Underwater communication remains a challenging technology via communication cables and the cost of underwater sensor network (UWSN) deployment is still very high. As an alternative, underwater wireless communication has been proposed and have received more attention in the last decade. Preliminary research indicated that the Radio Frequency (RF) and Magneto-Inductive (MI) communication achieve higher data rate in the near field communication. The optical communication achieves good performance when limited to the line-of-sight positioning. The acoustic communication allows long transmission range. However, it suffers from transmission losses and time-varying signal distortion due to its dependency on environmental properties. These latter are salinity, temperature, pressure, depth of transceivers, and the environment geometry. This paper is focused on both the acoustic and magneto-inductive communications, which are the most used technologies for underwater networking. Such as acoustic communication is employed for applications requiring long communication range while the MI is used for real-time communication. Moreover, this paper highlights the trade-off between underwater properties, wireless communication technologies, and communication quality. This can help the researcher community by providing clear insight for further research.

Anomaly detection is one of the biggest issues of security in the Industrial Internet of Things (IIoT) due to the increase in cyber attack dangers for distributed devices and critical infrastructure networks. To face these challenges, the Intrusion Detection System (IDS) is suggested as a robust mechanism to protect and monitor malicious activities in IIoT networks. In this work, we suggest a new mechanism to improve the efficiency and robustness of the IDS system using Distributional Reinforcement Learning (DRL) and the Generative Adversarial Network (GAN). We aim to develop realistic and equilibrated distribution for a given feature set using artificial data in order to overcome the issue of data imbalance. We show how the GAN can efficiently assist the distributional RL-based-IDS in enhancing the detection of minority attacks. To assess the taxonomy of our approach, we verified the effectiveness of our algorithm by using the Distributed Smart Space Orchestration System (DS2OS) dataset. The performance of the normal DRL and DRL-GAN models in binary and multiclass classifications was evaluated based on anomaly detection datasets. The proposed models outperformed the normal DRL in the standard metrics of accuracy, precision, recall, and F1 score. We demonstrated that the GAN introduced in the training process of DRL with the aim of improving the detection of a specific class of data achieves the best results.

The incorporation of advanced networking technolo- gies makes modern systems vulnerable to cyber-attacks that can result in a number of harmful outcomes. Due to the increase of security incidents and massive activities on networks, existing works have mainly focused on designing Intrusion Detection Systems (IDSs) based on traditional machine learning and deep learning models. In recent times, state of the art performance has been achieved in various fields through Deep Reinforcement Learning (DRL), which combines deep learning with reinforce- ment learning. In this paper, we propose a new DRL-based IDS for network traffics using Markov decision process (MDP) to improve the IDS decision-making performance. In addition, an extensive analysis of the IDS behavior is provided through mod- eling the interaction between the well-behaving IDS and attacker players using Stochastic Game Theory. Specifically, we used a non- zero-sum stochastic game, where the transitions between states depend on both the IDS and the attacker's actions at each stage of the game. We show that our game reaches a Nash Equilibrium upon convergence to seek the optimal solution, which corresponds to the optimal decision policy where both players maximize their profits. We compared the performance of our proposed DRL-IDS to the baseline benchmark of standard reinforcement learning (RL) and several machine learning algorithms using NSL-KDD dataset. As a result, our proposed DRL-IDS outperforms the existing models by improving both the detection rate and the accuracy while reducing false alarms. Results were provided to demonstrate the convergence of the game theory-based IDS under various settings toward equilibrium. This equilibrium corresponds to the safe state where both players are playing their respective best strategies.

Nowadays, information security is extremely critical issues for every organization to protect information from the useless data on the manipulation of network traffic or intrusion. Intrusion detection system has one of the important roles to prevent data or information from malicious behaviors because its capable of detecting attacks in several available environments. Thereafter, many researches concentrate on developing new algorithms to treat the Dataset by different way. In this work, we suggest a new proposed PCA-fuzzy Clustering-KNN method that means ensemble of Analysis of Principal Component and Fuzzy Clustering with K-Nearest Neighbor feature selection technics. However, we perform two main class classifications to construct our suggested model. Then, to check the robustness of model we used as well-known Dataset NSL-KDD used for analysis of anomaly. This Dataset is based on benchmark data used for intrusion detection, KDDCup 1999. Therefore, we analyse NSL-KDD Dataset using PCA-fuzzy Clustering-KNN analytic and try to define the performance of incident using machine learning algorithms, the algorithm learns what type of attacks are found in which classes in order to improve the classification accuracy and reduce high false alarm rate and detects the maximum of detection rate from Dataset as shown by the numerical results.

Recently, the problem of the intrusion detection has been largely studied by the computer and networks security communities. Then, the Intrusion Detection System (IDS) becomes a interest topic in research and in particular in machine learning and data mining. In order to improve the classification accuracy and to reduce high false alarm rate from the classical data base like KDD99 or others. In this work, we present a state of the art about this topic and we use classification algorithms such as Linear discriminant analysis (LDA) and Principal Component Analysis (PCA) to identify the intrusion and classification anomaly. The experiments of the IDS are performed with NSL-KDD data set and we try to improve the existing classification methods.

Recently Underwater wireless Sensor Networks (UWSNs) have been suggested as a powerful technology for many civilian and military applications, such that tactical surveillance. Geographic routing that uses the position information of nodes to route the packet toward a destination is preferable for UWSNs. In this paper, we propose a New Greedy Forwarding (NGF) strategy using splitting mechanism based on Chinese remainder theorem(CRT) for UWSNs. In the approach, source node reduced the number of bits transmitted using the proposed splitting mechanism based on CRT if there are more than two nodes participate in the forwarding of one packet. This strategy distribute the forwarding task between more nodes instead of selecting one node as next-hop, that reduce the energy consumption per node and maintain node communication for a long time. Thus resulting the increase of network life time and decrease the number of isolated/void nodes. We use topology control through depth adjustment to cope with the problem of isolated and void nodes appeared in geographic routing protocols. Simulation results shows that with the anycast greedy forwarding strategy the network life time is about 500 rounds whereas it is about 1000 rounds using the new greedy forwarding strategy, which means the new strategy increase the network life time and increase the network performance in energy saving.