Distributed Denial of Service (DDoS) attacks on Web-based services have grown in both number and sophistication with the rise of advanced wireless technology and modern computing paradigms. Detecting these attacks in the sea of communication packets is very important. There were a lot of DDoS attacks that were directed at the network and transport layers at first. During the past few years, attackers have changed their strategies to try to get into the application layer. The application layer attacks could be more harmful and stealthier because the attack traffic and the normal traffic flows cannot be told apart. Distributed attacks are hard to fight because they can affect real computing resources as well as network bandwidth. DDoS attacks can also be made with smart devices that connect to the Internet, which can be infected and used as botnets. They use Deep Learning (D.L.) techniques like Convolutional Neural Network (C.N.N.) and variants of Recurrent Neural Networks (R.N.N.), such as Long Short-Term Memory (L.S.T.M.), Bidirectional L.S.T.M., Stacked L.S.T.M., and the Gat G.R.U.. These techniques have been used to detect (DDoS) attacks. The Portmap.csv file from the most recent DDoS dataset, CICDDoS2019, has been used to test D.L. approaches. Before giving the data to the D.L. approaches, the data is cleaned up. The pre-processed dataset is used to train and test the D.L. approaches. In the paper, we show how the D.L. approach works with multiple models and how they compare to each other.
Various visual media are becoming an increasingly important and active instrument of communication. This fact has led some political parties and leading personalities in Iraq to make use of them as an accepted forum for the discussion of public affairs usually in a manner that conforms to their declared policy. They have to draw as much popular support as they could for the causes which they fight for. As a result, a state of great confusion has been created from the contradictory statements made by the contending parties and gave left grave consequences on all types of the audience receiving them. The problem of the study can be summarized in one major question: What is the opinions of the audience as regards the statements made by the
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Twitter popularity has increasingly grown in the last few years, influencing life’s social, political, and business aspects. People would leave their tweets on social media about an event, and simultaneously inquire to see other people's experiences and whether they had a positive/negative opinion about that event. Sentiment Analysis can be used to obtain this categorization. Product reviews, events, and other topics from all users that comprise unstructured text comments are gathered and categorized as good, harmful, or neutral using sentiment analysis. Such issues are called polarity classifications. This study aims to use Twitter data about OK cuisine reviews obtained from the Amazon website and compare the effectiveness
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Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has
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In this paper, the botnet detection problem is defined as a feature selection problem and the genetic algorithm (GA) is used to search for the best significant combination of features from the entire search space of set of features. Furthermore, the Decision Tree (DT) classifier is used as an objective function to direct the ability of the proposed GA to locate the combination of features that can correctly classify the activities into normal traffics and botnet attacks. Two datasets namely the UNSW-NB15 and the Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS2017), are used as evaluation datasets. The results reveal that the proposed DT-aware GA can effectively find the relevant features from
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In this paper, the botnet detection problem is defined as a feature selection problem and the genetic algorithm (GA) is used to search for the best significant combination of features from the entire search space of set of features. Furthermore, the Decision Tree (DT) classifier is used as an objective function to direct the ability of the proposed GA to locate the combination of features that can correctly classify the activities into normal traffics and botnet attacks. Two datasets namely the UNSW-NB15 and the Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS2017), are used as evaluation datasets. The results reveal that the proposed DT-aware GA can effectively find the relevant
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The current study introduces a novel method for calculating the stability time by a new approach based on the conversion of degradation from the conductivity curve results obtained by the conventional method. The stability time calculated by the novel method is shorter than the time measured by the conventional method. The stability time in the novel method can be calculated by the endpoint of the tangency of the conversion curve with the tangent line. This point of tangency represents the stability time, as will be explained in detail. Still, it gives a clear and accurate envisage of the dehydrochlorination behavior and can be generalized to all types of polyvinyl chloride compared to the stability time measured by conventional ones based
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Diabetic foot ulcer (DFU) or Lower limb ulcers are one of the major complications caused by diabetes mellitus especially when patients fail to maintain tight glycemic control. DFU is linked to multiple risk factors along with the genetic factors and ethnicity which play a significant role in the development of DFUs through their effects on multiple aspects of the pathophysiological process. This narrative review aimed to summarize all the previous studies within the last ten years associating gene polymorphism and DFU. Polymorphism associated with vascular endothelial growth factor (rs699947), the G894T polymorphism of the endothelial nitric oxide synthase gene, interleukin-6–174 G>C gene polymorphism, heat shock protein 70 gene polymorph
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