Zaghloul Saad Zaghloul

Nelly Elsayed, Zaghloul Saad Zaghloul, Sylvia Worlali Azumah et al.

Internet of Things (IoT) allowed smart homes to improve the quality and the comfort of our daily lives. However, these conveniences introduced several security concerns that increase rapidly. IoT devices, smart home hubs, and gateway raise various security risks. The smart home gateways act as a centralized point of communication between the IoT devices, which can create a backdoor into network data for hackers. One of the common and effective ways to detect such attacks is intrusion detection in the network traffic. In this paper, we proposed an intrusion detection system (IDS) to detect anomalies in a smart home network using a bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN) hybrid model. The BiLSTM recurrent behavior provides the intrusion detection model to preserve the learned information through time, and the CNN extracts perfectly the data features. The proposed model can be applied to any smart home network gateway.

Zaghloul Saad Zaghloul, Magdy Bayoumi

Controlling the surrounding world and predicting future events has always seemed like a dream, but that could become a reality using a Brain-Computer/Machine Interface (BCI/BMI). Epilepsy is a group of neurological diseases characterized by epileptic seizures. It affects millions of people worldwide, with 80 percent of cases occurring in developing countries. This can result in accidents and sudden, unexpected death. Seizures can happen undetectably in newborns, comatose, or motor-impaired patients, especially due to the fact that many medical personnel is not qualified for EEG signal analysis. Therefore, a portable automated detection and monitoring solution is in high demand. Thus, in this study, a system of a wireless wearable adaptive for early prediction of epilepsy seizures is proposed, works via minimally invasive wireless technology paired with an external control device (e.g., a doctors smartphone), with a higher than standard accuracy 71 percent and prediction time (14.56 sec). This novel architecture has not only opened new opportunities for daily usable BCI implementations, but they can also save a life by helping to prevent a seizure fatal consequences