Cybersecurity is an increasingly critical aspect of modern society, with cyber attacks becoming more sophisticated and frequent. Artificial intelligence (AI) and neural network models have emerged as promising tools for improving cyber defense. This paper explores the potential of AI and neural network models in cybersecurity, focusing on their applications in intrusion detection, malware detection, and vulnerability analysis. Intruder detection, or "intrusion detection," is the process of identifying Invasion of Privacy to a computer system. AI-based security systems that can spot intrusions (IDS) use AI-powered packet-level network traffic analysis and intrusion detection patterns to signify an assault. Neural network models can also be used to improve IDS accuracy by modeling the behavior of legitimate users and detecting anomalies. Malware detection involves identifying malicious software on a computer system. AI-based malware machine-learning algorithms are used by detecting systems to assess the behavior of software and recognize patterns that indicate malicious activity. Neural network models can also serve to hone the precision of malware identification by modeling the behavior of known malware and identifying new variants. Vulnerability analysis involves identifying weaknesses in a computer system that could be exploited by attackers. AI-based vulnerability analysis systems use machine learning algorithms to analyze system configurations and identify potential vulnerabilities. Neural network models can also be used to improve the accuracy of vulnerability analysis by modeling the behavior of known vulnerabilities and identifying new ones. Overall, AI and neural network models have significant potential in cybersecurity. By improving intrusion detection, malware detection, and vulnerability analysis, they can help organizations better defend against cyber attacks. However, these technologies also present challenges, including a lack of understanding of the importance of data in machine learning and the potential for attackers to use AI themselves. As such, careful consideration is necessary when implementing AI and neural network models in cybersecurity.

As vehicles increasingly embed digital systems, new security vulnerabilities are also being introduced. Computational constraints make it challenging to add security oversight layers on top of core vehicle systems, especially when the security layers rely on additional deep learning models for anomaly detection. To improve security-aware decision-making for autonomous vehicles (AV), this paper proposes a bi-level security framework. The first security level consists of a one-shot resource allocation game that enables a single vehicle to fend off an attacker by optimizing the configuration of its intrusion prevention system based on risk estimation. The second level relies on a reinforcement learning (RL) environment where an agent is responsible for forming and managing a platoon of vehicles on the fly while also dealing with a potential attacker. We solve the first problem using a minimax algorithm to identify optimal strategies for each player. Then, we train RL agents and analyze their performance in forming security-aware platoons. The trained agents demonstrate superior performance compared to our baseline strategies that do not consider security risk.

The last decade has shown that networked cyberphysical systems (NCPS) are the future of critical infrastructure such as transportation systems and energy production. However, they have introduced an uncharted territory of security vulnerabilities and a wider attack surface, mainly due to network openness and the deeply integrated physical and cyber spaces. On the other hand, relying on manual analysis of intrusion detection alarms might be effective in stopping run-of-the-mill automated probes but remain useless against the growing number of targeted, persistent, and often AI-enabled attacks on large-scale NCPS. Hence, there is a pressing need for new research directions to provide advanced protection. This paper introduces a novel security paradigm for emerging NCPS, namely Autonomous CyberPhysical Defense (ACPD). We lay out the theoretical foundations and describe the methods for building autonomous and stealthy cyber-physical defense agents that are able to dynamically hunt, detect, and respond to intelligent and sophisticated adversaries in real time without human intervention. By leveraging the power of game theory and multi-agent reinforcement learning, these selflearning agents will be able to deploy complex cyber-physical deception scenarios on the fly, generate optimal and adaptive security policies without prior knowledge of potential threats, and defend themselves against adversarial learning. Nonetheless, serious challenges including trustworthiness, scalability, and transfer learning are yet to be addressed for these autonomous agents to become the next-generation tools of cyber-physical defense.

Intelligent Data and Security - The recent 5G networks aim to provide higher speed, lower latency, and greater capacity; therefore, compared to the previous mobile networks, more advanced and intelligent network security is essential for 5G networks. To detect unknown and evolving 5G network intrusions, this paper presents an artificial intelligence (AI)-based network threat detection system to perform data labeling, data filtering, data preprocessing, and data learning for 5G network flow and security event data. The performance evaluations are first conducted on two well-known datasets-NSL-KDD and CICIDS 2017; then, the practical testing of proposed system is performed in 5G industrial IoT environments. To demonstrate detection against network threats in real 5G environments, this study utilizes the 5G model factory, which is downscaled to a real smart factory that comprises a number of 5G industrial IoT-based devices.

The rapid growth of number of devices that are connected to internet of things (IoT) networks, increases the severity of security problems that need to be solved in order to provide safe environment for network data exchange. The discovery of new vulnerabilities is everyday challenge for security experts and many novel methods for detection and prevention of intrusions are being developed for dealing with this issue. To overcome these shortcomings, artificial intelligence (AI) can be used in development of advanced intrusion detection systems (IDS). This allows such system to adapt to emerging threats, react in real-time and adjust its behavior based on previous experiences. On the other hand, the traffic classification task becomes more difficult because of the large amount of data generated by network systems and high processing demands. For this reason, feature selection (FS) process is applied to reduce data complexity by removing less relevant data for the active classification task and therefore improving algorithm's accuracy. In this work, hybrid version of recently proposed sand cat swarm optimizer algorithm is proposed for feature selection with the goal of increasing performance of extreme learning machine classifier. The performance improvements are demonstrated by validating the proposed method on two well-known datasets - UNSW-NB15 and CICIDS-2017, and comparing the results with those reported for other cutting-edge algorithms that are dealing with the same problems and work in a similar configuration.

Recently, research on AI-based network intrusion detection has been actively conducted. In previous studies, the machine learning models such as SVM (Support Vector Machine) and RF (Random Forest) showed consistently high performance, whereas the NB (Naïve Bayes) showed various performances with large deviations. In the paper, after analyzing the cause of the NB models showing various performances addressed in the several studies, we measured the performance of the Gaussian NB model according to the smoothing factor that is closely related to these causes. Furthermore, we compared the performance of the Gaussian NB model with that of the other models as a zero-day attack detection system. As a result of the experiment, the accuracy was 38.80% and 87.99% in case that the smoothing factor is 0 and default respectively, and the highest accuracy was 94.53% in case that the smoothing factor is 1e-01. In the experiment, we used only some types of the attack data in the NSL-KDD dataset. The experiments showed the applicability of the Gaussian NB model as a zero-day attack detection system in the future. In addition, it is clarified that the smoothing factor of the Gaussian NB model determines the shape of gaussian distribution that is related to the likelihood.

Network Intrusion Detection Systems (IDSs) have been used to increase the level of network security for many years. The main purpose of such systems is to detect and block malicious activity in the network traffic. Researchers have been improving the performance of IDS technology for decades by applying various machine-learning techniques. From the perspective of academia, obtaining a quality dataset (i.e. a sufficient amount of captured network packets that contain both malicious and normal traffic) to support machine learning approaches has always been a challenge. There are many datasets publicly available for research purposes, including NSL-KDD, KDDCUP 99, CICIDS 2017 and UNSWNB15. However, these datasets are becoming obsolete over time and may no longer be adequate or valid to model and validate IDSs against state-of-the-art attack techniques. As attack techniques are continuously evolving, datasets used to develop and test IDSs also need to be kept up to date. Proven performance of an IDS tested on old attack patterns does not necessarily mean it will perform well against new patterns. Moreover, existing datasets may lack certain data fields or attributes necessary to analyse some of the new attack techniques. In this paper, we argue that academia needs up-to-date high-quality datasets. We compare publicly available datasets and suggest a way to provide up-to-date high-quality datasets for researchers and the security industry. The proposed solution is to utilize the network traffic captured from the Locked Shields exercise, one of the world’s largest live-fire international cyber defence exercises held annually by the NATO CCDCOE. During this three-day exercise, red team members consisting of dozens of white hackers selected by the governments of over 20 participating countries attempt to infiltrate the networks of over 20 blue teams, who are tasked to defend a fictional country called Berylia. After the exercise, network packets captured from each blue team’s network are handed over to each team. However, the countries are not willing to disclose the packet capture (PCAP) files to the public since these files contain specific information that could reveal how a particular nation might react to certain types of cyberattacks. To overcome this problem, we propose to create a dedicated virtual team, capture all the traffic from this team’s network, and disclose it to the public so that academia can use it for unclassified research and studies. In this way, the organizers of Locked Shields can effectively contribute to the advancement of future artificial intelligence (AI) enabled security solutions by providing annual datasets of up-to-date attack patterns.