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.

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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

Software-Defined Network (SDN) has been developed to reduce network complexity through control and manage the whole network from a centralized location. Today, SDN is widely implemented in many data center's network environments. Nevertheless, emerging technology itself can lead to many vulnerabilities and threats which are still challenging for manufacturers to address it. Therefore, deploying Intrusion Detection Systems (IDSs) to monitor malicious activities is a crucial part of the network architecture. Although the centralized view of the SDN network creates new opportunities for the implementation of IDSs, the performance of these detection techniques relies on the quality of the training datasets. Unfortunately, there are no publicly available datasets that can be used directly for anomaly detection systems applied in SDN networks. The majority of the published studies use non-compatible and outdated datasets, such as the KDD'99 dataset. This manuscript aims to generate an attack-specific SDN dataset and it is publicly available to the researchers. To the best of our knowledge, our work is one of the first solutions to produce a comprehensive SDN dataset to verify the performance of intrusion detection systems. The new dataset includes the benign and various attack categories that can occur in the different elements of the SDN platform. Further, we demonstrate the use of our proposed dataset by performing an experimental evaluation using eight popular machine-learning-based techniques for IDSs.

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InSDN: A Novel SDN Intrusion Dataset

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

Background and Aim: deep learning has not been successfully implemented in liver tumour feature extraction and classification using computer-aided diagnosis. This study aims to enhance classification accuracy and improves the processing time to better differentiate tumour types. Methodology: This study proposed a hybrid model, which combines the regularization function with the current loss function for the support vector machine (SVM) classifier. Regularization function is used for prioritizing image classes before feeding it to the linear mapping. The proposed model consists of the region growing algorithm to get the region-of-interest (ROI), and Weiner filtering algorithm for image enhancement and noise removal. The gray level co-occurrence matrix (GLCM) was performed to extract the feature from the image. The extracted feature then fed to SVM classifier using selected feature vectors to classify the affected region and neglecting the unwanted areas. Results: classification accuracy was calculated using probability score, and the processing time was calculated based on the total execution time. The proposed system was able to achieve an average classification accuracy of 98.9%, which is about 2–3% higher than the current system. The results showed that 12 ms reduced the processing time on average. Conclusion: The proposed system focused on improving feature extraction and classification for different types of tumours from the MRI images. The study solved the problem in linear mapping of support vector machine and enhanced the classification accuracy and the processing time of early diagnosis of three different types of tumours in liver MRI images.

Deep learning for liver tumour classification: enhanced loss function

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.

Ahmed Dawoud

Papers

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

Deep Learning for Network Anomalies Detection

Deep learning for liver tumour classification: enhanced loss function

A Deep Learning Framework to Enhance Software Defined Networks Security

Unsupervised Deep Learning for Software Defined Networks Anomalies Detection