The rapid growth in IoT devices has modernized connectivity across industries. It has also raised severe security concerns regarding IoT networks, making them a prime target of cyber attacks. In this work, we present an in-depth study on the detection of IoT attacks using state-of-the-art machine learning techniques such as Artificial Neural Networks, Random Forests, Extreme Gradient Boosting, and Convolutional Neural Networks. After underlining the careful cleaning and preprocessing of data, we have conducted several simulations on the IoT-23 dataset, which includes many attacks against devices, like Distributed Denial of Service and malware from the Mirai and Okiru botnets. In this paper, we present experimental results that show our approach significantly improves the accuracy and efficiency of attack detection in IoT environments with respect to the literature. Results point out that good data preprocessing lies at the very core of better detection performance; it provides a framework for developing practical, scalable, and reliable security solutions in the future for the IoT.

In recent years, the evolution of machine learning techniques has significantly impacted the field of intrusion de-tection, particularly within the context of the Internet of Things (IoT). As IoT networks expand, the need for robust security measures to counteract potential threats has become increasingly critical. This paper introduces a hybrid Intrusion Detection System (IDS) that synergistically combines Kolmogorov-Arnold Networks (KANs) with the XGBoost algorithm. Our proposed IDS leverages the unique capabilities of KANs, which utilize learnable activation functions to model complex relationships within data, alongside the powerful ensemble learning techniques of XGBoost, known for its high performance in classification tasks. This hybrid approach not only enhances the detection accuracy but also improves the interpretability of the model, making it suitable for dynamic and intricate IoT environments. Experimental evaluations demonstrate that our hybrid IDS achieves an impressive detection accuracy exceeding 99 % in dis-tinguishing between benign and malicious activities. Additionally, we were able to achieve F1-scores, precision, and recall that are exceeding 98%. Furthermore, we conduct a comparative analysis against traditional Multi-Layer Perceptron (MLP) networks, assessing performance metrics such as Precision, Recall, and F1-score. The results underscore the efficacy of integrating KANs with XGBoost, highlighting the potential of this innovative approach to significantly strengthen the security framework of IoT networks.

This paper investigates methods to secure Remote Terminal Units (RTUs) which are the building blocks of a smart grid systems - the next generation version to replace the power grid systems that are being used today. RTUs are identified as the heart of automation and control (SCADA) systems by the systems engineers. As such, security and maintaining nominal operability of such devices has prime importance, especially for the industrial automation networks such as the smart grid. A way of measuring the security of systems and networks is executing a series of cybersecurity weakness assessment tests called penetration testing. Another way of such an assessment is called vulnerability analysis by threat modelling which involves careful investigation and modelling of each and every component of a network/system under investigation. This article, aims at marrying these two methodologies for the vulnerability assessment of the RTUs in a methodological and scientific way.

With the advent of the latest technologies, the Internet of Things (IoT) is the most widely used field in which billions of devices have been connected over the Internet. Long Range Wide Area Network (LoRaWAN) is among one of the most emerging technologies that deliver packets over very long-range with extremely low power consumption. However, it faces the problem of high latency. To cope with this problem, this paper aims to evaluate LoRaWAN in terms of latency by comparing how the default latency method that exists in LoRaWAN fair against an alternative method that has been researched and presented in the literature. Furthermore, having the concrete results of the latency will help companies to determine whether LoRaWAN is a suitable technology to use for Industrial IoT (IIoT) devices. Moreover, due to its backbone TCP/IP communication support, LoRaWAN will also facilitate the future architecture for the Web of Things (WoT).

The increase of cyber-attacks raised security concerns for critical assets worldwide in the last decade. Leading to more efforts spent towards increasing the cyber security among companies and countries. For the sake of enhancing cyber security, representation and testing of attacks have prime importance in understanding system vulnerabilities. One of the available tools for simulating attacks on systems is the Meta Attack Language (MAL), which allows representing the effects of certain cyber-attacks. However, only understanding the component vulnerabilities is not enough in securing enterprise systems. Another important factor is the ‘human‘, which constitutes the biggest ‘insider threat‘. For this, Security Behavior Analysis (SBA) helps understanding which system components that might be directly affected by the ‘human‘. As such, in this work, the authors present an approach for integrating user actions, so called “security behavior”, by mapping SBA to a MAL-based language through MITRE ATT&CK techniques.

Smart grids are a promising upgrade to legacy power grids due to enhanced cooperation of involved parties, such as consumers and utility providers. These newer grids improve the efficiency of electricity generation and distribution by leveraging communication networks to exchange information between those different parties. However, the increased connection and communication also expose the control networks of the power grid to the possibility of cyber-attacks. Therefore, research on cybersecurity for smart grids is crucial to ensure the safe operation of the power grid and to protect the privacy of consumers. In this paper, we investigate the security and privacy challenges of the smart grid; present current solutions to these challenges, especially in the light of intrusion detection systems; and discuss how future grids will create new opportunities for cybersecurity.

Internet of Things (IoT) is in the booming age of its growth, therefore a vast amount of applications, projects, hardware/software solutions, and customized concepts are being developed. The proliferation of IoT will enable location-based services to be available everywhere for everyone, and this will raise a large number of privacy issues related to the collection, usage, retention, and disclosure of the user’s location information. In order to provide a solution to this unique problem of IoT, this paper proposes Location Privacy Assured Internet of Things (LPA-IoT) scheme, which uses the concepts of Mix-Zone, location-obfuscation along with context-awareness. To the authors’ best knowledge, the proposed LPA-IoT scheme is the first location-based privacy-preserving scheme for IoT that provides flexible privacy levels associated with the present context of the user.

Due to their universal accessibility, interactivity and scaling ease, Web applications relying on client-side code execution are currently the most common form of delivering applications and it is ...

In this paper, we evaluate the performance of our Advanced Two Tier User Authentication (ATTUA) scheme [5] which is proposed for heterogeneous Wireless Sensor Networks. ATTUA scheme employs both Public Key Cryptography (PKC) and Secret Key Cryptography (SKC) approaches, such that it takes advantage of both schemes. Our analysis and simulation results have shown that, ATTUA scheme is not only more secure and yet scalable than existing SKC based schemes, but also requires less processing power and provides higher energy efficiency than existing PKC based schemes.

In this paper, we study the adaptive rate transmission with opportunistic scheduling in two-hop relaying networks, in which only channel state information (CSI) at receivers are available. Previous work [5] that proposed an opportunistic relaying scheme has focused on the fixed rate transmission. We develop an adaptive rate transmission scheme according to instantaneous channel connections. The scheme operates with cooperative relays and opportunistic scheduling for adaptive rate transmission, to improve the throughput performance. We obtain the exact throughput expression and we show that the advantage using adaptive rate transmission over fixed rate transmission is the multiplicative factor of log log n, where n is the number of users. Furthermore, we prove that the throughput achieved by the adaptive rate transmission scheme is actually the optimal throughput for two-hop relaying networks. Numerical results are presented, indicating the alignment with our theoretical analysis.

In this paper, we propose a novel User Authentication (UA) scheme for heterogeneous Wireless Sensor Networks (WSNs), which employs both Public Key Cryptography (PKC) and Symmetric Key Cryptography (SKC) approaches, such that it takes advantage of both schemes. Our analysis results have shown that, our scheme is not only more secure and scalable than existing SKC based schemes, but also requires lesser processing power and provides higher energy efficiency than existing PKC based schemes.

In this paper, we investigate applications of access control to Wireless Sensor Networks (WSNs). Although access control was well established for traditional computer systems, it has not been studied thoroughly in WSN environments. We briefly provide the WSN specifications and then stress the constraints of the WSNs that would affect the access control implementation. We also provide a literature overview and discuss further possible solutions.

The quick-start mechanism has been presented as a way for transport control protocols to efficiently use available bandwidth in under-utilized networks. Although studies of performance of quick-start have been carried out, its performance in low latency networks has not been well investigated. In this paper, we show that the benefits of quick-start in low latency networks are minimal. We also show that the improvement in network utilization is much lower than in networks with higher latencies. Our future work in this field will undoubtedly lead to new algorithms and improvements to existing ones.