Cloud computing plays a critical role in modern society and enables a range of applications from infrastructure to social media. Such system must cope with varying load and evolving usage reflecting societies’ interaction and dependency on automated computing systems whilst satisfying Quality of Service (QoS) guarantees. Enabling these systems are a cohort of conceptual technologies, synthesized to meet demand of evolving computing applications. In order to understand current and future challenges of such system, there is a need to identify key technologies enabling future applications. In this study, we aim to explore how three emerging paradigms (Blockchain, IoT and Artificial Intelligence) will influence future cloud computing systems. Further, we identify several technologies driving these paradigms and invite international experts to discuss the current status and future directions of cloud computing. Finally, we proposed a conceptual model for cloud futurology to explore the influence of emerging paradigms and technologies on evolution of cloud computing.

/pdf/transformative-effects-of-iot-blockchain-and-artificial-4yaue57y4f.pdf

Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges

Internet of Things (IoT) is one of the most rapidly used technologies in the last decade in various applications. The smart things are connected in wireless or wired for communication, processing, computing, and monitoring different real-time scenarios. The things are heterogeneous and have low memory, less processing power. The implementation of the IoT system comes with security and privacy challenges because traditional based existing security protocols do not suitable for IoT devices. In this survey, the authors initially described an overview of the IoT technology and the area of its application. The primary security issue CIA (confidentially, Integrity, Availability) and layer-wise issues are identified. Then the authors systematically study the three primary technology Machine learning(ML), Artificial intelligence (AI), and Blockchain for addressing the security issue in IoT. In the end, an analysis of this survey, security issues solved by the ML, AI, and Blockchain with research challenges are mention.

Survey on IoT security: Challenges and solution using machine learning, artificial intelligence and blockchain technology

Deep learning and big data technologies for IoT security

Deep recurrent neural network for IoT intrusion detection system

The main task of future networks is to build, as much as possible, intelligent networking architectures for intellectualization, activation, and customization. Software-defined networking (SDN) technology breaks the tight coupling between the control plane and the data plane in the traditional network architecture, making the controllability, security, and economy of network resources into a reality. As one of the important actualization methods of artificial intelligence (AI), machine learning (ML), combined with SDN architecture will have great potential in areas, such as network resource management, route planning, traffic scheduling, fault diagnosis, and network security. This paper presents the network applications combined with SDN concepts based on ML from two perspectives, namely the perspective of ML algorithms and SDN network applications. From the perspective of ML algorithms, this paper focuses on the applications of classical ML algorithms in SDN-based networks, after a characteristic analysis of algorithms. From the other perspective, after classifying the existing network applications based on the SDN architecture, the related ML solutions are introduced. Finally, the future development of the ML algorithms and SDN concepts is discussed and analyzed. This paper occupies the intersection of the AI, big data, computer networking, and other disciplines; the AI itself is a new and complex interdisciplinary field, which causes the researchers in this field to often have different professional backgrounds and, sometimes, divergent research purposes. This paper is necessary and helpful for researchers from different fields to accurately master the key issues.

/pdf/a-survey-of-networking-applications-applying-the-software-24p87ht2ge.pdf

A Survey of Networking Applications Applying the Software Defined Networking Concept Based on Machine Learning

Internet of Things (IoT) introduces new challenges to conventional communication model. IoT networks characteristics, such as objects heterogeneity and scalability, require revolutionary solutions. Currently, there is no universal architecture for IoT. However, several architectures were proposed based on Software Defined Networks (SDN). SDN introduces network programmability , and centralisation, these features facilitate network abstractions, simplifying network management and eases evolution. In this paper, we investigate SDN as a novel communication architecture for IoT networking to enhance the security and resilience of IoT. SDN enhances network resilience and scalability which are essential in large-scale IoT deployments, e.g., smart cities. However, security is a significant concern for IoT while SDN deepens these concerns. SDN itself presents new security threats; specifically, threats related to the controller. We propose a secure, framework for IoT based on SDN. The framework is generalization for the integration of SDN and IoT. We focus on massive IoT deployment, for instance, smart cities applications , where, security is critical, and network traffic is enormous. The study investigates the SDN architecture from a security perspective. Improving SDN security will boost the deployment of SDN-based IoT architecture. We deploy an Intrusion detection system based on Deep Learning (DL). The detection module uses Restricted Boltzmann Machines (RBM). The precision rate shows significant improvements over standard ML, e.g. SVM and PCA.

Deep learning and software-defined networks: Towards secure IoT architecture

Intrusion Detection Systems (IDS) provide substantial measures to protect networks assets. IDSs are software /hardware systems dedicated to exposing network threats. Signature-based, and anomalies detection are conventional approaches applied for the detection. Signaturebased approach inspects the network traffic for a predefined threats signature pattern. This technique suffers limitations in detecting unprecedented attacks. The anomalies detection systems deploy methods to separate the normal and abnormal network traffics. These methods experience inaccurate results, e.g., high false-positives and true- negative alarms. Anomalies detection adopted various methods, for instance, statistical methods, rule-based, and machine learning algorithms. The neural network is one of the machine learning algorithms utilized in intrusion detection, unfortunately, with discouraging accuracy results. Recently, breakthroughs in the neural network were achieved by training deeper neural networks. The approach is known as Deep Learning (DL), it proofed success in several applications domains, e.g., objects and voice recognition. However, there is a limitation on applying deep learning in outliers detection specifically, in network anomalies detection. In this paper, we are revisiting network anomaly detection to explore the potentials of DL for network threats detection . In our study, we focus on unsupervised learning DL algorithms. The study proposes a semi-supervised detection framework based on Unsupervised DL algorithms. The research explores the opportunities and challenges of applying DL to detect anomalies, primarily, autoencoders as a non-probabilistic algorithm. We provide an in-depth-analysis for AE for anomalies detection. Our results show the USDL would enhance detection with accuracy over 99%.

Deep Learning for Network Anomalies Detection

Software-Defined Networks (SDN) initiates a novel networking model. SDN proposes the separation of forward and control planes by introducing a new independent plane called network controller. The architecture enhances the network resilient, decompose management complexity, and support more straightforward network policies enforcement. However, the model suffers from severe security threats. Specifically, a centralized network controller is a precious target for two reasons. First, the controller is located at a central point between the application and data planes. Second, a controller is software which prone to vulnerabilities, e.g., buffer and stack overflow. Hence, providing security measures is a crucial procedure towards the fully unleash of the new model capabilities. Intrusion detection is an option to enhance the networking security. Several approaches were proposed, for instance, signature-based, and anomaly detection. Anomaly detection is a broad approach deployed by various methods, e.g., machine learning. For many decades intrusion detection solution suffers performance and accuracy deficiencies. This paper revisits network anomalies detection as recent advances in machine learning particularly deep learning proofed success in many areas like computer vision and speech recognition. The study proposes an intrusion detection framework based on unsupervised deep learning algorithms.

A Deep Learning Framework to Enhance Software Defined Networks Security

Software-Defined Networks (SDN) initiates a novel networking model. SDN introduces the separation of forwarding and control planes by proposing a new independent plane called network controller. The architecture enhances the network resilient, decompose management complexity, and support more straightforward network policies enforcement. However, the model suffers severe security threats. Specifically, a centralized network controller is a precious target for the attackers for two reasons. First, the controller is located at a central location between the application and data planes. Second, a controller is software which prone to vulnerabilities, e.g., buffer and stack overflow. Hence, providing security measures is a crucial procedure towards the fully unleash of the new model capabilities. Intrusion detection is one option to enhance networking security. Several approaches were proposed, for instance, signature-based, and anomaly detection. Anomaly detection is a broad approach deployed by various methods, e.g., machine learning. For many decades intrusion detection solution suffers performance and accuracy deficiencies. This paper revisits network anomalies detection as recent advances in machine learning particularly deep learning. The study proposes an intrusion detection framework based on unsupervised deep learning algorithms. The framework consists of an unsupervised deep learning phase followed by simple clustering algorithms, e.g. k-means. Our results showed accuracy over 99%, that is a significant improvement in detection accuracy.

Unsupervised Deep Learning for Software Defined Networks Anomalies Detection

Cyber threats are a severed challenge in current communications networks. Several security measures were introduced to at different network layers to enhance security. One of the common networking security solutions is intrusion detection and prevention systems, with more focus on detecting the attacks. Various approaches are being used in network threat detection, for instance, signature-based and anomalies detection methods. Signature-based depends on a database of predefined attacks signature, in operation, the systems compare the traffic against the signature, if a match occurs, then an attack is identified. This approach cannot detect attacks that do not have a signature in the database. The anomalies detection approach utilizing various approaches to define the threats, for instance, statistical, and machine learning algorithms. Several machine learning algorithms had been used for network anomalies detection. A major common deficiency was poor accuracy, which kept the approach not industrially applicable. In this paper, we propose a framework for network anomalies detection. The proposed framework showed improvement in detection accuracy. The framework adopts semi-unsupervised algorithms for novelty detection to tackle the rapid development in the cyber security attacks. The framework embraces the unsupervised deep learning in more elegant technique, where it dramatically reduces the features from the first phase.

Chun Raun

Papers

Deep learning and software-defined networks: Towards secure IoT architecture

Deep Learning for Network Anomalies Detection

A Deep Learning Framework to Enhance Software Defined Networks Security

Unsupervised Deep Learning for Software Defined Networks Anomalies Detection

Dimensionality reduction for network anomalies detection : a deep learning approach