F5 Networks (United States)

Cyberattacks have been becoming increasingly sophisticated in speed and complexity, while Cybersecurity is being pushed by cyberattacks to provide the appropriate level of defensive preparedness. This research has developed an Adaptive Cyber Security Framework that uses artificial intelligence not only to identify potential cyber threats but to be prepared for the forensic analysis of those threats that are able to evade detection. The primary framework was based upon Long Short-Term Memory (LSTM) neural network architecture with attention mechanism, providing the model’s ability to provide strong predictive capabilities as well as a layer of interpretability which could be trusted by human security analysts. The simulation testing demonstrated the model was able to achieve a solid balance between precision and recall; demonstrated low latency performance even at high load levels; and remained stable and robust even when there were high levels of noise present within the test data. In addition, the model provided a satisfactory logging of anomalous events; demonstrating its readiness for forensic analysis. While the model was trained using synthetic data, the results indicate the development of a practical methodology for developing scalable, adaptable, and memory-based AI driven Cyber Defense systems rather than reactive ones.

This paper aims to classify signal and modulation classes of a given wireless signal with high accuracy using a model having a low number of parameters. We propose an end-to-end method to classify a wireless signal based on its signal and modulation type using a CNN-based architecture. The proposed architecture is similar to that of the LeNet-5. Firstly, we implement signal and modulation classification using a decision tree, followed by a random forest algorithm, classic examples of machine learning(ML) based algorithms. Since our dataset is a time series, we also implement using RNN-LSTM based model for the classification. The proposed model has fewer parameters than that of the CNN-based, RNN-LSTM based architectures. Moreover, it achieves better accuracy for a wide range of signal-to-noise ratios than a decision tree, random forest, RNN-LSTM based classifiers.

The chapter focuses on the context of Ghana. Based on the Ghanaian YARG data, the chapter explores a set of notable ways in which the proliferation of social media has affected the religious engagements and practices of young adults in Ghana. The chapter highlights how active use of social media, as evident among young adults in Ghana, influences their religious lifestyles. It particularly reflects on the respondents’ agency and the way they negotiate the material they find on the Internet and social media, focusing on both the “contents” and “discontents” they express with the medium.

We first explain problems with the default scheduler used by the Linux kernel MPTCP implementation. Then we propose the design of a new scheduler. Preliminary empirical results show that our proposed scheduler improves the throughput in MPTCP by alleviating the problems caused by the default scheduler.

The results of truing and dressing depend on: sharpness of the dresser, speed of dressing, depth of cut and vibration phenomena. Prediction of the optimum wheel surface for grinding which produces an accurate profile and cuts most efficiently can minimize the time and cost and optimize performance of the operation before it is performed in the shop. It has especially significant meaning in truing and dressing of hard CBN wheels. The research described in this paper shows computer simulated and visualized wheel surface before and after the wheel dressing process. This paper provides additional results to those was reported in [8] taking into account the influence of vibrations. It is a companion paper to [8].

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