Distributed Artificial Intelligence Solution for D2D Communication in 5G Networks

Device to Device (D2D) Communication is one of the technology components of the evolving 5G architecture, as it promises improvements in energy efficiency, spectral efficiency, overall system capacity, and higher data rates. The above noted improvements in network performance spearheaded a vast amount of research in D2D, which have identified significant challenges that need to be addressed before realizing their full potential in emerging 5G Networks. Towards this end, this paper proposes the use of a distributed intelligent approach to control the generation of D2D networks. More precisely, the proposed approach uses Belief-Desire-Intention (BDI) intelligent agents with extended capabilities (BDIx) to manage each D2D node independently and autonomously, without the help of the Base Station. The paper includes detailed algorithmic description for the decision of transmission mode, which maximizes the data rate, minimizes the power consumptions, while taking into consideration the computational load. Simulations show the applicability of BDI agents in jointly solving D2D challenges.Comment: 10 pages,9 figure

A novel Distributed AI framework with ML for D2D communication in 5G/6G networks

Inspired by the adoption of Artificial Intelligence (AI) and Machine Learning (ML) approaches in 5G and 6G networks, in this paper we propose a novel ML based Distributed AI (DAI) framework able to attain the ambitious goals set for emerging 5G/6G networks. The novelty of the DAI framework is that it is implemented in an autonomous, dynamic and flexible fashion, utilising Belief Desire Intention (BDI) agents, extended with ML capabilities, which reside on the mobile devices (User Equipment). We refer to these as BDIx agents. This provides a component-based framework (likened to LEGO-based building blocks), which can build on and utilise execution plans, by composing and arranging ML techniques in flexible ways within the framework, in order to achieve the desired goals. More specifically, we form a modular BDIx agent at a multi-agent system (MAS), integrated with Fuzzy Logic for the perception/cognitive part of the agents. By exploiting the capabilities of the BDIx agents in our DAI framework, we allow mobile devices to intercommunicate and cooperate in an autonomous manner, thus offering a number of attractive features, including improved performance in terms of network control execution time and message exchange, fast response in handling dynamic aspects in the network, self-organizing network functionalities, and a framework that can act as the glue platform in employing one or more intelligent approaches to tackle the diverse 5G/6G technical requirements. To demonstrate the potential of the DAI framework we focus on Device to Device (D2D) communication and illustrate its flexibility in addressing diverse D2D challenges. Through example Plan Libraries and enhanced metrics, we outline DAI implementation specifics to achieve a number of identified 5G/6G D2D requirements. To embed the concept further, the specific problem of D2D transmission mode selection is expanded upon, from problem description to solution approach (DAIS) and implementation specifics, and hence comparatively evaluate over other approaches (i.e., Unsupervised learning ML techniques, centralised control techniques, random techniques). The results demonstrated that DAIS provides, among other performance metrics, improved mobile network Spectral Efficiency (SE) and Power Consumption (PC), better and more efficient cluster formation and reduced control decision delay.This work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE – Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2) and the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy

Content-Aware Network Traffic Prediction Framework for Quality of Service-Aware Dynamic Network Resource Management

Next-generation mobile networks, such as Fifth-Generation (5G), and Sixth-Generation (6G) are envisioned to undergo an unprecedented transformation from connected things to connected intelligence with more stringent characteristics, i.e., low end-to-end latency, high bandwidth, reliable connectivity, etc. Such networks will significantly increase network traffic in the distribution networks, causing the need for real-time automated decision-making, such as automated network resource allocation. The network resources be allocated dynamically based on the Quality of Service (QoS) requirements. However, the primary concern is that the distribution networks may get congested soon after allocating network resources. Thus, a QoS-aware prediction framework can be used proactively to predict the future trend of network traffic in each QoS class. In this paper, we propose a framework for predicting the heterogeneous multivariate QoS-aware network traffic to make the best use of network resources dynamically. Specifically, we use a Recurrent Neural Network (RNN) to integrate a Bidirectional Long Short-Term Memory (BLSTM) neural network. The results show that the fusion of RNN-BLSTM can predict the QoS-aware network traffic for over 13 hours with an average accuracy of 97.68%. Moreover, the proposed model is trained and tested over limited data, collected and identified through Deep Packet Inspection (DPI) over an operational network. In addition, we compared the RNN-BLSTM with other prediction algorithms (i.e., LSTM, ARIMA, SVM) in terms of precision, execution time, and energy consumption. Lastly, the proposed framework is used to assign the network resources to each QoS class based on the QoS requirements of that class and its pre-defined priority

Detection of DDoS attacks in D2D communications using machine learning approach

In device-to-device (D2D) communications, distributed Denial-of-Service (DDoS) attacks can be quite detrimental because it can result in network structure destruction. Towards this end, the research objective of this paper is to identify and prevent DDoS and Denial-of-Service (DoS) attacks (i.e., SYN, Slowloris) in a D2D communication environment. Specifically, by replicating a real-world scenario, we emulate SLowloris attacks in a D2D communication network and generate a D2D Network-specific Slowloris dataset. This dataset along with the CICDDoS2019 dataset was then used to train our proposed Machine learning (ML) model that aids in the detection and prevention of DDoS attacks (Slowloris and SYN) in the considered D2D framework. The whole process of how to construct an emulation network for D2D communication and test it against a variety of attacks and implementations is also demonstrated in the paper. To quantify the detection accuracy in the context of DDoS and DoS attacks, we use various ML algorithms such as Random Forest, Light GBM, XGBoost, and Ada Boost and study their performance with the aid of extensive emulation. The results collected revealed that both Slowloris and CICDDoS2019 datasets achieve greater accuracy with Random Forest. Consequently, the results compel us to develop a technique for combining the identification of DDoS and DoS attacks in binary classification Random Forests with the binary decision. The proposed technique has been evaluated and compared with other related approaches in the open literature demonstrating significant performance in terms of identification and prevention time, processing and memory resources required, and device battery consumption, without affecting the accuracy of the attack identification. Hence, we advocate that our proposed technique can be extremely beneficial in preventing DDoS and DoS attacks in a D2D communication environment, where its lifetime and capabilities are mainly associated with the resources of the D2D device (i.e., CPU, Memory, and battery life).This work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE – Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2) and the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy

A Novel Deep Hierarchical Machine Learning Approach for Identification of Known and Unknown Multiple Security Attacks in a D2D Communications Network

Intrusion Detection Systems (IDSs) have played a crucial role in identifying cyber threats for a very long time. Still, their significance has increased significantly with the advent of 5G/6G technologies, particularly Device-to-Device (D2D) communication. Multiple cyberattacks, such as Man in the Middle (MITM) attacks, Structured Query Language (SQL) injection attacks, Dictionary attacks, Distributed Denial of Service (DDoS) attacks, and others by using specific attack tools such as HULK, RUDY, and GoldenEye, that can cause rapid battery drain, rendering D2D network devices more prone to hardware failure or even to the dissolution of the D2D communication network affecting the operation and the performance of the mobile network. Using a Deep Hierarchical Machine Learning Model/Deep Hierarchical Neural Network (DHMLM/DHNN) technique, we develop an Intrusion Detection System (IDS) for D2D communication that, due to its hierarchical structure, is distinct from other comparable approaches. (i.e., Recurrent Neural Networks (RNN), Deep Neural Networks (DNN), Long short-term memory (LSTM)), has several advantages, including i) reduced training time (training time can be reduced by 56%.); ii) the ability to identify multiple types of attacks; iii) the ability to identify Zero-day/Unknown attacks (i.e., attacks that it has not seen before); iv) a more straightforward model design due to the low number of connections and neurons compared to other approaches (excluding RNN and LSTM), and; v) overall outstanding performance in terms of accuracy (i.e., 99.07%). The custom/unified data set used to train and evaluate the model was partially manually emulated and partially sampled from a large set (>95%) from the commonly used CIC-DDoS-2019 data set. The after-comparison final proposed model’s 99.07% accuracy on this unified data set demonstrates the efficacy of our method. The model was also tested and demonstrated an astounding 99.63% accuracy for zero-day/unknown attacks