many types of images, for example medical images. Texture Analysis is the technique that uses measurable features to categorize complex textures. The main goal is to extract discriminative features that are used in different pattern recognition applications and texture categorization. This paper investigates the extraction of most discriminative features for different texture images from the “Colored Brodatz” dataset using two types of image contrast measures, as well as using the statistical moments on five bands (red, green, blue, grey, and black). The Euclidean distance measure is used in the matching step to check the similarity degree. The proposed method was tested on 112 classes o
... Show MoreThe distribution of the intensity of the comet Ison C/2013 is studied by taking its histogram. This distribution reveals four distinct regions that related to the background, tail, coma and nucleus. One dimensional temperature distribution fitting is achieved by using two mathematical equations that related to the coordinate of the center of the comet. The quiver plot of the gradient of the comet shows very clearly that arrows headed towards the maximum intensity of the comet.
Within the framework of big data, energy issues are highly significant. Despite the significance of energy, theoretical studies focusing primarily on the issue of energy within big data analytics in relation to computational intelligent algorithms are scarce. The purpose of this study is to explore the theoretical aspects of energy issues in big data analytics in relation to computational intelligent algorithms since this is critical in exploring the emperica aspects of big data. In this chapter, we present a theoretical study of energy issues related to applications of computational intelligent algorithms in big data analytics. This work highlights that big data analytics using computational intelligent algorithms generates a very high amo
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