In recent decades, the internet has grown and changed the world tremendously, and this, in turn, has brought about many cyberattacks. Cybersecurity represents one of the most serious threats to society, and it costs millions of dollars each year. The most significant question remains: Where do these attacks come from? The answer is that botnets provide platforms for cyberattacks. For many organizations, a botnet-assisted attack is a terrifying threat that can cause financial losses and leave global victims in its wake. It is therefore imperative to defend organizations against botnet-assisted attacks. Software defined networking (SDN) has emerged as one of the most promising paradigms for this because it allows exponential increases in the complexity of network management and configuration. SDN has a substantial advantage over traditional approaches with regard to network management because it separates the control plane from network equipment. However, security challenges continue to arise, which raises the need for different types of implementation strategies to spread attack vectors, despite the significant benefits. The main objective of this survey is to assess botnet detection techniques by using systematic reviews and meta-analyses (PRISMA) guidelines. We evaluated various articles published since 2006 in the field of botnet detection, based on machine learning, and from 2015 in the field of SDN. Specifically, we used top-rated journals that featured the highest impact factors. In this paper, we aim to elaborate on several research areas regarding botnet attacks, detection techniques, machine learning, and SDN. We also address current research challenges and propose directions for future research.

Alzheimer’s disease (AD) is the most common neurodegenerative disorder in the world, affecting an estimated 50 million individuals. The nerve cells become impaired and die due to the formation of amyloid-beta (Aβ) plaques and neurofibrillary tangles (NFTs). Dementia is one of the most common symptoms seen in people with AD. Genes, lifestyle, mitochondrial dysfunction, oxidative stress, obesity, infections, and head injuries are some of the factors that can contribute to the development and progression of AD. There are just a few FDA-approved treatments without side effects in the market, and their efficacy is restricted due to their narrow target in the etiology of AD. Therefore, our aim is to identify a safe and potent treatment for Alzheimer’s disease. We chose the ursolic acid (UA) and its similar compounds as a compounds’ library. And the ChEMBL database was adopted to obtain the active and inactive chemicals against Keap1. The best Quantitative structure-activity relationship (QSAR) model was created by evaluating standard machine learning techniques, and the best model has the lowest RMSE and greatest R2 (Random Forest Regressor). We chose pIC50 of 6.5 as threshold, where the top five potent medicines (DB06841, DB04310, DB11784, DB12730, and DB12677) with the highest predicted pIC50 (7.091184, 6.900866, 6.800155, 6.768965, and 6.756439) based on QSAR analysis. Furthermore, the top five medicines utilize as ligand molecules were docked in Keap1’s binding region. The structural stability of the nominated medications was then evaluated using molecular dynamics simulations, RMSD, RMSF, Rg, and hydrogen bonding. All models are stable at 20 ns during simulation, with no major fluctuations observed. Finally, the top five medications are shown as prospective inhibitors of Keap1 and are the most promising to battle AD.

In this study, the natural frequencies and roots (Eigenvalues) of the transcendental equation in a cantilever steel beam for transverse vibration with clamped free (CF) boundary conditions are estimated using a long short-term memory-recurrent neural network (LSTM-RNN) approach. The finite element method (FEM) package ANSYS is used for dynamic analysis and, with the aid of simulated results, the Euler–Bernoulli beam theory is adopted for the generation of sample datasets. Then, a deep neural network (DNN)-based LSTM-RNN technique is implemented to approximate the roots of the transcendental equation. Datasets are mainly based on the cantilever beam geometry characteristics used for training and testing the proposed LSTM-RNN network. Furthermore, an algorithm using MATLAB platform for numerical solutions is used to cross-validate the dataset results. The network performance is evaluated using the mean square error (MSE) and mean absolute error (MAE). Finally, the numerical and simulated results are compared using the LSTM-RNN methodology to demonstrate the network validity.

The number of botnet malware attacks on Internet devices has grown at an equivalent rate to the number of Internet devices that are connected to the Internet. Bot detection using machine learning (ML) with flow-based features has been extensively studied in the literature. Existing flow-based detection methods involve significant computational overhead that does not completely capture network communication patterns that might reveal other features of malicious hosts. Recently, Graph-Based Bot Detection methods using ML have gained attention to overcome these limitations, as graphs provide a real representation of network communications. The purpose of this study is to build a botnet malware detection system utilizing centrality measures for graph-based botnet detection and ML. We propose BotSward, a graph-based bot detection system that is based on ML. We apply the efficient centrality measures, which are Closeness Centrality (CC), Degree Centrality (CC), and PageRank (PR), and compare them with others used in the state-of-the-art. The efficiency of the proposed method is verified on the available Czech Technical University 13 dataset (CTU-13). The CTU-13 dataset contains 13 real botnet traffic scenarios that are connected to a command-and-control (C&C) channel and that cause malicious actions such as phishing, distributed denial-of-service (DDoS) attacks, spam attacks, etc. BotSward is robust to zero-day attacks, suitable for large-scale datasets, and is intended to produce better accuracy than state-of-the-art techniques. The proposed BotSward solution achieved 99% accuracy in botnet attack detection with a false positive rate as low as 0.0001%.