Intrusion detection systems (IDS) are useful tools that help security administrators in the developing task to secure the network and alert in any possible harmful event. IDS can be classified either as misuse or anomaly, depending on the detection methodology. Where Misuse IDS can recognize the known attack based on their signatures, the main disadvantage of these systems is that they cannot detect new attacks. At the same time, the anomaly IDS depends on normal behaviour, where the main advantage of this system is its ability to discover new attacks. On the other hand, the main drawback of anomaly IDS is high false alarm rate results. Therefore, a hybrid IDS is a combination of misuse and anomaly and acts as a solution to overcome the disadvantages of these two methods. In this paper, a new hybrid IDS is proposed based on the RNA encoding idea and applying the K-means clustering algorithm. Firstly, choosing random records for both training and testing. Secondly, propose RNA encoding by calculating all possible record values within dataset and generating RNA characters for each value, then dividing it into blocks. The third step is done by searching and extracting normal keys based on the most repeated blocks, and the same procedure is applied to extract the attack keys. Finally, the Kmeans clustering method is used to classify the testing records based on extracted keys. The proposed method is evaluated by calculating the detection rate (DR), false alarm rate (FAR), and accuracy, where the achieved DR, FAR, and accuracy are equal to 91.13%, 0.46%, and 92.02% respectively. Based on the achieved results, it can be said that the proposed hybrid IDS has high DR and accuracy results, can detect new attacks, and can solve the problem of anomaly IDS by getting a low false alarm rate result.
With the development of cloud computing during the latest years, data center networks have become a great topic in both industrial and academic societies. Nevertheless, traditional methods based on manual and hardware devices are burdensome, expensive, and cannot completely utilize the ability of physical network infrastructure. Thus, Software-Defined Networking (SDN) has been hyped as one of the best encouraging solutions for future Internet performance. SDN notable by two features; the separation of control plane from the data plane, and providing the network development by programmable capabilities instead of hardware solutions. Current paper introduces an SDN-based optimized Resch
Corona virus sickness has become a big public health issue in 2019. Because of its contact-transparent characteristics, it is rapidly spreading. The use of a face mask is among the most efficient methods for preventing the transmission of the Covid-19 virus. Wearing the face mask alone can cut the chance of catching the virus by over 70\%. Consequently, World Health Organization (WHO) advised wearing masks in crowded places as precautionary measures. Because of the incorrect use of facial masks, illnesses have spread rapidly in some locations. To solve this challenge, we needed a reliable mask monitoring system. Numerous government entities are attempting to make wearing a face mask mandatory; this process can be facilitated by using face m
... Show MoreThis study depicts the removal of Manganese ions (Mn2+) from simulated wastewater by combined electrocoagulation/ electroflotation technologies. The effects of initial Mn concentration, current density (C.D.), electrolysis time, and different mesh numbers of stainless steel screen electrodes were investigated in a batch cell by adopting Taguchi experimental design to explore the optimum conditions for maximum removal efficiency of Mn. The results of multiple regression and signal to noise ratio (S/N) showed that the optimum conditions were Mn initial concentration of 100 ppm, C.D. of 4 mA/cm2, time of 120 min, and mesh no. of 30 (wire/inch). Also, the relative significance of each factor was attained by the analysis
... Show MoreThis study depicts the removal of Manganese ions (Mn2+) from simulated wastewater by combined electrocoagulation/ electroflotation technologies. The effects of initial Mn concentration, current density (C.D.), electrolysis time, and different mesh numbers of stainless steel screen electrodes were investigated in a batch cell by adopting Taguchi experimental design to explore the optimum conditions for maximum removal efficiency of Mn. The results of multiple regression and signal to noise ratio (S/N) showed that the optimum conditions were Mn initial concentration of 100 ppm, C.D. of 4 mA/cm2, time of 120 min, and mesh no. of 30 (wire/inch). Also, the relative significance of each factor was attained by the analysis of variance (ANO
... Show MoreVitamins k is an important fat-soluble vitamin that can be obtained from plants, bacteria and animals and is necessary for the blood clotting. It plays a key function as a cofactor in the synthesizing of blood clotting proteins in the liver; recently, the interest for its functions in extra-hepatic tissue has increased. Vitamin k deficiency is usually caused by abnormal absorption rather than in the lack of vitamin in food. Apart from its impact on clotting, chronic subclinical deficiency of vitamin K maybe a risk factor for many diseases such as osteoporosis, atherosclerosis, cancer, insulin resistance, neurodegenerative diseases and others, while current food intake guidelines be focused on the daily dose necessary to avoid blood loss.
... Show MoreVoice Activity Detection (VAD) is considered as an important pre-processing step in speech processing systems such as speech enhancement, speech recognition, gender and age identification. VAD helps in reducing the time required to process speech data and to improve final system accuracy by focusing the work on the voiced part of the speech. An automatic technique for VAD using Fuzzy-Neuro technique (FN-AVAD) is presented in this paper. The aim of this work is to alleviate the problem of choosing the best threshold value in traditional VAD methods and achieves automaticity by combining fuzzy clustering and machine learning techniques. Four features are extracted from each speech segment, which are short term energy, zero-crossing rate, auto
... Show MoreMost of the Internet of Things (IoT), cell phones, and Radio Frequency Identification (RFID) applications need high speed in the execution and processing of data. this is done by reducing, system energy consumption, latency, throughput, and processing time. Thus, it will affect against security of such devices and may be attacked by malicious programs. Lightweight cryptographic algorithms are one of the most ideal methods Securing these IoT applications. Cryptography obfuscates and removes the ability to capture all key information patterns ensures that all data transfers occur Safe, accurate, verified, legal and undeniable. Fortunately, various lightweight encryption algorithms could be used to increase defense against various at
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