As exponential data generation and system interconnection have transformed organizational capabilities worldwide, isolated information networks are now coupled to many third-party commercial, industrial, and civic networks. This ecosystem complexity complicates threat landscapes and obscures awareness, requiring cybersecurity paradigms to go beyond perimeter-focused safeguards and isolated incident response. The authors call for network security situational awareness approaches that analyze security posture in light of supply chain integration and ongoing adversary threats. An integrated network security situational awareness framework integrates threat telemetry ingestion, risk modeling, metrics rationalization, analytics processing, and predictive simulation. This aims to move enterprises from reactive to strategic resource allocation to fortify network security over time. Security situational awareness approaches in statistical analytics, machine learning, data visualization, and prediction are reviewed to compare their benefits and drawbacks for network security situation cognition in heterogeneous situations. Comparative observations guide future studies to balance customization and standardization for mass awareness. The paper recommends flexible command centers that continually synthesize environmental information, intelligent alerting, quick response mobilization, and predictive supply chain risk identification to connect network security methods with chaotic current reality. Resilience requires constant uncertainty-based operational truth illumination in complexity acceleration (Li et al., 2019).
With the rapid growth of interconnected networks, reactive cybersecurity models are insufficient for reliable operations due to supply chain dependencies and evolving adversaries. Thus, a shift toward holistic network security situational awareness approaches that examine integrated data, predict exposure footprints, and constantly prepare for dynamic threats is underway. The authors suggest five steps of network security situational awareness to expose facts through analytical rigor and technical lucidity and accurately inform leaders. First, threat factor ingestion organizes enterprise-wide incidents and suspicious behavioral signatures into uniform schemas that contextualize events and adversary tradecraft. Risk modeling uses rich feature sets to quantify exposure probability based on asset category and geographical site business effect evaluations (Gunduz & Das, 2020). Through statistical correlation, rationalized key performance indicators identify injury tolerance thresholds and response efficacy and proactively pace current capabilities against benchmarks from unified data lakes. Optimized analytics procedures turn metrics into actionable risk dashboards that show vulnerabilities, intrusion likelihoods, and capability gaps, helping stakeholders make tradeoffs and strategically uplift. Augmented intelligence simulates long-term breach potentials under changing budgeting conditions to maintain this uplift. This ordered paradigm helps us understand the complex processes behind modern cyber threats at a scale beyond prevention-focused security. Institutional resilience results from accepting continuous misfortune and uncertainty while gradually building skills (Humayun et al., 2020).
The article emphasizes network security situational awareness. The data value chain approach is used to propose a five-step logical analysis framework: factor acquisition, model representation, measurement establishment, solution analysis, and scenario prediction. Each level is detailed, along with its mainstream approaches. Network security situational awareness should be approached systematically by collecting security-related data, creating formal models to represent elements, establishing metrics to quantify elements, and analyzing the data to predict security situations. The study proposes a structured approach to network security, helping to understand its security posture (Li et al., 2019). I like the author’s network security situational awareness approach. It follows industry standards and recognizes the complexity of cybersecurity threats. Effective network security includes real-time monitoring, data analysis, and proactive threat detection and response. Organizations can improve security issue detection and response by adopting the paper’s logical structure (Gunduz & Das, 2020).
The paper acknowledges the diverse nature of network security risks inside network security situational awareness without naming particular threats. Network security situational awareness includes understanding threats and obstacles that could affect digital asset integrity, confidentiality, and availability. Malware is a major network security threat. Viruses, worms, trojans, ransomware, and other dangerous programs enter systems, disrupt operations, and jeopardize sensitive data. The article highlights the growing threat of phishing attacks. Phishing is a misleading attempt to steal login credentials or financial information. Phishing can compromise data and access (Gunduz & Das, 2020).
Another threat is data breaches. Network security is threatened by unauthorized access and disclosure of sensitive data. Data breaches can expose personal, financial, and intellectual property, causing severe harm to persons and businesses. Situational awareness emphasizes detecting unauthorized access attempts (Humayun et al., 2020). External or internal dangers can obtain unauthorized access and use it for malevolent purposes. Addressing network vulnerabilities is essential for network security situational awareness. This comprises software, hardware, and configuration vulnerabilities that attackers could use to obtain access or impair operations. The article also highlights organized and sophisticated attacks, indicating a concern for these threats. Skilled adversaries launch long-term, targeted APTs to infiltrate networks unnoticed (Li et al., 2019).
References
Gunduz, M. Z., & Das, R. (2020). Cyber-Security on Smart Grid: Threats and Potential Solutions. Computer networks, 169, 107094. https://www.sciencedirect.com/science/article/pii/S1389128619311235
Li, Y., Huang, G. Q., Wang, C. Z., & Li, Y. C. (2019). Analysis Framework of Network Security Situational Awareness and Comparison of Implementation Methods. EURASIP Journal on Wireless Communications and Networking, 2019(1), 1-32.
Humayun, M., Niazi, M., Jhanjhi, N. Z., Alshayeb, M., & Mahmood, S. (2020). Cyber security threats and vulnerabilities: a systematic mapping study. Arabian Journal for Science and Engineering, 45, 3171-3189. https://link.springer.com/article/10.1007/s13369-019-04319-2
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