Nowadays, electronic applications are being adopted instead of many traditional processes in data and information management that use Internet technology as a transmission medium. Therefore, these data and information suffer from different types of attacks that aim to destroy or steal them. One of these attacks is the cyber classification that can halt the whole system. In this paper, a cyber-attacks detector method is proposed based on deep learning technology for Wireless Sensor Network (WSN). This method adopts the behavior of the WSN's nodes as well as the data transmission that depends on the MQTT protocol. The use of the deep learning model in this method improves the detection accuracy compared to traditional machine learning methods. The results demonstrate the efficiency of using the combination of deep learning CNN-LSTM techniques to be 96.02% in training accuracy and 95.08% for validation accuracy depending on the dataset of [1]. The machine learning model in [1] obtains an accuracy between 87% and 91% for the augmented dataset processes.

Recently, wireless sensor networks is considered as an important part of the life. It becomes wide spread in different applications, such as military, medical and environment. In this paper, a modified lightweight authentication and key management protocol for wireless sensor network is introduced, employing the technique of Elliptic Curve Cryptography with Diffie-Hellman. In addition, A designed simulator that simulate the phases of authentication starting from base station tell the last sensor node. This simulator is produced to tackle the problem of lightweight protocol absence in the well-known simulators, such as NS2 and NS3. The modified protocol and the presented simulator are tested over different scenarios and the obtained results show the superior performance of them. Moreover, the results illustrate the high accuracy of the simulator and benefits of the modification on the lightweight protocol.

Nowadays, numerous attacks can be considered high risks in terms of the security of Wireless Sensor Networks (WSN). As a result, different applications are introduced to manage the data and information exchange and related security sides to be save in transmission of data. Recently, most of the security attacks are classified as cyber ones. These attacks interest in the system halting and destroying the data rather than stealing the data. In this paper, a cyber-attacks detection system is proposed based on an intelligent hybrid model that uses deep and machine learning technologies. The proposed model improves the cyber-attack detection speed. In addition, a feature reduction model is proposed using machine learning methods (PCA and SVD) to select the most related features to the adopted classes of attacks. This can positively affect the deep-learning model complexity. The obtained results demonstrate the superiority of the proposed hybrid model-based cyber detection system in comparison to the traditional ones in reaching an accuracy of 99.98%, 100%, 100%, 100% for precision, recall, and F1-measure respectively, and reducing the time to 23s for the datasets of Message Queuing Telemetry Transport-Dataset (MQTT-DS) and Wireless Sensor Networks Dataset (WSN-DS).