For the billions of entities connected via IoT, traditional firewalls and skilled network security professionals do not guarantee network infrastructure security. The utilization of traditional and newer systems together makes the diagnosis of a breach of security difficult primarily due to the current system’s inability to process the immense data generated.
Defense resources comprising network device events, logging, file integrity monitoring, and compliance reporting, are limited in their monitoring and anomaly detection capabilities. The National Institute of Standards and Technology recommends continuous monitoring and real-time assessments through Big Data analytics. Application of predictive analytics with the optimization and automation of the existing SIEM systems are also recommended to find threat locations, leaked data identity, and destination.
Comprehensive visibility of both current and historical data for a company can help estimate the probability of cyber-attack. The massive volumes of data require companies to have high ingestion speed of massive volumes of data as possessed by the big data analytics tool Hadoop.
Data technologies in combination with data science and machine learning are considered preferable for the security of governmental and large organizations, with more than 90 percent of federal agencies intending to invest in big data technologies and more than 80 percent of big data users claiming threat reduction accomplishment. Despite billions of dollars worth of estimated growth, the global cyber security market faces obstacles such as lack of skilled workforce and adequate infrastructure. Consequently, developing economies should encourage investment in big data analytics tools, infrastructure, and education to maintain growth in areas such as mobile/cloud security, threat intelligence, and security analytics, to inspire innovation.