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Luo, Bo, Beuran, Razvan, Tan, Yasuo.  2020.  Smart Grid Security: Attack Modeling from a CPS Perspective. 2020 IEEE Computing, Communications and IoT Applications (ComComAp). :1–6.
With the development of smart grid technologies and the fast adoption of household IoT devices in recent years, new threats, attacks, and security challenges arise. While a large number of vulnerabilities, threats, attacks and controls have been discussed in the literature, there lacks an abstract and generalizable framework that can be used to model the cyber-physical interactions of attacks and guide the design of defense mechanisms. In this paper, we propose a new modeling approach for security attacks in smart grids and IoT devices using a Cyber-Physical Systems (CPS) perspective. The model considers both the cyber and physical aspects of the core components of the smart grid system and the household IoT devices, as well as the interactions between the components. In particular, our model recognizes the two parallel attack channels via the cyber world and the physical world, and identifies the potential crossing routes between these two attack channels. We further discuss all possible attack surfaces, attack objectives, and attack paths in this newly proposed model. As case studies, we examine from the perspective of this new model three representative attacks proposed in the literature. The analysis demonstrates the applicability of the model, for instance, to assist the design of detection and defense mechanisms against smart grid cyber-attacks.
Sun, Yizhen, Lin, Dandan, Song, Hong, Yan, Minjia, Cao, Linjing.  2020.  A Method to Construct Vulnerability Knowledge Graph Based on Heterogeneous Data. 2020 16th International Conference on Mobility, Sensing and Networking (MSN). :740–745.
In recent years, there are more and more attacks and exploitation aiming at network security vulnerabilities. It is effective for us to prevent criminals from exploiting vulnerabilities for attacks and help security analysts maintain equipment security that knows vulnerabilities and threats on time. With the knowledge graph, we can organize, manage, and utilize the massive information effectively in cyberspace. In this paper we construct the vulnerability ontology after analyzing multi-source heterogeneous databases. And the vulnerability knowledge graph is established. Experimental results show that the accuracy of entity recognition for extracting vendor names reaches 89.76%. The more rules used in entity recognition, the higher the accuracy and the lower the error rate.
Ren, Xun-yi, Luo, Qi-qi, Shi, Chen, Huang, Jia-ming.  2020.  Network Security Posture Prediction Based on SAPSO-Elman Neural Networks. 2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE). :533–537.
With the increasing popularity of the Internet, mobile Internet and the Internet of Things, the current network environment continues to become more complicated. Due to the increasing variety and severity of cybersecurity threats, traditional means of network security protection have ushered in a huge challenge. The network security posture prediction can effectively predict the network development trend in the future time based on the collected network history data, so this paper proposes an algorithm based on simulated annealing-particle swarm algorithm to optimize improved Elman neural network parameters to achieve posture prediction for network security. Taking advantage of the characteristic that the value of network security posture has periodicity, a simulated annealing algorithm is introduced along with an improved particle swarm algorithm to solve the problem that neural network training is prone to fall into a local optimal solution and achieve accurate prediction of the network security posture. Comparison of the proposed scheme with existing prediction methods validates that the scheme has a good posture prediction accuracy.
Lee, Yen-Ting, Ban, Tao, Wan, Tzu-Ling, Cheng, Shin-Ming, Isawa, Ryoichi, Takahashi, Takeshi, Inoue, Daisuke.  2020.  Cross Platform IoT-Malware Family Classification Based on Printable Strings. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :775–784.
In this era of rapid network development, Internet of Things (IoT) security considerations receive a lot of attention from both the research and commercial sectors. With limited computation resource, unfriendly interface, and poor software implementation, legacy IoT devices are vulnerable to many infamous mal ware attacks. Moreover, the heterogeneity of IoT platforms and the diversity of IoT malware make the detection and classification of IoT malware even more challenging. In this paper, we propose to use printable strings as an easy-to-get but effective cross-platform feature to identify IoT malware on different IoT platforms. The discriminating capability of these strings are verified using a set of machine learning algorithms on malware family classification across different platforms. The proposed scheme shows a 99% accuracy on a large scale IoT malware dataset consisted of 120K executable fils in executable and linkable format when the training and test are done on the same platform. Meanwhile, it also achieves a 96% accuracy when training is carried out on a few popular IoT platforms but test is done on different platforms. Efficient malware prevention and mitigation solutions can be enabled based on the proposed method to prevent and mitigate IoT malware damages across different platforms.
Chen, Chin-Wei, Su, Ching-Hung, Lee, Kun-Wei, Bair, Ping-Hao.  2020.  Malware Family Classification Using Active Learning by Learning. 2020 22nd International Conference on Advanced Communication Technology (ICACT). :590–595.
In the past few years, the malware industry has been thriving. Malware variants among the same malware family shared similar behavioural patterns or signatures reflecting their purpose. We propose an approach that combines support vector machine (SVM) classifiers and active learning by learning (ALBL) techniques to deal with insufficient labeled data in terms of the malware classification tasks. The proposed approach is evaluated with the malware family dataset from Microsoft Malware Classification Challenge (BIG 2015) on Kaggle. The results show that ALBL techniques can effectively boost the performance of our machine learning models and improve the quality of labeled samples.
Lin, Kuang-Yao, Huang, Wei-Ren.  2020.  Using Federated Learning on Malware Classification. 2020 22nd International Conference on Advanced Communication Technology (ICACT). :585–589.
In recent years, everything has been more and more systematic, and it would generate many cyber security issues. One of the most important of these is the malware. Modern malware has switched to a high-growth phase. According to the AV-TEST Institute showed that there are over 350,000 new malicious programs (malware) and potentially unwanted applications (PUA) be registered every day. This threat was presented and discussed in the present paper. In addition, we also considered data privacy by using federated learning. Feature extraction can be performed based on malware. The proposed method achieves very high accuracy ($\approx$0.9167) on the dataset provided by VirusTotal.
Vurdelja, Igor, Blažić, Ivan, Bojić, Dragan, Drašković, Dražen.  2020.  A framework for automated dynamic malware analysis for Linux. 2020 28th Telecommunications Forum (℡FOR). :1–4.
Development of malware protection tools requires a more advanced test environment comparing to safe software. This kind of development includes a safe execution of many malware samples in order to evaluate the protective power of the tool. The host machine needs to be protected from the harmful effects of malware samples and provide a realistic simulation of the execution environment. In this paper, a framework for automated malware analysis on Linux is presented. Different types of malware analysis methods are discussed, as well as the properties of a good framework for dynamic malware analysis.
Grusho, A., Nikolaev, A., Piskovski, V., Sentchilo, V., Timonina, E..  2020.  Endpoint Cloud Terminal as an Approach to Secure the Use of an Enterprise Private Cloud. 2020 International Scientific and Technical Conference Modern Computer Network Technologies (MoNeTeC). :1–4.
Practical activities usually require the ability to simultaneously work with internal, distributed information resources and access to the Internet. The need to solve this problem necessitates the use of appropriate administrative and technical methods to protect information. Such methods relate to the idea of domain isolation. This paper considers the principles of implementation and properties of an "Endpoint Cloud Terminal" that is general-purpose software tool with built-in security instruments. This apparatus solves the problem by combining an arbitrary number of isolated and independent workplaces on one hardware unit, a personal computer.
Wright, Marc, Chizari, Hassan, Viana, Thiago.  2020.  Analytical Framework for National Cyber-Security and Corresponding Critical Infrastructure: A Pragmatistic Approach. 2020 International Conference on Computational Science and Computational Intelligence (CSCI). :127–130.
Countries are putting cyber-security at the forefront of their national issues. With the increase in cyber capabilities and infrastructure systems becoming cyber-enabled, threats now have a physical impact from the cyber dimension. This paper proposes an analytical framework for national cyber-security profiling by taking national governmental and technical threat modeling simulations. Applying thematic analysis towards national cybersecurity strategy helps further develop understanding, in conjunction with threat modeling methodology simulation, to gain insight into critical infrastructure threat impact.
Dessouky, Ghada, Frassetto, Tommaso, Jauernig, Patrick, Sadeghi, Ahmad-Reza, Stapf, Emmanuel.  2020.  With Great Complexity Comes Great Vulnerability: From Stand-Alone Fixes to Reconfigurable Security. IEEE Security Privacy. 18:57–66.
The increasing complexity of modern computing devices has rendered security architectures vulnerable to recent side-channel and transient-execution attacks. We discuss the most relevant defenses as well as their drawbacks and how to overcome them for next-generation secure processor design.
Conference Name: IEEE Security Privacy
Yang, Xiaodong, Liu, Rui, Chen, Guilan, Wang, Meiding, Wang, Caifen.  2020.  Security Analysis of a Certificateless Signcryption Mechanism without Bilinear Mapping. 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). 1:2431–2434.
Certificateless signcryption mechanism can not only provide security services, such as message integrity, non-repudiation and confidentiality, but also solve the problems of public key certificate management and key escrow. Zhou et al. proposed a certificateless signcryption mechanism without bilinear mapping and gave its security proof under the discrete logarithm problem and the computational Diffie Hellman problem in the random oracle model. However, the analysis show that this scheme has security flaws. That is, attackers can forge legitimate signatures of any messages. Finally, we give the specific attack process.
Gegan, Ross, Mao, Christina, Ghosal, Dipak, Bishop, Matt, Peisert, Sean.  2020.  Anomaly Detection for Science DMZs Using System Performance Data. 2020 International Conference on Computing, Networking and Communications (ICNC). :492—496.
Science DMZs are specialized networks that enable large-scale distributed scientific research, providing efficient and guaranteed performance while transferring large amounts of data at high rates. The high-speed performance of a Science DMZ is made viable via data transfer nodes (DTNs), therefore they are a critical point of failure. DTNs are usually monitored with network intrusion detection systems (NIDS). However, NIDS do not consider system performance data, such as network I/O interrupts and context switches, which can also be useful in revealing anomalous system performance potentially arising due to external network based attacks or insider attacks. In this paper, we demonstrate how system performance metrics can be applied towards securing a DTN in a Science DMZ network. Specifically, we evaluate the effectiveness of system performance data in detecting TCP-SYN flood attacks on a DTN using DBSCAN (a density-based clustering algorithm) for anomaly detection. Our results demonstrate that system interrupts and context switches can be used to successfully detect TCP-SYN floods, suggesting that system performance data could be effective in detecting a variety of attacks not easily detected through network monitoring alone.
Shin, Sanggyu, Seto, Yoichi.  2020.  Development of IoT Security Exercise Contents for Cyber Security Exercise System. 2020 13th International Conference on Human System Interaction (HSI). :1—6.
In this paper, we discuss the development of the IoT security exercise content and the implementation of it to the CyExec. While the Internet of Things (IoT) devices are becoming more popular, vulnerability countermeasures are insufficient, and many incidents have occurred. It is because there is insufficient protection against vulnerabilities specific to IoT equipment. Also, the developers and users have low awareness of IoT devices against vulnerabilities from the past. Therefore, the importance of security education on IoT devices is increasing. However, the enormous burden of introduction and operation costs limited the use of commercial cybersecurity exercise systems. CyExec (Cyber Security Exercise System), consisting of a virtual environment using VirtualBox and Docker, is a low-cost and flexible cybersecurity exercise system, which we have proposed for the dissemination of security education. And the content of the exercises for CyExec is composed of the Basic exercises and Applied exercises.
Saeed, Imtithal A., Selamat, Ali, Rohani, Mohd Foad, Krejcar, Ondrej, Chaudhry, Junaid Ahsenali.  2020.  A Systematic State-of-the-Art Analysis of Multi-Agent Intrusion Detection. IEEE Access. 8:180184–180209.
Multi-agent architectures have been successful in attaining considerable attention among computer security researchers. This is so, because of their demonstrated capabilities such as autonomy, embedded intelligence, learning and self-growing knowledge-base, high scalability, fault tolerance, and automatic parallelism. These characteristics have made this technology a de facto standard for developing ambient security systems to meet the open and dynamic nature of today's online communities. Although multi-agent architectures are increasingly studied in the area of computer security, there is still not enough empirical evidence on their performance in intrusions and attacks detection. The aim of this paper is to report the systematic literature review conducted in the context of specific research questions, to investigate multi-agent IDS architectures to highlight the issues that affect their performance in terms of detection accuracy and response time. We used pertinent keywords and terms to search and retrieve the most recent research studies, on multi-agent IDS architectures, from the major research databases and digital libraries such as SCOPUS, Springer, and IEEE Explore. The search processes resulted in a number of studies; among them, there were journal articles, book chapters, conference papers, dissertations, and theses. The obtained studies were assessed and filtered out, and finally, there were over 71 studies chosen to answer the research questions. The results of this study have shown that multi-agent architectures include several advantages that can help in the development of ambient IDS. However, it has been found that there are several issues in the current multi-agent IDS architectures that may degrade the accuracy and response time of intrusions and attacks detection. Based on our findings, the issues of multi-agent IDS architectures include limitations in the techniques, mechanisms, and schemes used for multi-agent IDS adaptation and learning, load balancing, scalability, fault-tolerance, and high communication overhead. It has also been found that new measurement metrics are required for evaluating multi-agent IDS architectures.
Xia, Shaoxian, Wang, Zheng, Hou, Zhanbin, Ye, Hongshu, Xue, Binbin, Wang, Shouzhi, Zhang, Xuecheng, Yang, Kewen.  2020.  Design of Quantum Key Fusion Model for Power Multi-terminal. 2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE). :196—199.
With the construction of State Grid informatization, professional data such as operation inspection, marketing, and regulation have gradually shifted from offline to online. In recent years, cyberspace security incidents have occurred frequently, and national and group cybersecurity threats have emerged. As the next-generation communication system, quantum security has to satisfy the security requirements. Also, it is especially important to build the fusion application of energy network quantum private communication technology and conventional network, and to form a safe and reliable quantum-level communication technology solution suitable for the power grid. In this paper, from the perspective of the multi-terminal quantum key application, combined with a mature electricity consumption information collection system, a handheld meter reading solution based on quantum private communication technology is proposed to effectively integrate the two and achieve technological upgrading. First, from the technical theory and application fields, the current situation of quantum private communication technology and its feasibility of combining with classical facilities are introduced and analyzed. Then, the hardware security module and handheld meter reading terminal equipment are taken as typical examples to design and realize quantum key shared storage, business security process application model; finally, based on the overall environment of quantum key distribution, the architecture design of multi-terminal quantum key application verification is implemented to verify the quantum key business application process.
He, Kexun, Qin, Kongjian, Wang, Changyuan, Fang, Xiyu.  2020.  Research on Cyber Security Test Method for GNSS of Intelligent Connected Vehicle. 2020 International Conference on Computer Information and Big Data Applications (CIBDA). :200—203.
Intelligent connected vehicle cyber security has attracted widespread attention this year. The safety of GNSS information is related to the safety of cars and has become a key technology. This paper researches the cyber security characteristics of intelligent connected vehicle navigation and positioning by analyzing the signal receiving mode of navigation and positioning on the vehicle terminal. The article expounds the principles of deceiving and interfering cyber security that lead to the safety of GNSS information. This paper studies the key causes of cyber security. Based on key causes, the article constructs a GNSS cyber security test method by combining a navigation signal simulator and an interference signal generator. The results shows that the method can realize the security test of the GNSS information of the vehicle terminal. This method provides a test method for the navigation terminal defense cyber security capability for a vehicle terminal, and fills a gap in the industry for the vehicle terminal information security test.
Nilă, Constantin, Patriciu, Victor.  2020.  Taking advantage of unsupervised learning in incident response. 2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). :1–6.
This paper looks at new ways to improve the necessary time for incident response triage operations. By employing unsupervised K-means, enhanced by both manual and automated feature extraction techniques, the incident response team can quickly and decisively extrapolate malicious web requests that concluded to the investigated exploitation. More precisely, we evaluated the benefits of different visualization enhancing methods that can improve feature selection and other dimensionality reduction techniques. Furthermore, early tests of the gross framework have shown that the necessary time for triage is diminished, more so if a hybrid multi-model is employed. Our case study revolved around the need for unsupervised classification of unknown web access logs. However, the demonstrated principals may be considered for other applications of machine learning in the cybersecurity domain.
Hopkins, Stephen, Kalaimannan, Ezhil, John, Caroline Sangeetha.  2020.  Cyber Resilience using State Estimation Updates Based on Cyber Attack Matrix Classification. 2020 IEEE Kansas Power and Energy Conference (KPEC). :1—6.
Cyber-physical systems (CPS) maintain operation, reliability, and safety performance using state estimation and control methods. Internet connectivity and Internet of Things (IoT) devices are integrated with CPS, such as in smart grids. This integration of Operational Technology (OT) and Information Technology (IT) brings with it challenges for state estimation and exposure to cyber-threats. This research establishes a state estimation baseline, details the integration of IT, evaluates the vulnerabilities, and develops an approach for detecting and responding to cyber-attack data injections. Where other approaches focus on integration of IT cyber-controls, this research focuses on development of classification tools using data currently available in state estimation methods to quantitatively determine the presence of cyber-attack data. The tools may increase computational requirements but provide methods which can be integrated with existing state estimation methods and provide for future research in state estimation based cyber-attack incident response. A robust cyber-resilient CPS includes the ability to detect and classify a cyber-attack, determine the true system state, and respond to the cyber-attack. The purpose of this paper is to establish a means for a cyber aware state estimator given the existence of sub-erroneous outlier detection, cyber-attack data weighting, cyber-attack data classification, and state estimation cyber detection.
Alabadi, Montdher, Albayrak, Zafer.  2020.  Q-Learning for Securing Cyber-Physical Systems : A survey. 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). :1–13.
A cyber-physical system (CPS) is a term that implements mainly three parts, Physical elements, communication networks, and control systems. Currently, CPS includes the Internet of Things (IoT), Internet of Vehicles (IoV), and many other systems. These systems face many security challenges and different types of attacks, such as Jamming, DDoS.CPS attacks tend to be much smarter and more dynamic; thus, it needs defending strategies that can handle this level of intelligence and dynamicity. Last few years, many researchers use machine learning as a base solution to many CPS security issues. This paper provides a survey of the recent works that utilized the Q-Learning algorithm in terms of security enabling and privacy-preserving. Different adoption of Q-Learning for security and defending strategies are studied. The state-of-the-art of Q-learning and CPS systems are classified and analyzed according to their attacks, domain, supported techniques, and details of the Q-Learning algorithm. Finally, this work highlight The future research trends toward efficient utilization of Q-learning and deep Q-learning on CPS security.
Qian, Kai, Dan Lo, Chia-Tien, Guo, Minzhe, Bhattacharya, Prabir, Yang, Li.  2012.  Mobile security labware with smart devices for cybersecurity education. IEEE 2nd Integrated STEM Education Conference. :1—3.

Smart mobile devices such as smartphones and tablets have become an integral part of our society. However, it also becomes a prime target for attackers with malicious intents. There have been a number of efforts on developing innovative courseware to promote cybersecurity education and to improve student learning; however, hands-on labs are not well developed for smart mobile devices and for mobile security topics. In this paper, we propose to design and develop a mobile security labware with smart mobile devices to promote the cybersecurity education. The integration of mobile computing technologies and smart devices into cybersecurity education will connect the education to leading-edge information technologies, motivate and engage students in security learning, fill in the gap with IT industry need, and help faculties build expertise on mobile computing. In addition, the hands-on experience with mobile app development will promote student learning and supply them with a better understanding of security knowledge not only in classical security domains but also in the emerging mobile security areas.

Laato, Samuli, Farooq, Ali, Tenhunen, Henri, Pitkamaki, Tinja, Hakkala, Antti, Airola, Antti.  2020.  AI in Cybersecurity Education- A Systematic Literature Review of Studies on Cybersecurity MOOCs. 2020 IEEE 20th International Conference on Advanced Learning Technologies (ICALT). :6—10.

Machine learning (ML) techniques are changing both the offensive and defensive aspects of cybersecurity. The implications are especially strong for privacy, as ML approaches provide unprecedented opportunities to make use of collected data. Thus, education on cybersecurity and AI is needed. To investigate how AI and cybersecurity should be taught together, we look at previous studies on cybersecurity MOOCs by conducting a systematic literature review. The initial search resulted in 72 items and after screening for only peer-reviewed publications on cybersecurity online courses, 15 studies remained. Three of the studies concerned multiple cybersecurity MOOCs whereas 12 focused on individual courses. The number of published work evaluating specific cybersecurity MOOCs was found to be small compared to all available cybersecurity MOOCs. Analysis of the studies revealed that cybersecurity education is, in almost all cases, organised based on the topic instead of used tools, making it difficult for learners to find focused information on AI applications in cybersecurity. Furthermore, there is a gab in academic literature on how AI applications in cybersecurity should be taught in online courses.

Chao, Henry, Stark, Benjamin, Samarah, Mohammad.  2019.  Analysis of Learning Modalities Towards Effective Undergraduate Cybersecurity Education Design. 2019 IEEE International Conference on Engineering, Technology and Education (TALE). :1—6.
Cybersecurity education is a critical component of today's computer science and IT curriculum. To provide for a highly effective cybersecurity education, we propose using machine-learning techniques to identify common learning modalities of cybersecurity students in order to optimize how cybersecurity core topics, threats, tools and techniques are taught. We test various hypothesis, e.g. that students of selected VARK learning styles will outperform their peers. The results indicate that for the class assignments in our study preference of read/write and kinesthetic modalities yielded the best results. This further indicates that specific learning instruments can be tailored for students based on their individual VARK learning styles.
Addae, Joyce, Radenkovic, Milena, Sun, Xu, Towey, Dave.  2016.  An extended perspective on cybersecurity education. 2016 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE). :367—369.
The current trend of ubiquitous device use whereby computing is becoming increasingly context-aware and personal, has created a growing concern for the protection of personal privacy. Privacy is an essential component of security, and there is a need to be able to secure personal computers and networks to minimize privacy depreciation within cyberspace. Human error has been recognized as playing a major role in security breaches: Hence technological solutions alone cannot adequately address the emerging security and privacy threats. Home users are particularly vulnerable to cybersecurity threats for a number of reasons, including a particularly important one that our research seeks to address: The lack of cybersecurity education. We argue that research seeking to address the human element of cybersecurity should not be limited only to the design of more usable technical security mechanisms, but should be extended and applied to offering appropriate training to all stakeholders within cyberspace.
Alnsour, Rawan, Hamdan, Basil.  2020.  Incorporating SCADA Cybersecurity in Undergraduate Engineering Technology Information Technology Education. 2020 Intermountain Engineering, Technology and Computing (IETC). :1—4.

The purpose of this paper is threefold. First, it makes the case for incorporating cybersecurity principles into undergraduate Engineering Technology Education and for incorporating Industrial Control Systems (ICS) principles into undergraduate Information Technology (IT)/Cybersecurity Education. Specifically, the paper highlights the knowledge/skill gap between engineers and IT/Cybersecurity professionals with respect to the cybersecurity of the ICS. Secondly, it identifies several areas where traditional IT systems and ICS intercept. This interception not only implies that ICS are susceptible to the same cyber threats as traditional IT/IS but also to threats that are unique to ICS. Subsequently, the paper identifies several areas where cybersecurity principles can be applied to ICS. By incorporating cybersecurity principles into Engineering Technology Education, the paper hopes to provide IT/Cybersecurity and Engineering Students with (a) the theoretical knowledge of the cybersecurity issues associated with administering and operating ICS and (b) the applied technical skills necessary to manage and mitigate the cyber risks against these systems. Overall, the paper holds the promise of contributing to the ongoing effort aimed at bridging the knowledge/skill gap with respect to securing ICS against cyber threats and attacks.

Raj, Rajendra K., Ekstrom, Joseph J., Impagliazzo, John, Lingafelt, Steven, Parrish, Allen, Reif, Harry, Sobiesk, Ed.  2017.  Perspectives on the future of cybersecurity education. 2017 IEEE Frontiers in Education Conference (FIE). :1—2.
As the worldwide demand for cybersecurity-trained professionals continues to grow, the need to understand and define what cybersecurity education really means at the college or university level. Given the relative infancy of these efforts to define undergraduate cybersecurity programs, the panelists will present different perspectives on how such programs can be structured. They will then engage with the audience to explore additional viewpoints on cybersecurity, and work toward a shared understanding of undergraduate cybersecurity programs.