Physical cyber-security algorithm for wireless sensor networks

Today, the wireless sensor network (WSN) plays an important role in our daily life. In addition, it is used in many applications such as military, medical, greenhouse, and transport. Due to the sending data between its nodes or to the base station requires a connection link, the sensor nodes can be exposed to the many attacks that exploit the weaknesses of the network. One of the most important types of these attacks is the denial of service (DoS). DoS attack exhausts the system's resources that lead the system to be out of service. In this paper, a cyber-security algorithm is proposed for physical level of WSN that adopts message queuing telemetry transport (MQTT) protocol for data transmission and networking. This algorithm predicts the DoS attacks at the first time of happening to be isolated from the WSN. It includes three stages of detecting the attack, predicting the effects of this attack and preventing the attacks by excluding the predicted nodes from the WSN. We applied a type of DoS attack that is a DoS injection attack (DoSIA) on the network protocol. The proposed algorithm is tested by adopting three case studies to cover the most common cases of attacks. The experiment results show the superior of the proposed algorithm in detecting and solving the cyber-attacks

Developed security and privacy algorithms for cyber physical system

Cyber-physical system (CPS) is a modern technology in the cyber world, and it integrates with wireless sensor network (WSN). This system is widely used in many applications such as a smart city, greenhouse, healthcare, and power grid. Therefore, the data security and integrity are necessary to ensure the highest level of protection and performance for such systems. In this paper, two sides security system for cyber-physical level is proposed to obtain security, privacy, and integrity. The first side is applied the secure sockets layer (SSL)/transport layer security (TLS) encryption protocol with the internet of things (IoT) based message queuing telemetry transport (MQTT) protocol to secure the connection and encrypt the data exchange between the system's parties. The second side proposes an algorithm to detect and prevent a denial of service (DoS) attack (hypertext transfer protocol (HTTP) post request) on a Web server. The experiment results show the superior performance of the proposed method to secure the CPS by detecting and preventing the cyber-attacks, which infect the Web servers. They also prove the implementation of security, privacy and integrity aspects on the CPS

The classification of autism spectrum disorder by machine learning methods on multiple datasets for four age groups

The world has seen the advent of numerous illnesses that cannot be medically recognized, such as Autism Spectrum Disorder (ASD). It affects several behavioral domains, including social and linguistic competence and stereotyped and repetitive actions. This illness is a serious neurodevelopmental disorder. Since many other mental illnesses have strikingly similar symptoms to those of ASD, diagnosing ASD can be difficult and time-consuming. Early diagnosis based on different health and physiological characteristics seems feasible with the rising usage of machine learning-based models in predicting many human diseases. This study aims to create a classification model that can predict the likelihood of ASD with the greatest degree of precision. To investigate the potential for predicting and analyzing ASD traits in the Toddler, Child, Adolescent, and adult age groups, we used several supervised Machine Learning (ML) models. These include Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), Nave Bayes (NB), Logistic Regression (LR), and Random Forest (RF). Four publicly available, distinctive non-clinical ASD screening datasets from Kaggle and the UCI machine learning library are used to test these models. The first dataset includes 1054 instances and 19 toddler-related features. The remaining ones consist of 21 traits and 292, 104, and 704 cases involving children, adolescents, and adults, respectively. After implementing different ML approaches over the pre-processing datasets, the results showed that the DT, LR, and RF classifiers are the dominant models. These dominated models achieve the highest prediction accuracy, among other studied models, of about 100% for all the utilized datasets