Recently, networks have shifted from traditional in-house servers to third-party-managed cloud platforms due to its cost-effectiveness and increased accessibility toward its management. However, the network remains reactive, with less accountability and oversight of its overall security. Several emerging technologies have restructured our approach to the security of cloud networks; one such approach is the zero-trust network architecture (ZTNA), where no entity is implicitly trusted in the network, regardless of its origin or scope of access. The network rewards trusted behaviour and proactively predicts threats based on its users’ behaviour. The zero-trust network architecture is still at a nascent stage, and there are many frameworks and models to follow. The primary focus of this survey is to compare the novel requirement-specific features used by state-of-the-art research models for zero-trust cloud networks. In this manner, the features are categorized across nine parameters into three main types: zero-trust-based cloud network models, frameworks and proofs-of-concept. ZTNA, when wholly realized, enables network administrators to tackle critical issues such as how to inhibit internal and external cyber threats, enhance the visibility of the network, automate the calculation of trust for network entities and orchestrate security for users. The paper further focuses on domain-specific issues plaguing modern cloud computing networks, which leverage choosing and implementing features necessary for future networks and incorporate intelligent security orchestration, automation and response. The paper also discusses challenges associated with cloud platforms and requirements for migrating to zero-trust architecture. Finally, possible future research directions are discussed, wherein new technologies can be incorporated into the ZTA to build robust trust-based enterprise networks deployed in the cloud.

The 5G networks aim to realize a massive Internet of Things (IoT) environment with low latency. IoT devices with weak security can cause Tbps-level Distributed Denial of Service (DDoS) attacks on 5G mobile networks. Therefore, interest in automatic network intrusion detection using machine learning (ML) technology in 5G networks is increasing. ML-based DDoS attack detection in a 5G environment should provide ultra-low latency. To this end, utilizing a feature-selection process that reduces computational complexity and improves performance by identifying features important for learning in large datasets is possible. Existing ML-based DDoS detection technology mostly focuses on DDoS detection learning models on the wired Internet. In addition, studies on feature engineering related to 5G traffic are relatively insufficient. Therefore, this study performed feature selection experiments to reduce the time complexity of detecting and analyzing large-capacity DDoS attacks in real time based on ML in a 5G core network environment. The results of the experiment showed that the performance was maintained and improved when the feature selection process was used. In particular, as the size of the dataset increased, the difference in time complexity increased rapidly. The experiments show that the real-time detection of large-scale DDoS attacks in 5G core networks is possible using the feature selection process. This demonstrates the importance of the feature selection process for removing noisy features before training and detection. As this study conducted a feature study to detect network traffic passing through the 5G core with low latency using ML, it is expected to contribute to improving the performance of the 5G network DDoS attack automation detection technology using AI technology.

Recently, users have used open‐source intelligence (OSINT) to gather and obtain information regarding the data of interest. The advantage of using data gathered by OSINT is that security threats arising in cyberspace can be addressed. However, if a user uses data collected by OSINT for malicious purposes, information regarding the target of an attack can be gathered, which may lead to various cybercrimes, such as hacking, malware, and a denial‐of‐service attack. Therefore, from a cybersecurity point of view, it is important to positively use the data gathered by OSINT in a positive manner. If exploited in a negative manner, it is important to prepare countermeasures that can minimize the damage caused by cybercrimes. In this paper, the current status and security trends of OSINT will be explained. Specifically, we present security threats and cybercrimes that may occur if data gathered by OSINT are exploited by malicious users. Furthermore, to solve this problem, we propose security requirements that can be applied to the OSINT environment. The proposed security requirements are necessary for securely gathering and storing data in the OSINT environment and for securely accessing and using the data collected by OSINT. The goal of the proposed security requirements is to minimize the damage when cybercrimes occur in the OSINT environment.

Recently, the use of IoT devices in living environments has increased due to the development of various wireless communication technologies. As the number of types of IoT devices has grown exponentially, many kinds of insecure operating systems and open source software programs are being used. As they run with security vulnerabilities, IoT devices using such OS and software can be targeted by malicious attackers. In addition, due to the operating characteristics of IoT devices, it is difficult to apply security patches immediately when vulnerabilities are found. Accordingly, there is an increasing need for managing and sharing cyber security threat information in order to prevent security threats and accidents. This paper suggests a platform structure and application method for collecting, analyzing and sharing vulnerability information about IoT devices.

Since 2016, Mirai and Persirai malware have infected hundreds of thousands of Internet of Things (IoT) devices and created a massive IoT botnet, which caused distributed denial of service (DDoS) attacks. IoT malware targets vulnerable IoT devices, which are vulnerable to security risks. Techniques are needed to prevent IoT devices from being exploited by attackers. However, unlike high-performance PCs, IoT devices are lightweight, low-power, and low-cost, having performance limitations regarding processing and memory, which makes it difficult to install security and anti-malware programs. Recently, several studies have been attempted to quickly search for vulnerable internet-connected devices to solve this real issue. Issues yet to be studied still exist regarding these types of internet-wide scan technologies, such as filtering by security devices and a shortage of collected operating system (OS) information. This paper proposes an intelligent internet-wide scan model that improves IP state scanning with advanced internet protocol (IP) randomization, reactive protocol (port) scanning, and OS fingerprinting scanning, applying k* algorithm in order to find vulnerable IoT devices. Additionally, we describe the experiment’s results compared to the existing internet-wide scan technologies, such as ZMap and Shodan. As a result, the proposed model experimentally shows improved performance. Although we improved the ZMap, the throughput per minute (TPM) performance is similar to ZMap without degrading the IP scan throughput and the performance of generating a single IP address is about 118% better than ZMap. In the protocol scan performance experiments, it is about 129% better than the Censys based ZMap, and the performance of OS fingerprinting is better than ZMap, with about 50% accuracy.

As hacking techniques become more sophisticated, vulnerabilities have been gradually increasing. Between 2010 and 2015, around 80,000 vulnerabilities were newly registered in the CVE (Common Vulnerability Enumeration), and the number of vulnerabilities has continued to rise. While the number of vulnerabilities is increasing rapidly, the response to them relies on manual analysis, resulting in a slow response speed. It is necessary to develop techniques that can detect and patch vulnerabilities automatically. This paper introduces a trend of techniques and tools related to automated vulnerability detection and remediation. We propose an automated vulnerability detection method based on binary complexity analysis to prevent a zero-day attack. We also introduce an automatic patch generation method through PLT/GOT table modification to respond to zero-day vulnerabilities.