Big data analytics, as used in defense, is the capacity to gather vast amounts of digital data for analysis, visualization, and decision-making that might aid in anticipating and preventing cyberattacks. When combined with security technologies, it improves it position in terms of cyber defense. They enable companies to identify behavioral patterns that point to network dangers. With its potent capabilities to tackle the increasing scope, variety, and complexity of cyberthreats, big data analytics has become a disruptive force in contemporary cybersecurity. Traditional data processing methods fall short in managing the massive volumes, varieties, and velocities (3Vs) characteristic of big data. This paper explores the foundational principles of big data analytics, including its core dimensions and key application areas such as healthcare, transportation, finance, education, and social media. The study further investigates the classification of cyberattacks malware, phishing, ransomware, and advanced persistent threats (APTs) and their evolving complexity due to AI-powered automation, IoT proliferation, and multi-vector intrusion techniques. It is highlighted how crucial big data is to supporting real-time threat detection, predictive modelling, and automated incident response. Techniques such as behavioral analysis, threat intelligence integration, and anomaly detection are examined for their effectiveness in identifying sophisticated attacks like polymorphic malware and zero-day exploits. Ultimately, this paper highlights how big data analytics enhances cybersecurity capabilities by delivering predictive, prescriptive, diagnostic, and cyber-specific insights that empower proactive threat mitigation and ensure digital resilience.

Data system security has emerged as a top concern for businesses throughout the globe in today's fast-paced digital environment. The increasing sophistication and frequency of cyber-attacks necessitate more sophisticated and flexible forms of defense measures to deal with them. Machine learning, deep learning, and behavioral analytics are all forms of Artificial Intelligence (AI), which has become a revolutionary area of cybersecurity. This paper looks at how AI can enhance the security of a data system in general, and how it can be applied in threat detection, intrusion prevention, malware analysis, and predictive security, in particular. The intertwining of AI technology allows automated responses to potential threats, real-time anomaly detection, and enormous amounts of data processing. This paper gives an informative review of the extent to which AI is transforming the cybersecurity space and enhancing cyber infrastructures through investigating the latest progress and deployment plans.

The rapid development of the Internet of Things (IoT), a network of interconnected devices and sensors, has improved operational efficiency, comfort, and sustainability in smart buildings. However, relying on interconnected systems also introduces cybersecurity vulnerabilities. For instance, attackers can exploit zero-day vulnerabilities (previously unknown security flaws), launch Distributed Denial of Service (DDoS) attacks (overwhelming network resources with traffic), or access sensitive Building Management Systems (BMS, centralized platforms for controlling building operations). By targeting critical assets such as Heating, Ventilation, and Air Conditioning (HVAC) systems, security cameras, and access control networks, they may compromise the safety and functionality of the entire building. To address these threats, this paper presents a cybersecure intelligent sensor framework to protect smart buildings from various IoT-related cyberattacks. The main component is an automated Intrusion Detection System (IDS, software that monitors network activity for suspicious actions), which uses machine learning algorithms to rapidly identify, classify, and respond to potential threats. Furthermore, the framework integrates intelligent sensor networks with AI-based analytics, enabling continuous monitoring of environmental and system data for behaviors that might indicate security breaches. By using predictive modeling (forecasting attacks based on prior data) and automated responses, the proposed system enhances resilience against attacks such as denial of service, unauthorized access, and data manipulation. Simulation and testing results show high detection rates, low false alarm frequencies, and fast response times, thereby supporting the cybersecurity of smart building infrastructures and minimizing downtime. Overall, the findings suggest that AI-enhanced cybersecurity systems offer promise for IoT-based smart building security.

The shift to the sixth-generation networks (6G) presents new demands of automation and intelligence and trust of heterogeneously linked edge worlds. This paper describes a proposed framework of the Secured and Standardized Zero-Touch 6G (SS-ZT6G) incorporating Zero-Trust concepts, blockchain-security audit and AI-driven orchestration to allow autonomous, resilient and standards-compliant edge services management. The framework uses closed-loop automation and predictive orchestration as well as standardized APIs, in alignment with ETSI ZSM, 3GPP MANO, and O-RAN specifications, to achieve interoperability and compatibility of service provisioning. Empirical tests indicate a 44.9% decrease in latency, an increase of 9.8% in efficiency of automation, and 97.5% security detection accuracy as compared to reference architectures. The findings confirm that SS-ZT6G has the potential to deliver a secure, interoperable, and self-optimizing backbone of Edge-AI-based 6G ecosystems, which will create the possibility of sustainable and intelligent network autonomy.

Cybersecurity plays a crucial role in protecting the critical infrastructure of a nation, especially systems that are essential to public