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.

A neurodevelopmental illness called autism spectrum disorder (ASD) is frequently associated with sensory problems including an excessive or insufficient sensitivity to noises, scents, or touch. Children with autism typically do not talk much and keep to themselves, but they can imitate certain actions from cartoons and movies. They may exhibit unsafe or unexpected behavior as a result. Early detection and therapy can assist in improving the diseases. In this study, we suggested a data‐driven machine learning (ML) model for examining the autism dataset of diverse age groups (toddlers, children, and adults) to classify autism in the initial stage. The proposed ML model can efficiently analyze autism patients’ datasets and correctly classify and detect ASD features. We utilized a data preprocessing technique followed by feature selection methods using information gain and Pearson correlation. Then, we employed five different ML classifiers (KNN, RF, SVM, NB, and MLP) together with a hyperparameter optimization strategy. We assess their work using performance metrics such as prediction accuracy and the F1‐measure. After comparing the accuracy between different classifiers, SVM produced the highest accuracies of 98%, 99%, and 100% for the toddler, child, and adult datasets while MLP produced an accuracy of 0.94 for the Pakistani child dataset, respectively. These thorough experimental assessments suggest that correct fine‐tuning of the ML techniques can be crucial in the classification of autism in individuals of various ages. We believe that the thorough feature significance analysis presented in this study can guide medical professionals’ judgment when screening ASD individuals. In comparison with other methods currently used for the timely identification of ASD, the suggested framework has shown encouraging results.