Ahmed Ali

With the rapid growth of informatics systems’ technology in this modern age, the Internet of Things (IoT) has become more valuable and vital to everyday life in many ways. IoT applications are now more popular than they used to be due to the availability of many gadgets that work as IoT enablers, including smartwatches, smartphones, security cameras, and smart sensors. However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. IoT systems have several security limitations due to their disreputability characteristics, like dynamic communication between IoT devices. The dynamic communications resulted from the limited resources of these devices, such as their data storage and processing units. Recently, many attempts have been made to develop intelligent models to protect IoT networks against DDoS attacks. The main ongoing research issue is developing a model capable of protecting the network from DDoS attacks that is sensitive to various classes of DDoS and can recognize legitimate traffic to avoid false alarms. Subsequently, this study proposes combining three deep learning algorithms, namely recurrent neural network (RNN), long short-term memory (LSTM)-RNN, and convolutional neural network (CNN), to build a bidirectional CNN-BiLSTM DDoS detection model. The RNN, CNN, LSTM, and CNN-BiLSTM are implemented and tested to determine the most effective model against DDoS attacks that can accurately detect and distinguish DDoS from legitimate traffic. The intrusion detection evaluation dataset (CICIDS2017) is used to provide more realistic detection. The CICIDS2017 dataset includes benign and up-to-date examples of typical attacks, closely matching real-world data of Packet Capture. The four models are tested and assessed using Confusion Metrix against four commonly used criteria: accuracy, precision, recall, and F-measure. The performance of the models is quite effective as they obtain an accuracy rate of around 99.00%, except for the CNN model, which achieves an accuracy of 98.82%. The CNN-BiLSTM achieves the best accuracy of 99.76% and precision of 98.90%.

Kant organizes his analysis in terms of four moments of the judgment of taste, each of which is supposed to contribute something essential to the complete exposition of the beautiful in "Critique of Judgment". He, in the third moment, based on the key concept analysis of "purposiveness", reaches to the beauty definition "a purposiveness without a purpose" (subjective purposiveness). Below this moment, two concepts of "dependent beauty" and "the ideal of beauty" also arises that aesthetic judgment in both of them is based on an objective purposiveness and a concept indicating nature of the object and the perfection of the object that is considered as default for judgment. This default in dependent beauty is perfection of the object and in the ideal of beauty is a concept from the sum of the rational idea and the aesthetic normal idea, but this point of view is in contradiction with the principle of Kant's aesthetic, which judgment of the beautiful just relies on the subjective purposiveness. This article reviewing the concept of purposiveness and the type of subjective purposiveness in the ideal of beauty and dependent beauty, attempts to explain the aesthetic value of these concepts and their differences and similarities, and finally to determine the position of these two concepts in Kant's philosophy.

This investigation is intended to explore the effect of the digital environment as well as perfectionism during the COVID-19 pandemic on anxiety and depression. The study used a mixed-methods design; a mixed research methodology was used regarding explanatory design by using a qualitative sub-sample from quantitative sample data. The researcher conducted the study on a sample of 980 students and non-students smartphone and internet users using both qualitative and quantitative approach. Three different questionnaires were used: The Multidimensional Perfectionism Scale, Beck Depression, and Beck Anxiety Inventory. On the other hand, the researcher designed a self-reported interview for the qualitative part that included 5 major questions and 10 sub-questions. The gathered data were investigated using SPSS version 22 to analyze the collected data for this study, and simple descriptive statistics and coding were used. The results denote that the digital environment is significantly correlated with multidimensional perfectionism in a low positive manner for students. However, the correlation effects for non-students are significantly high as evidenced by a significant positive correlation. Another finding suggests that there is a positive significant association between perfectionism and being depressed and anxious.

Here, a novel conservative chaotic oscillator is presented. Various dynamics of the oscillator are examined. Studying the dynamical properties of the oscillator reveals its unique behaviors. The oscillator is multistable with symmetric dynamics. Equilibrium points of the oscillator are investigated. Bifurcations, Lyapunov exponents, and the Poincare section of the oscillator’s dynamics are analyzed. Also, the oscillator is investigated from the viewpoint of initial conditions. The study results show that the oscillator is conservative and has no dissipation. It also has various dynamics, such as equilibrium point and chaos. The stability analysis of equilibrium points shows there are both stable and unstable fixed points.