A Novel Learning based Secured Data Evaluation Scheme using Cybersecurity Principles with Artificial Intelligence Logic

The focus of the article revolves around NATO’s roadmap of smart defense against cyber attacks as the bedrock of Euro-Atlantic security. The author discloses NATO’S new policy and cutting-edge technical initiatives, aimed at focusing on countering global threats and cyber security challenges. It is stressed out, that new ideas towards a more synergetic approach between all the NATO Cyber Defense agencies should be explored to develop a shared framework for cybersecurity that might provide up-to-the date intelligence in order to ensure the development, acquisition and maintenance of the necessary military capabilities. The research highlights the core aspects of the 2010 Lisbon Summit that adopted NATO’s Strategic Concept ‘Active Engagement, Modern Defense: Strategic Concept for the Members of the North Atlantic. Treaty Organization’, that recognizes Cyber Defense as one of its strategic priorities. In particular, to foster Allied Nations’ cooperative efforts to counter terrorism, cyber attacks, prevent the proliferation of nuclear weapons and other weapons of mass destruction (WMD), Reinforce energy security and environmental constraints. Develop the capacity to contribute to energy security, including protection of critical energy infrastructure and transit areas and lines, cooperation with partners, and consultations among Allies on the basis of trategic assessments and contingency planning; In July 2011, NATO Defense Ministers adopted revised NATO Policy on Cyber Defense, which highlighted three areas: • The principles of subsidiarity and proportionality, which involve the assistance provided only upon request, in any other cases, the principle of selfresponsibility of sovereign states is applied; • Avoiding unnecessary duplication of the structures or capabilities and approaches on the international, regional and national levels; • Collaboration based on trust, with regard to the potential sensitivity and vulnerability of the system, the access to which has to be given. After the 2014 Wales Summit, in the revised NATO Cyber Defense Policy, cyber threats were identified as a potential prerequisite for collective defense under the Article 5 of the NATO Treaty. Noteworthy, Cyber Security is responsible for providing the broad spectrum of services in the following specialist security areas: CIS Security, Cyber Defense, Information Assurance, and Computer & Communications Security. Cyber defense is provided by many Alliance bodies: any NATO response concerning collective defense against cyber attacks will be subordinated to the North Atlantic Council (NAC), The Cyber Defense Committee (CDC) – the leading advisory body of the NAC. The executive level is represented by The Cyber Defense Management Board (CDMB), NATO Communications and Information Agency (NCI Agency), Cyber Security incorporates the NATO Computer Incident Response Capability (NCIRC) Technical Centre, providing specialist services to prevent, detect, respond to and recover from cyber security incidents.

Technologically advanced artificial intelligence (AI) is making a significant contribution to strengthening national security. AI algorithms facilitate the processing of vast amounts of information, increasing the speed and accuracy of decision-making. Artificial intelligence and machine learning (AI/ML) are crucial for state and integrated hybrid attacks and protecting new threats in cyberspace. Existing AI capabilities have significant potential to impact national security by leveraging existing machine learning technology for automation in labor-intensive activities such as satellite imagery analysis and defense against cyber attacks. This article examines selected aspects of the impact of artificial intelligence on enhancing a state’s ability to protect its interests and its citizens., artificial intelligence through the use of neutron networks, predictive analytics and machine learning algorithms enables security agencies to analyse vast amounts of data and identify patterns indicative of potential threats. Integrating artificial intelligence into surveillance, border control and threat assessment systems enhances the ability to respond preemptively to security challenges. In addition, artificial intelligence algorithms facilitate the processing of vast amounts of information, increasing the speed and accuracy of decision-making by police authorities. The rapid development of AI raises a number of questions for its use in securing not only national security but protecting all citizens. In particular, it is worth answering the question How does artificial intelligence affect national security and clarifying the issue of how law enforcement agencies can use artificial intelligence to maximise the benefits of the new technology in terms of security and protecting communities from rising crime. The analysis is based on a descriptive method in describing the phenomenon; by explaining the concepts and applications of artificial intelligence to determine its role in the national security sphere. An analysis of the usefulness of artificial intelligence in particular in police operations is undertaken, with the aim of defending the thesis that, despite some threats to the protection of human rights from AI, it is becoming the best tool in the fight against all types of crime in the country. Technological advances in AI can also have many positive effects for law enforcement, and useful for law enforcement agencies, for example in facilitating the identification of persons or vehicles, predicting trends in criminal activities, tracking illegal criminal activities or illegal money flows, flagging and responding to fake news. Artificial intelligence (AI) has emerged as one of the biggest threats to information security, but efforts are being made to mitigate this new threat, but also to find solutions on how AI can become an ally in the fight against cyber-security, crime and terrorist threats. Artificial intelligence algorithms search huge datasets of communication traffic, satellite images and social media posts to identify potential cyber security threats, terrorist activities and organized crime. It is advisable, when analyzing the opportunities and threats that AI poses to national and public security, to gain a strategic advantage in the context of rapid technological change and also to manage the many risks associated with AI. The conclusion highlights the impact of AI on national security, creating a range of new opportunities coupled with challenges that government agencies should be prepared for in addressing ethical and security dilemmas. Furthermore, AI improves predictive analytics, thereby enabling security agencies to more accurately anticipate potential threats and enhance their preparedness by identifying vulnerabilities in the national security infrastructure

Handbook of logic in artificial intelligence and logic programming, Volume 3, Nonmonotonic reasoning and uncertain reasoning, edited by Dov M. Gabbay, C. J. Hogger, and J. A. Robinson, with D. Nute, Handbooks of logic in computer science and artificial intelligence and logic programming, Clarendon Press, Oxford University Press, Oxford, New York, etc., 1994, xix + 529 pp.– - Volume 6 Issue 4

Machine learning enabled Industrial IoT Security: Challenges, Trends and Solutions

The article presents, summarizes and develops technological concepts of cyber security based on machine learning, data processing and analysis. The seven steps of cyber attacks, use of key techniques of data analysis in cyber defense, application of data processing and analysis in information security and some tools used are presented. Applying data processing and analysis as well as extracting dependencies from them in the field of cyber security has some advantages and disadvantages wich are indicated. A common scheme for data analysis is introduced. Main types of penetration of cyberattacks are involved. Cyber attack models abuse detection and anomaly detection are presented too. Тhree types of anomalies the timing, the number, and the pattern(s) are revealed and examples are pointed.

The article explores the technical and theoretical aspects of machine learning (ML) in addressing the escalating complexities of cybersecurity threats in the digital age since the ever-growing rise in cybercrime has prompted users to utilize newer approaches to raise the bar on cybersecurity. Research considers the adoption of machine learning (ML) technology as a cornerstone of virtually any contemporary problem in cyber security, particularly processes and techniques involved in problem analysis, detection, attack prediction, and even behavioral profiling. Elaborated on how ML makes a better response compared to traditional methods like signature-based detection by explaining how real-time analysis of massive data becomes possible. An overview of the important features of supervised and unsupervised learning is provided in the context of anomaly detection and malicious activity recognition with a focus on Support Vector Machine and Isolation Forests algorithms as well as a detailed look at the LSTM model for phishing URL evolution analysis. Also, those algorithms have been highlighted from the technical implementation side: supervised learning with Support Vector Machines using Scikit-Learn to classify network traffic trained on features like IP addresses and ports, unsupervised learning with Isolation Forests for anomaly detection in multidimensional data, and deep learning with Long Short-Term Memory (LSTM) networks for phishing URL analysis. This paper investigates significantly important difficulties in carrying out ML algorithms, such as class imbalance, adversarial attacks, and lack of model transparency. Such techniques as SMOTE (Synthetic Minority Over-sampling Technique) are proposed for developing training datasets, whereas model adversarial training and robust optimization methods are suggested for defense against malicious model exploitation. Also, the role of explainability methods such as SHAP and LIME are emphasized to build the trust and acceptance of automated ML systems in cybersecurity. Identified research opportunities and suggested that further testing be done on improving model robustness and performance metrics in constrained environments.

With the rapid development of information technology and the rapid popularization of the Internet, while people enjoy the convenience and efficiency brought about by new technologies, they are also suffering from the harm caused by cyber attacks. In addition to efficiently thwarting network assaults, a high volume of complicated security event data might unintentionally increase the strain of policy makers. At present, NS threats mainly include network viruses, trojans, DOS (Denial-Of-Service), etc. For the increasingly complex Network Security (NS) problems, the traditional rule-based network monitoring technology is difficult to predict the unknown attack behavior. Environment-based, dynamic and integrated data fusion can integrate data from a macro perspective. In recent years, Machine Learning (ML) technology has developed rapidly, which could easily train, test and predict existing third-party models. It uses ML algorithms to find out the association between data rather than manually sets rules. Support vector machine is a common ML method, which can predict the security of the network well after training and testing. In order to monitor the overall security status of the entire network, NS situation awareness refers to the real-time and accurate reproduction of network attacks using the reconstruction approach. Situation awareness technology is a powerful network monitoring and security technology, but there are many problems in the existing NS technology. For example, the state of the network cannot be accurately detected, and its change rule cannot be understood. In order to effectively predict network attacks, this paper adopted a technology based on ML and data analysis, and constructed a NS situational awareness model. The results showed that the detection efficiency of the model based on ML and data analysis was 7.18% higher than that of the traditional NS state awareness model.

Cyber security mitigation is becoming an increasingly urgent aspect in today's digital era, in response to the threat of theft and disruption to information systems, software and hardware. The Covid-19 pandemic has exacerbated this situation by increasing the chances of success of cyberattacks, as the number and scope of such attacks increases. The impact of increasing levels of cyberattacks is the wider disruption to various sectors. This study aims to investigate cybersecurity issues that arose during the Covid-19 pandemic. Researchers analyzed the current conditions as well as cybercrime acts and various types of cyberattacks carried out during this period. In addition, this research also proposes a practical approach to mitigating the risk of cyber attacks that can be implemented by organizations. It is important for organizations to strengthen the protection of their data and critical assets by implementing a comprehensive cybersecurity approach. The results of this study present various techniques for detecting and avoiding cybercrime threats, even after the pandemic is over. By implementing this approach, damage caused by cyber attacks can be minimized, maintaining the integrity of an organization's data and systems. For future research, it is necessary to carry out further development in the field of cyber security by integrating the latest technologies such as artificial intelligence, blockchain, Internet of Things, and other technologies. In this way, efforts to mitigate cyber attacks can continue to be improved, maintaining the integrity and security of information systems in this ever-growing digital era. This research makes an important contribution to understanding and addressing cyber security issues during the pandemic and provides a foundation for further development in the future.

Botnets are used for malicious activities such as cyber-attacks, spamming, and data theft and have become a significant threat to cyber security. Despite existing approaches for cyber attack detection, botnets prove to be a particularly difficult problem that calls for more advanced detection methods. In this research, a stacking classifier is proposed based on K-nearest neighbor, support vector machine, decision tree, random forest, and multilayer perceptron, called KSDRM, for botnet detection. Logistic regression acts as the meta-learner to combine the predictions from the base classifiers into the final prediction with the aim of increasing the overall accuracy and predictive performance of the ensemble. The UNSW-NB15 dataset is used to train machine learning models and evaluate their effectiveness in detecting cyber-attacks on IoT networks. The categorical features are transformed into numerical values using label encoding. Machine learning techniques are adopted to recognize botnet attacks to enhance cyber security measures. The KSDRM model successfully captures the complex patterns and traits of botnet attacks and obtains 99.99% training accuracy. The KSDRM model also performs well during testing by achieving an accuracy of 97.94%. Based on 3, 5, 7, and 10 folds, the k-fold cross-validation results show that the proposed method’s average accuracy is 99.89%, 99.88%, 99.89%, and 99.87%, respectively. Further, the demonstration of experiments and results shows the KSDRM model is an effective method to identify botnet-based cyber attacks. The findings of this study have the potential to improve cyber security controls and strengthen networks against changing threats.

Cyber-attacks have become a very serious issue in Europe, targeting essential services such as national health systems, banks, electoral campaigns or mobile services. There is certainly no one single solution to the need to improve cybersecurity, but a wide range of collective and far-reaching technical and legal measures may make it as hard as is possible for those who want to attack the security of infrastructures, services and products. This article aims to aid our understanding of cybersecurity, cyber threats, cyber-attacks and cyber defence from both legal and technological perspectives. It discusses the most recent EU cybersecurity legislative movements and considers whether current legal and technical measures, including the newly adopted EU Cybersecurity Act 2019, have provided efficient solutions to respond to radically changed cyber threats and attacks, in particular in critical services in the EU. It offers insights into the scope and limitations of technical measures in achieving the highest possible level of cybersecurity due to the unpredictable nature of certain cyber-attacks.

The cyber security industry is witnessing a significant shift as more and more companies look to artificial intelligence (AI) to rethink their defense strategies against emerging cyber threats. This study explores how artificial intelligence (AI) is fundamentally changing the way that threat detection paradigms are traditionally understood. Through an examination of the past and present of traditional approaches, we draw attention to the growing necessity of artificial intelligence-driven solutions. The paper provides a thorough examination of the basis of artificial intelligence (AI) in threat detection, with a focus on neural networks and machine learning methods. This paper examines several AI-powered threat detection methods, such as behavioral analysis, anomaly detection, and a comparison of heuristic-based and signature-based strategies. Examined are issues like explain ability, interpretability, and adversarial attacks, which offer a thorough understanding of the difficulties and factors, related to AI-driven cyber security. With a focus on threat intelligence platforms, explainable AI, predictive analytics, and integration, the paper takes a forward-looking approach to improving cyber security. The effectiveness and efficiency of AI-powered threat detection are compared to more conventional techniques in a comparative analysis that ends with advice for businesses looking to use AI-driven solutions. Key findings and their implications for the changing cyber defense landscape are summarized in the study&#39, s conclusion. By doing this research, we hope to add to the continuing conversation about the development of threat detection and provide useful advice and insights to help enterprises successfully incorporate AI into their cyber security plans.

Cybersecurity is the process of protecting computer networks from cyber attacks or unintended unauthorized access.It is the need of the hour.Organizations, businesses, and governments need cybersecurity solutions because cyber criminals pose a threat to everyone.Artificial intelligence promises to be a great solution for this.By combining the strength of artificial intelligence with cybersecurity, security experts are more capable to defend vulnerable networks and data from cyber attackers.This paper provides an introduction to the use of artificial intelligence in cybersecurity.

It's virtually not possible to measures the reasons why cybersecurity is crucial in the expanding battlefield of cybersecurity. Permitting mischievous threats to operate amok anywhere, whenever, and in any circumstance is not acceptable, particularly when it comes to the complex network of client and organization data that cyber security teams are tasked with safeguarding. Individuals and families, as well as corporations, agencies, and educational institutions, must consider cyber security. Machine Learning Can Help Advance the Cyber Security Landscape. Today's businesses collect massive quantities of information/data. Data is considered as the heart of almost every business-focused system imaginable. Infrastructure systems are included in this. In today’s era high-tech structure, network and cybersecurity systems, collects massive quantities of data and analytics on most mission-critical systems' key elements. While humans also provide critical serviceable oversight and brilliant insights in today's infrastructure, machine learning and artificial intelligence are rapidly gaining traction in almost all domain of today's systems, be it on premises or on the cloud.

The present study explores contemporary approaches to the analysis, modelling and assurance of cyber security of critical information infrastructure in the face of modern threats. This text focuses on the utilisation of intelligent methods and advanced technologies for the protection of critical information infrastructure (CII). The objective of the present study is to employ an integrative approach to analysing, modelling, and ensuring the cyber security of CII in the face of contemporary threats. The methodological foundation of the study is a comprehensive analysis of scientific literature dedicated to the application of intelligent methods and technologies for the protection of CII. This includes both fundamental theoretical developments and practical aspects of implementing modern cyber security approaches, as well as experimental modelling of fraud detection processes in the CII of the financial sector. This modelling was conducted by the authors using artificial intelligence (AI) methods. A comparative analysis of concepts proposed by modern researchers is given particular emphasis, with the objective of identifying the main trends, development prospects, and potential areas for improving existing CII protection systems. In relation to the outcomes of the extension of scientific sources, it was determined that intelligent methodologies founded on machine learning and AI represent pivotal technologies for the effective safeguarding of critical infrastructure within the financial sector against contemporary cyberattacks and security threats. The results of the modelling of fraud detection processes in the CII of the financial sector of the economy allow an assessment of the effectiveness of the methods used, determination of their advantages and limitations, and formulation of recommendations for further improvement of cyber protection systems for the CII of the financial sector of the economy. The employment of an integrative approach, encompassing threat analysis, simulation modelling, artificial intelligence, and contemporary cyber defence technologies, facilitates the enhancement of the security of critical infrastructure and the effective response to emergent threats. The proposed solutions have the potential to facilitate the development of highly effective cyber defence systems in various critical infrastructure industries, including the financial, energy, and government sectors.

We will review cyber-security, and chart a course for the future. To understand the problems of cyber security is to know the risks and the fundamental issues encompassing it. Today, the internet and computers have no boundaries. Data is available locally, remotely, and distributed in the cloud. Connectivity, and sharing across software applications has caused a super-exponential growth in the number of possible ways to exploit systems. There is a great need for seamless cybersecurity protection covering: prevention, identification, and defense against attacks. We need cybersecurity that is simple to use, has low overhead, and really works. We believe the future of cybersecurity is at the hardware level. There are no boundaries to cybersecurity. Cyber attackers, intruders, pranksters, and malicious users can be external, internal, local, next door, or on the other side of the world. The attacker’s goal is to exploit a weakness, remain anonymous, and take advantage of stolen information. Risks are many covering simple to define to beyond the state-of-the art to understand. Traditional attack paths include viruses (malicious code hidden in software), worms (automatically selfpropagate), and Trojan horses (create remote back doors for attackers). More recent attack paths are taking advantage of hardware assisted virtualization, and hardware assisted virtual machines. The New Year 2018 brought, new information leakage attacks called Spectre [3] and Meldown [4]. Malicious code can combine viruses, worms, Trojan horses, scripts, et al. to attack multiple, cross-platform systems. For future cyber security, we need simple cyber mechanism(s) amicable to machine generated formal proofs for the whole system (including hardware and software). We present research directions for the future.