This document provides updates to IEEE Std 802.16's MIB for the MAC, PHY and asso- ciated management procedures in order to accommodate recent extensions to the standard.

IEEE Standard for Local and Metropolitan Area Networks Part 16: Air Interface for Fixed Broadband Wireless Access Systems Draft Amendment: Management Information Base Extensions

In recent years, technology has advanced to the fourth industrial revolution (Industry 4.0), where the Internet of things (IoTs), fog computing, computer security, and cyberattacks have evolved exponentially on a large scale. The rapid development of IoT devices and networks in various forms generate enormous amounts of data which in turn demand careful authentication and security. Artificial intelligence (AI) is considered one of the most promising methods for addressing cybersecurity threats and providing security. In this study, we present a systematic literature review (SLR) that categorize, map and survey the existing literature on AI methods used to detect cybersecurity attacks in the IoT environment. The scope of this SLR includes an in-depth investigation on most AI trending techniques in cybersecurity and state-of-art solutions. A systematic search was performed on various electronic databases (SCOPUS, Science Direct, IEEE Xplore, Web of Science, ACM, and MDPI). Out of the identified records, 80 studies published between 2016 and 2021 were selected, surveyed and carefully assessed. This review has explored deep learning (DL) and machine learning (ML) techniques used in IoT security, and their effectiveness in detecting attacks. However, several studies have proposed smart intrusion detection systems (IDS) with intelligent architectural frameworks using AI to overcome the existing security and privacy challenges. It is found that support vector machines (SVM) and random forest (RF) are among the most used methods, due to high accuracy detection another reason may be efficient memory. In addition, other methods also provide better performance such as extreme gradient boosting (XGBoost), neural networks (NN) and recurrent neural networks (RNN). This analysis also provides an insight into the AI roadmap to detect threats based on attack categories. Finally, we present recommendations for potential future investigations.

/pdf/detecting-cybersecurity-attacks-in-internet-of-things-using-2lek4rb9.pdf

Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review

The massive modern technical revolution in electronics, cognitive computing, and sensing has provided critical infrastructure for the development of today’s Internet of Things (IoT) for a wide range of applications. However, because endpoint devices’ computing, storage, and communication capabilities are limited, IoT infrastructures are exposed to a wide range of cyber-attacks. As such, Darknet or blackholes (sinkholes) attacks are significant, and recent attack vectors that are launched against several IoT communication services. Since Darknet address space evolved as a reserved internet address space that is not contemplated to be used by legitimate hosts globally, any communication traffic is speculated to be unsolicited and distinctively deemed a probe, backscatter, or misconfiguration. Thus, in this paper, we develop, investigate, and evaluate the performance of machine-learning-based Darknet traffic detection systems (DTDS) in IoT networks. Mainly, we make use of six supervised machine-learning techniques, including bagging decision tree ensembles (BAG-DT), AdaBoost decision tree ensembles (ADA-DT), RUSBoosted decision tree ensembles (RUS-DT), optimizable decision tree (O-DT), optimizable k-nearest neighbor (O-KNN), and optimizable discriminant (O-DSC). We evaluate the implemented DTDS models on a recent and comprehensive dataset, known as the CIC-Darknet-2020 dataset, composed of contemporary actual IoT communication traffic involving four different classes that combine VPN and Tor traffic in a single dataset covering a wide range of captured cyber-attacks and hidden services provided by the Darknet. Our empirical performance analysis demonstrates that bagging ensemble techniques (BAG-DT) offer better accuracy and lower error rates than other implemented supervised learning techniques, scoring a 99.50% of classification accuracy with a low inferencing overhead of 9.09 µ second. Finally, we also contrast our BAG-DT-DTDS with other existing DTDS models and demonstrate that our best results are improved by (1.9~27%) over the former state-of-the-art models.

/pdf/machine-learning-based-darknet-traffic-detection-system-for-2oss9gp6.pdf

Machine-Learning-Based Darknet Traffic Detection System for IoT Applications

In today’s Industrial Internet of Things (IIoT) environment, where different systems interact with the physical world, the state proposed by the Industry 4.0 standards can lead to escalating vulnerabilities, especially when these systems receive data streams from multiple intermediaries, requiring multilevel security approaches, in addition to link encryption. At the same time taking into account the heterogeneity of the systems included in the IIoT ecosystem and the non-institutionalized interoperability in terms of hardware and software, serious issues arise as to how to secure these systems. In this framework, given that the protection of industrial equipment is a requirement inextricably linked to technological developments and the use of the IoT, it is important to identify the major vulnerabilities and the associated risks and threats and to suggest the most appropriate countermeasures. In this context, this study provides a description of the attacks against IIoT systems, as well as a thorough analysis of the solutions for these attacks, as they have been proposed in the most recent literature.

/pdf/cyber-threats-to-industrial-iot-a-survey-on-attacks-and-3q7po5j93y.pdf

Cyber Threats to Industrial IoT: A Survey on Attacks and Countermeasures

Security attacks on legitimate websites to steal users’ information, known as phishing attacks, have been increasing. This kind of attack does not just affect individuals’ or organisations’ websites. Although several detection methods for phishing websites have been proposed using machine learning, deep learning, and other approaches, their detection accuracy still needs to be enhanced. This paper proposes an optimized stacking ensemble method for phishing website detection. The optimisation was carried out using a genetic algorithm (GA) to tune the parameters of several ensemble machine learning methods, including random forests, AdaBoost, XGBoost, Bagging, GradientBoost, and LightGBM. The optimized classifiers were then ranked, and the best three models were chosen as base classifiers of a stacking ensemble method. The experiments were conducted on three phishing website datasets that consisted of both phishing websites and legitimate websites—the Phishing Websites Data Set from UCI (Dataset 1); Phishing Dataset for Machine Learning from Mendeley (Dataset 2, and Datasets for Phishing Websites Detection from Mendeley (Dataset 3). The experimental results showed an improvement using the optimized stacking ensemble method, where the detection accuracy reached 97.16%, 98.58%, and 97.39% for Dataset 1, Dataset 2, and Dataset 3, respectively.

An optimized stacking ensemble model for phishing websites detection

Attackers are perpetually modifying their tactics to avoid detection and frequently leverage legitimate credentials with trusted tools already deployed in a network environment, making it difficult for organizations to proactively identify critical security risks. Network traffic analysis products have emerged in response to attackers’ relentless innovation, offering organizations a realistic path forward for combatting creative attackers. Additionally, thanks to the widespread adoption of cloud computing, Device Operators (DevOps) processes, and the Internet of Things (IoT), maintaining effective network visibility has become a highly complex and overwhelming process. What makes network traffic analysis technology particularly meaningful is its ability to combine its core capabilities to deliver malicious intent detection. In this paper, we propose a novel darknet traffic analysis and network management framework to real-time automating the malicious intent detection process, using a weight agnostic neural networks architecture. It is an effective and accurate computational intelligent forensics tool for network traffic analysis, the demystification of malware traffic, and encrypted traffic identification in real time. Based on a weight agnostic neural networks (WANNs) methodology, we propose an automated searching neural net architecture strategy that can perform various tasks such as identifying zero-day attacks. By automating the malicious intent detection process from the darknet, the advanced proposed solution is reducing the skills and effort barrier that prevents many organizations from effectively protecting their most critical assets.

https://www.mdpi.com/2079-9292/10/7/781/pdf

Darknet Traffic Big-Data Analysis and Network Management for Real-Time Automating of the Malicious Intent Detection Process by a Weight Agnostic Neural Networks Framework

An important application for the IEEE 802.16 technology (also called WiMAX) is to provide high-speed access to the Internet where the transmission control protocol (TCP) is the core transport protocol. In this paper we study through extensive simulation scenarios the performance characteristics of five representative TCP schemes, namely, TCP New Reno, Vegas, Veno, Westwood, and BIC, in WiMAX (and WLANs) networks, under the conditions of correlated wireless errors, asymmetric end-to-end capabilities, and link congestion. The target is to evaluate how the above conditions would affect the TCP congestion control and suggest the best schemes to be employed in WiMAX networks.

/pdf/comparative-performance-evaluation-of-tcp-variants-in-wimax-4f3qi9sal2.pdf

Comparative Performance Evaluation of TCP Variants in WiMAX (and WLANs) Network Configurations

An important application for the IEEE 802.16 technology (also called WiMAX) it to provide high-speed access to the Internet where the transmission control protocol (TCP) is the core transport protocol. In this paper we study through extensive simulation scenarios, the performance characteristics of five representative TCP schemes, namely TCP New Reno, Vegas, Veno, Westwood and BIC, in WiMAX networks, under the conditions of correlated wireless errors, asymmetric end-to-end capabilities and link congestion. The target is to evaluate how the above conditions would affect the TCP congestion control, and suggest the best schemes to be employed in WiMAX Networks.

Konstantinos Tsiknas

Papers

Cyber Threats to Industrial IoT: A Survey on Attacks and Countermeasures

Cyber Threats to Industrial IoT: A Survey on Attacks and Countermeasures

Darknet Traffic Big-Data Analysis and Network Management for Real-Time Automating of the Malicious Intent Detection Process by a Weight Agnostic Neural Networks Framework

Comparative Performance Evaluation of TCP Variants in WiMAX (and WLANs) Network Configurations

Performance evaluation of TCP in IEEE 802.16 networks