The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. IoT is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. However, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network and application security for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to effectively secure the IoT ecosystem. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory novelty to practical machinery in several important applications. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Security has become the primary concern in many telecommunications industries today as risks can have high consequences. Especially, as the core and enable technologies will be associated with 5G network, the confidential information will move at all layers in future wireless systems. Several incidents revealed that the hazard encountered by an infected wireless network, not only affects the security and privacy concerns, but also impedes the complex dynamics of the communications ecosystem. Consequently, the complexity and strength of security attacks have increased in the recent past making the detection or prevention of sabotage a global challenge. From the security and privacy perspectives, this paper presents a comprehensive detail on the core and enabling technologies, which are used to build the 5G security model; network softwarization security, PHY (Physical) layer security and 5G privacy concerns, among others. Additionally, the paper includes discussion on security monitoring and management of 5G networks. This paper also evaluates the related security measures and standards of core 5G technologies by resorting to different standardization bodies and provide a brief overview of 5G standardization security forces. Furthermore, the key projects of international significance, in line with the security concerns of 5G and beyond are also presented. Finally, a future directions and open challenges section has included to encourage future research.

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A Survey on Security and Privacy of 5G Technologies: Potential Solutions, Recent Advancements, and Future Directions

Wireless Sensor Networks (WSNs) constitute one of the most promising third-millennium technologies and have wide range of applications in our surrounding environment. The reason behind the vast adoption of WSNs in various applications is that they have tremendously appealing features, e.g., low production cost, low installation cost, unattended network operation, autonomous and longtime operation. WSNs have started to merge with the Internet of Things (IoT) through the introduction of Internet access capability in sensor nodes and sensing ability in Internet-connected devices. Thereby, the IoT is providing access to huge amount of data, collected by the WSNs, over the Internet. Hence, the security of IoT should start with foremost securing WSNs ahead of the other components. However, owing to the absence of a physical line-of-defense, i.e., there is no dedicated infrastructure such as gateways to watch and observe the flowing information in the network, security of WSNs along with IoT is of a big concern to the scientific community. More specifically, for the application areas in which CIA (confidentiality, integrity, availability) has prime importance, WSNs and emerging IoT technology might constitute an open avenue for the attackers. Besides, recent integration and collaboration of WSNs with IoT will open new challenges and problems in terms of security. Hence, this would be a nightmare for the individuals using these systems as well as the security administrators who are managing those networks. Therefore, a detailed review of security attacks towards WSNs and IoT, along with the techniques for prevention, detection, and mitigation of those attacks are provided in this paper. In this text, attacks are categorized and treated into mainly two parts, most or all types of attacks towards WSNs and IoT are investigated under that umbrella: “Passive Attacks” and “Active Attacks”. Understanding these attacks and their associated defense mechanisms will help paving a secure path towards the proliferation and public acceptance of IoT technology.

/pdf/security-of-the-internet-of-things-vulnerabilities-attacks-5f8nyperis.pdf

Security of the Internet of Things: Vulnerabilities, Attacks, and Countermeasures

As the deployment of Internet of Things (IoT) is experiencing an exponential growth, it is no surprise that many recent cyber attacks are IoT-enabled : the attacker initially exploits some vulnerable IoT technology as a first step toward compromising a critical system that is connected, in some way, with the IoT. For some sectors, like industry, smart grids, transportation, and medical services, the significance of such attacks is obvious, since IoT technologies are part of critical back-end systems. However, in sectors where IoT is usually at the end-user side, like smart homes, such attacks can be underestimated, since not all possible attack paths are examined. In this paper, we survey IoT-enabled cyber attacks, found in all application domains since 2010. For each sector, we emphasize on the latest, verified IoT-enabled attacks, based on known real-world incidents and published proof-of-concept attacks. We methodologically analyze representative attacks that demonstrate direct, indirect, and subliminal attack paths against critical targets. Our goal is threefold: 1) to assess IoT-enabled cyber attacks in a risk-like approach, in order to demonstrate their current threat landscape; 2) to identify hidden and subliminal IoT-enabled attack paths against critical infrastructures and services; and 3) to examine mitigation strategies for all application domains.

A Survey of IoT-Enabled Cyberattacks: Assessing Attack Paths to Critical Infrastructures and Services

Machine learning based solutions for security of Internet of Things (IoT): A survey

The Internet of Things (IoT) comprises a complex network of smart devices, which frequently exchange data through the Internet. Given the significant growth of IoT as a new technological paradigm, which may involve safety-critical operations and sensitive data to be put online, its security aspect is vital. This paper studies the network security matters in the smart home, health care and transportation domains. It is possible that the interruption might occur in IoT devices during operation causing them to be in the shutdown mode. Taxonomy of security attacks within IoT networks is constructed to assist IoT developers for better awareness of the risk of security flaws so that better protections shall be incorporated.

Internet of Things (IoT): Taxonomy of security attacks

Network anomaly detection system enables to monitor computer network that behaves differently from the network protocol and it is many implemented in various domains. Yet, the problem arises where different application domains have different defining anomalies in their environment. These make a difficulty to choose the best algorithms that suit and fulfill the requirements of certain domains and it is not straightforward. Additionally, the issue of centralization that cause fatal destruction of network system when powerful malicious code injects in the system. Therefore, in this paper we want to conduct experiment using supervised Machine Learning (ML) for network anomaly detection system that low communication cost and network bandwidth minimized by using UNSW-NB15 dataset to compare their performance in term of their accuracy (effective) and processing time (efficient) for a classifier to build a model. Supervised machine learning taking account the important features by labelling it from the datasets. The best machine learning algorithm for network dataset is AODE with a comparable accuracy is 97.26% and time taken approximately 7 seconds. Also, distributed algorithm solves the issue of centralization with the accuracy and processing time still a considerable compared to a centralized algorithm even though a little drop of the accuracy and a bit longer time needed.

http://journal.portalgaruda.org/index.php/EEI/article/download/1387/1545

Effective and efficient network anomaly detection system using machine learning algorithm

The rapid growth of technologies might endanger them to various network attacks due to the nature of data which are frequently exchange their data through Internet and large-scale data that need to be handle. Moreover, network anomaly detection using machine learning faced difficulty when dealing the involvement of dataset where the number of labelled network dataset is very few in public and this caused many researchers keep used the most commonly network dataset (KDDCup99) which is not relevant to employ the machine learning (ML) algorithms for a classification. Several issues regarding these available labelled network datasets are discussed in this paper. The aim of this paper to build a network anomaly detection system using machine learning algorithms that are efficient, effective and fast processing. The finding showed that AODE algorithm is performed well in term of accuracy and processing time for binary classification towards UNSW-NB15 dataset.

https://iopscience.iop.org/article/10.1088/1742-6596/1018/1/012015/pdf

Performances of Machine Learning Algorithms for Binary Classification of Network Anomaly Detection System

Wireless Sensor Networks (WSN) consists of sensor nodes for monitoring and reporting sensible changes on a field to a specific server One of the applications of WSN is large area monitoring, where sensor nodes are placed in far fields with limited power sources Due to the adhered reason, the energy consumption of sensor nodes is considered as one of the major challenge in WSN Many factor in WSN contributes to energy consumption such as Medium Access Control protocol (MAC), the network topology, and routing protocol With the variety of factors that affects the energy consumption in WSN; the challenge of optimizing WSN networks toward a low energy consumption is becoming a hard problem In the literature many efforts are paid for designing, implementing, and improving protocols in terms of power consumption However, few efforts are paid for optimizing the existing protocols and other network parameters toward a green technology This paper focuses in WSN infrastructure and protocols optimization by introducing the Ichi Taguchi (Taguchi) optimization method Taguchi method is used to predict the best design parameters to achieve optimal performance parameters Moreover, Taguchi method is used to optimize the energy consumed by sensor nodes against network protocols and network topology design parameters A simulation experiments are curried out on the discrete event simulator OMNET++ for the purposes of this research paper The obtained results show the impact of the network protocols toward the energy consumption Furthermore, a proposed network topology and protocols set is introduced, and compared against the existing once

Energy consumption optimization with Ichi Taguchi method for Wireless Sensor Networks

The 5G LTE-Advanced with Carrier Aggregation (CA) technology is a promising next generation of all IP mobile networks which has been invented and developed by 3GPP. The CA technique is very vital to support the 5G LTE-A network with unprecedented speed of data transmission and minimal latency. One of the main challenges in the presence of CA is to offer robust and suitable resource scheduling scheme in order to support and fulfill the user level Quality-of-Services (QoS) requirements for diversified services “Real-Time (RT) and Non-Real-Time (NRT) traffic”. In this paper, a User-Level Quality of Service aware packet scheduling for OFDMA resource blocks in 5G LTE-A system with CA scheme is designed. An improved Joint User Carrier Scheduling algorithm (IJUCS) suitable to various traffic users is proposed by introducing the service weight factor. The design objective is to guarantee the user quality of service (QoS) and equitable allocation of radio resources among users. The simulation outcome demonstrates that proposed IJUCS has mitigate average packet delay, Packet Drop Rate (PDR), and variation of average packet drop (STD) of real-time users as compared to the conventional JUCS and fulfils the user's throughput requirements of NRT streaming video traffic.

Ong Bi Lynn

Papers

Internet of Things (IoT): Taxonomy of security attacks

Effective and efficient network anomaly detection system using machine learning algorithm

Performances of Machine Learning Algorithms for Binary Classification of Network Anomaly Detection System

Energy consumption optimization with Ichi Taguchi method for Wireless Sensor Networks

An efficient scheduling scheme for heterogeneous services in OFDMA based 5G LTE-Advanced network with carrier aggregation