The Internet of Things operates in vital areas and involves people, objects, and networks. Indeed, it ensures the interconnection and exchange of critical information that does not tolerate flaws. A successful attack on an object in one of these fields will cause fatal damage. Therefore, securing IOTs requires particular attention to the used protocols. It is essential to choose adapted protocols to the requirements of the objects and provide maximum security. Authentication is the first entry to IOTs that must be optimal and secure, without encumbering objects with limited capacities. To address this issue and to avoid concentration points, we present an improved authentication protocol based on the Blake2 hash function and the distribution of calculating OTP function. We will then evaluate, in terms of spatial and temporal complexity, the improvement brought to the IOT authentication process by the proposed process. We will prove that the use of these technics ensure a secure authentication that consumes less time and less memory.

The demand for cloud computing systems has increased tremendously in the IT sector and various business applications due to their high computation and cost-effective solutions to various computing problems. This increased demand has raised several challenges such as load balancing and security in cloud systems. Numerous approaches have been presented for load balancing but providing security and maintaining integrity and privacy remains a less explored research area. Intrusion detection systems have emerged as a promising solution to predict attacks. In this work, we develop a deep learning-based scheme that contains data pre-processing, convolution operations, BiLSTM model, attention layer, and CRF modeling. The current study employs a machine learning-based approach to detect intrusions based on the attackers' historical behavior. Deep learning algorithms were used to extract features from the image and determine the significance of dense packets to generate the salient fine-grained feature that can be used to detect malicious traffic and presents the final classification using fused features.

Learning-based localization plays a significant role in wireless indoor localization problems over deterministic or probabilistic-based methods. Recent works on machine learning-based indoor localization show the high accuracy of predicting over traditional localization methods existing. This paper presents a Received Signal Strength (RSS) based improved localization method called TreeLoc(Tree-Based Localization). This novel method is based on ensemble learning trees. Popular Decision Tree Regressor (DTR), Random Forest Regression (RFR), and Extra Tree Regressor have been investigated to develop the novel TreeLoc method. Out of the tested algorithm, the TreeLoc algorithm showed better performances in position estimation for indoor environments with RMSE 8.79 for the x coordinate and 8.83 for the y coordinate.

This paper focuses on Spread Spectrum technique and its interference mitigation feature as applied in wireless communication. With spread spectrum technology it is possible to implement the transmission of multiple signals over wider ranges of spectrum without resulting to interference from other signals transmitted over the same frequencies. It does this by rejecting any received signal that does not carry the proper code. Interference rejection, one of the several features of spread spectrum technology is a difficult concept to understand. It is therefore proper to x-ray this topic in a less complex manner so that it can be well understood by those who are not mathematically grounded.   In view of this a further simplified approach in presenting this subject is necessary. A tutorial approach is used to simplify this subject for better understanding and how this feature is used in Code Division Multiple Access (CDMA) systems. To show how multipath interference rejection is achieved in CDMA systems simple equations and schematics were used. The discussions cover the method of code recognition at the receiver which serves as a technique for interference mitigation. The paper helps to understand the theory of code recognition in Spread Spectrum, and thus provides answer to the question on how does interference mitigation or rejection in spread spectrum works? 

Due to explosive growth of users, increasing energy demand and also the need to improve efficiency and maintain the stability of the electricity grid, smart grid is the only option available to electrical industry engineers. In fact, the smart grid is a physical-cyber system that provides coherent and integrated communication, processing and control functions. The smart grid provides control and management of millions of devices in the electricity industry in a reliable, scalable, cost-effective, real time and two-sided manner. Given the increasing growth of cyber threats in the last decade, the need to protect the electricity industry and its critical systems seems essential. The slightest disruption to the power industry's systems results in disruption to other industries, reduced productivity, and discontent. Hence we proposed an efficient cloud based architecture to improve smart grid performance. Proposed architecture provides data security and privacy against different types of cyber threats such as replay attack, modification attack and so on.