Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Network intrusion detection systems (NIDSs) play a crucial role in defending computer networks. However, there are concerns regarding the feasibility and sustainability of current approaches when faced with the demands of modern networks. More specifically, these concerns relate to the increasing levels of required human interaction and the decreasing levels of detection accuracy. This paper presents a novel deep learning technique for intrusion detection, which addresses these concerns. We detail our proposed nonsymmetric deep autoencoder (NDAE) for unsupervised feature learning. Furthermore, we also propose our novel deep learning classification model constructed using stacked NDAEs. Our proposed classifier has been implemented in graphics processing unit (GPU)-enabled TensorFlow and evaluated using the benchmark KDD Cup ’99 and NSL-KDD datasets. Promising results have been obtained from our model thus far, demonstrating improvements over existing approaches and the strong potential for use in modern NIDSs.

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A Deep Learning Approach to Network Intrusion Detection

Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset

The Internet Protocol.

The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resource-constrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy issues in IoT networks primarily through traditional cryptographic approaches. However, the unique characteristics of IoT nodes render the existing solutions insufficient to encompass the entire security spectrum of the IoT networks. Machine Learning (ML) and Deep Learning (DL) techniques, which are able to provide embedded intelligence in the IoT devices and networks, can be leveraged to cope with different security problems. In this paper, we systematically review the security requirements, attack vectors, and the current security solutions for the IoT networks. We then shed light on the gaps in these security solutions that call for ML and DL approaches. Finally, we discuss in detail the existing ML and DL solutions for addressing different security problems in IoT networks. We also discuss several future research directions for ML- and DL-based IoT security.

Machine Learning in IoT Security: Current Solutions and Future Challenges

Internet of Things (IoT) consists of smart devices that communicate with each other. It enables these devices to collect and exchange data. Besides, IoT has now a wide range of life applications such as industry, transportation, logistics, healthcare, smart environment, as well as personal, social gaming robot, and city information. Smart devices can have wired or wireless connection. As far as the wireless IoT is the main concern, many different wireless communication technologies and protocols can be used to connect the smart device such as Internet Protocol Version 6 (IPv6), over Low power Wireless Personal Area Networks (6LoWPAN), ZigBee, Bluetooth Low Energy (BLE), Z-Wave and Near Field Communication (NFC). They are short range standard network protocols, while SigFox and Cellular are Low Power Wide Area Network (LPWAN).standard protocols. This paper will be an attempt to review different communication protocols in IoT. In addition, it will compare between commonly IoT communication protocols, with an emphasis on the main features and behaviors of various metrics of power consumption security spreading data rate, and other features. This comparison aims at presenting guidelines for the researchers to be able to select the right protocol for different applications.

Internet of Things (IoT) communication protocols: Review

Vehicular ad hoc networks (VANETs) have become increasingly common in recent years due to their critical role in the field of smart transportation by supporting Vehicle-to-Vehicle and Vehicle-to-Infrastructure communication. The security and privacy of VANET are of the utmost importance due to the use of an open wireless communication medium where messages are exchanged in plain text, something which allows attackers to intercept, tamper, replay, and delete them. Hence, there is a high probability that the safety of a VANET-based smart transportation system could be compromised. Nowadays, securing and safeguarding the exchange of messages in VANETs is the focus of many security research teams, as reflected by the number of authentication and privacy schemes that have been proposed. However, these schemes have not fulfilled all aspects of the security and privacy requirements. The present paper is an effort to provide a thorough background on VANETs and their components; various types of attacks on them; and all the security and privacy requirements for authentication and privacy schemes for VANETs. This paper is among the first to provide a comprehensive survey of the existing authentication and privacy schemes and compare them based on all security and privacy requirements, computational and communicational overheads, and the level of resistance to different types of attacks. It also provides a qualitative comparison with the existing surveys. This paper could serve as a guide and reference in the design and development of any new security and privacy techniques for VANETs.

Survey of Authentication and Privacy Schemes in Vehicular ad hoc Networks

Internet of Things (IoT) environment consists of an enormous smart device that communicate with each other, so they can gather and transmit the sensing information into IoT network. Besides, IoT has now a wide range of live applications such as transportation, smart home, healthcare, industry, smart environment, smart city as well social gaming robots and personal. In parallel, Industrial Internet of Things (IIoT) is empowered by more momentum as businesses integration between Artificial Intelligence (AI), Big-data and IoT technologies. According to the International Data Corporation (IDC), the worldwide market for IoT solutions including intelligent systems, connectivity services, infrastructure, applications, security, analytics, and professional services has observed an annual growth rate of 20% CAGR (Compound Annual Growth Rate). Furthermore, IoT will play a crucial role in the near future as per the worldwide statistics. It is expected that there will be a considerable amount of cash flow through the worldwide market in the upcoming years for IoT. At the same time, this paper explicitly provides forecast statistics between 2020 till 2030, as it helps organizations and decision makers in many areas, such as industrial and manufacturing, healthcare and lifestyle, energy and utilization, and many other areas related to IoT spending by sector that show expected growth forecasted in this paper.

Internet of Things Market Analysis Forecasts, 2020–2030

Impact of Coronavirus Pandemic Crisis on Technologies and Cloud Computing Applications

One critical issue within network security refers to intrusion detection. The nature of intrusion attempts appears to be nonlinear, wherein the network traffic performance is unpredictable, and the problematic space features are numerous. These make intrusion detection systems (IDSs) a challenge within the research arena. Hence, selecting the essential aspects for intrusion detection is crucial in information security and with that, this study identified the related features in building a computationally efficient and effective intrusion system. Accordingly, a modified feature selection (FS) algorithm called modified binary grey wolf optimisation (MBGWO) is proposed in this study. The proposed algorithm is based on binary grey wolf optimisation to boost the performance of IDS. The new FS algorithm selected an optimal number of features. In order to evaluate the proposed algorithm, the benchmark of NSL-KDD network intrusion, which was modified from 99-data set KDD cup to assess issues linked with IDS, had been applied in this study. Additionally, the support vector machine was employed to classify the data set effectively. The proposed FS and classification algorithms enhanced the performance of the IDS in detecting attacks. The simulation outcomes portrayed that the proposed algorithm enhanced the accuracy of intrusion detection up to 99.22% and reduction in the number of features from 41 to 14.

Mohammed Anbar

Papers

Internet of Things (IoT) communication protocols: Review

Survey of Authentication and Privacy Schemes in Vehicular ad hoc Networks

Internet of Things Market Analysis Forecasts, 2020–2030

Impact of Coronavirus Pandemic Crisis on Technologies and Cloud Computing Applications

Intrusion detection system based on a modified binary grey wolf optimisation