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The Internet of Things (IoT) is rapidly growing, with an estimated 14.4 billion active endpoints in 2022 and a forecast of approximately 30 billion connected devices by 2027. This proliferation of IoT devices has come with significant security challenges, including intrinsic security vulnerabilities, limited computing power, and the absence of timely security updates. Attacks leveraging such shortcomings could lead to severe consequences, including data breaches and potential disruptions to critical infrastructures. In response to these challenges, this research paper presents the IoT Proxy, a modular component designed to create a more resilient and secure IoT environment, especially in resource-limited scenarios. The core idea behind the IoT Proxy is to externalize security-related aspects of IoT devices by channeling their traffic through a secure network gateway equipped with different Virtual Network Security Functions (VNSFs). Our solution includes a Virtual Private Network (VPN) terminator and an Intrusion Prevention System (IPS) that uses a machine learning-based technique called oblivious authentication to identify connected devices. The IoT Proxy’s modular, scalable, and externalized security approach creates a more resilient and secure IoT environment, especially for resource-limited IoT devices. The promising experimental results from laboratory testing demonstrate the suitability of IoT Proxy to secure real-world IoT ecosystems.

With the rapid progress of network technology, people gradually increase the coverage of the network. Network technology integration in all aspects of people’s lives. Information has become everyone’s important property. Many schools begin to pay attention to information security. In this process, people found some main measures of network security protection[5]. The application of campus security information protection based on virtual private network technology has become a hot topic of experts. This paper describes the characteristics of virtual private network. Finally, this paper puts forward some suggestions on campus information protection.

In COVID-19 Pandemic, Internet traffic has been increased by up to 90%. Work- from-home culture is initiated by almost every organization. The technology adapted to access the Enterprises Intranet is VPN (Virtual Private Network). Infrastructure administrators implemented/updated VPN with the latest versions along with the security scripts to access Intranet. However, the contingencies faced by the organizations are out of their scope. Now VPN security is a big challenge for almost every organization. The Veracity is that no one claims the full prove security system in their Infrastructures. The latest Vulnerabilities have been exposed and indexed in context to VPN Hardware's/Software's/Configurations and Implementations. In this paper, it has been decided to analyze the exposed VPN vulnerabilities, along with the ongoing issues which have not been listed to date through the survey. The mitigation policies have been proposed based on observations.

In an era marked by technological advancement, the rising reliance on Virtual Private Networks (VPNs) necessitates sophisticated forensic analysis techniques to investigate VPN traffic, especially in mobile environments. This research introduces an innovative approach utilizing Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs) for classifying VPN traffic, aiding forensic investigators in precisely identifying applications or websites accessed via VPN connections. By leveraging the combined strengths of CNNs and GNNs, our method provides an effective solution for discerning user activities during VPN sessions. Further extending this framework, we incorporate blockchain technology to meticulously record all mobile VPN transactions, ensuring a tamper-proof and transparent ledger that significantly bolsters the integrity and admissibility of forensic evidence in legal scenarios. A specific use-case demonstrates this methodology in mobile forensics, where our integrated approach not only accurately classifies data traffic but also securely logs transactional details on the blockchain, offering an unprecedented level of detail and reliability in forensic investigations. Extensive real-world VPN dataset experiments validate our approach, highlighting its potential to achieve high accuracy and offering invaluable insights for both technological and legal domains in the context of mobile VPN usage.

Modern organizations have migrated from localized physical offices to work-from-home environments. This surge in remote work culture has exponentially increased the demand for and usage of Virtual Private Networks (VPNs), which permit remote employees to access corporate offices effectively. However, the technology raises concerns, including security threats, latency, throughput, and scalability, among others. These newer-generation threats are more complex and frequent, which makes the legacy approach to security ineffective. This research paper gives an overview of contemporary technologies used across enterprises, including the VPNs, Zero Trust Network Access (ZTNA), proxy servers, Secure Shell (SSH) tunnels, the software-defined wide area network (SD-WAN), and Secure Access Service Edge (SASE). This paper also presents a comprehensive cybersecurity framework named Zero Trust VPN (ZT-VPN), which is a VPN solution based on Zero Trust principles. The proposed framework aims to enhance IT security and privacy for modern enterprises in remote work environments and address concerns of latency, throughput, scalability, and security. Finally, this paper demonstrates the effectiveness of the proposed framework in various enterprise scenarios, highlighting its ability to prevent data leaks, manage access permissions, and provide seamless security transitions. The findings underscore the importance of adopting ZT-VPN to fortify cybersecurity frameworks, offering an effective protection tool against contemporary cyber threats. This research serves as a valuable reference for organizations aiming to enhance their security posture in an increasingly hostile threat landscape.

Remote access technologies encrypt data to enforce policies and ensure protection. Attackers leverage such techniques to launch carefully crafted evasion attacks introducing malware and other unwanted traffic to the internal network. Traditional security controls such as anti-virus software, firewall, and intrusion detection systems (IDS) decrypt network traffic and employ signature and heuristic-based approaches for malware inspection. In the past, machine learning (ML) approaches have been proposed for specific malware detection and traffic type characterization. However, decryption introduces computational overheads and dilutes the privacy goal of encryption. The ML approaches employ limited features and are not objectively developed for remote access security. This paper presents a novel ML-based approach to encrypted remote access attack detection using a weighted random forest (W-RF) algorithm. Key features are determined using feature importance scores. Class weighing is used to address the imbalanced data distribution problem common in remote access network traffic where attacks comprise only a small proportion of network traffic. Results obtained during the evaluation of the approach on benign virtual private network (VPN) and attack network traffic datasets that comprise verified normal hosts and common attacks in real-world network traffic are presented. With recall and precision of 100%, the approach demonstrates effective performance. The results for k-fold cross-validation and receiver operating characteristic (ROC) mean area under the curve (AUC) demonstrate that the approach effectively detects attacks in encrypted remote access network traffic, successfully averting attackers and network intrusions.

The majority of Virtual Private Networks (VPNs) fail when it comes to protecting our privacy. If we are using a VPN to protect our online privacy, many of the well-known VPNs are not secure to use. When examined closely, VPNs can appear to be perfect on the surface but still be a complete privacy and security disaster. Some VPNs will steal our bandwidth, infect our computers with malware, install secret tracking libraries on our devices, steal our personal data, and leave our data exposed to third parties. Generally, Android users should be cautious when installing any VPN software on their devices. As a result, it is important to identify malicious VPNs before downloading and installing them on our Android devices. This paper provides an optimised deep learning neural network for identifying fake VPNs, and VPNs infected by malware based on the permissions of the apps, as well as a novel dataset of malicious and benign Android VPNs. Experimental results indicate that our proposed classifier identifies malicious VPNs with high accuracy, while it outperforms other standard classifiers in terms of evaluation metrics such as accuracy, precision, and recall.

Peer-to-peer (P2P) energy trading is an emerging energy supply paradigm where customers with distributed energy resources (DERs) are allowed to directly trade and share electricity with each other. P2P energy trading can facilitate local power and energy balance, thus being a potential way to manage the rapidly increasing number of DERs in net zero transition. It is of great importance to explore P2P energy trading via public power networks, to which most DERs are connected. Despite the extensive research on P2P energy trading, there has been little large-scale commercial deployment in practice across the world. In this paper, the practical challenges of conducting P2P energy trading via public power networks are identified and presented, based on the analysis of a practical Local Virtual Private Networks (LVPNs) case in North Wales, UK. The ongoing efforts and emerging solutions to tackling the challenges are then summarized and critically reviewed. Finally, the way forward for facilitating P2P energy trading via public power networks is proposed.

The evergrowing diversity of encrypted and anonymous network traffic makes network management more formidable to manage the network traffic. An intelligent system is essential to analyse and identify network traffic accurately. Network management needs such techniques to improve the Quality of Service and ensure the flow of secure network traffic. However, due to the usage of non‐standard ports and encryption of data payloads, the classical port‐based and payload‐based classification techniques fail to classify the secured network traffic. To solve the above‐mentioned problems, this paper proposed an effective deep learning‐based framework employed with flow‐time‐based features to predict heterogeneous secure network traffic best. The state‐of‐the‐art machine learning strategies (C4.5, random forest, and K‐nearest neighbour) are investigated for comparison. The proposed 1D‐CNN model achieved higher accuracy in classifying the heterogeneous secure network traffic. In the next step, the proposed deep learning model characterises the major categories (virtual private network traffic, the onion router network traffic, and plain encrypted network traffic) into several application types. The experimental results show the effectiveness and feasibility of the proposed deep learning framework, which yields improved predictive power compared to the state‐of‐the‐art machine learning techniques employed for secure network traffic analysis.