The Rise of Big Data Analytics in Cyber Defense
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The Rise of Big Data Analytics in Cyber Defense
Cyber defense is an increasingly complex operation in an increasingly vulnerable landscape. In the era of Big Data and the Internet of Things, data collection volumes are ever expanding, sometimes ingesting as much as a petabyte of security events per day, with ingestion rates only projected to increase exponentially over time. The connection of billions of devices through networks and clouds has created a vast surface of entry points to defend from cyber attacks. As the sophistication, volume, and variety of cyber attacks expands, the need for a robust, data-driven, real-time cyber security defense strategy is all the more pressing.
Outdated threat detection tools, intrusion response tools, and firewalls are not adequate solutions to defend against modern cybersecurity threats. Now more than ever, incorporating big data analytics into cybersecurity is an essential measure as business leaders are making fast and accurate threat detection a top priority. Big data analytics facilitates the fast processing of vast quantities of high velocity business data from several different sources. Fast processing of these enormous, disparate datasets is crucial for discovering anomalies and attack patterns as quickly as possible, limiting vulnerabilities in the system, and improving overall resilience.
The ramifications for cyber criminal infiltrations can be devastating. Big data security breaches can result in hefty penalties, enormous financial repercussions, irreparably damaged brand trust, total collapse of a business, and even jail time. Big data analytics platforms and machine learning empower cyber defense analysts to gather and better understand big data. Data analysis will reveal potential cyber threats and help business leaders better understand how to predict and prevent attacks with proactive cyber crime defense procedures.
Four Ways Big Data Analytics Fortifies Cyber Defense:
Predicting the Future
Big data analytics tools take business intelligence and cyber defense to the next level by incorporating elements like statistics, machine learning algorithms, predictive models, and computing systems to extract cyber threat intelligence to bolster proactive threat detection and response. Large volumes of structured and unstructured data is gathered from a multitude of sources, from which historical data is isolated and analyzed. Analyzing historical data can help cyber defense engineers create statistical models and Artificial Intelligence-based algorithms, and establish a baseline for normal activity, which will highlight anomalies and help predict cyber attacks.
Big data predictive analytics gathers historic data from previous cyber attacks, from which attack patterns and vulnerabilities can be identified. Attack patterns can be used to develop a tailored cyber threat defense response. A combination of analytic techniques such as artificial learning, data mining, machine learning, natural language processing, and statistics are utilized by big data analysts to detect variations from the norm to predict imminent attacks.
Big data analytics software, combined with data gathered from network flows, sensors, cloud systems, and security events, provide everything a cyber defense team needs to deploy an Intrusion Detection System (IDS), which enables businesses to detect and respond to intrusions in real time. There are two main IDS cyber defense system types: Network Intrusion Detection Systems, which analyze incoming network traffic, and Host-Based Intrusion Detection Systems, which monitor important operating system files.
IDS subtypes include Signature Based systems, which detect possible security threats by looking for specific patterns, such as malicious instruction sequences used in malware attacks; and Anomaly-Based systems, which use machine learning to design a defined model of trustworthy activity, against which new behavior is compared.
In order to handle the variety, velocity, and volume of data used in big data analytics, IDSs have evolved over the years from outdated systems into Security Information and Event Management (SIEM) systems, which are in turn further evolving, with the power of Machine Learning, to better handle diverse, unstructured, big data sets, and to reduce time to consolidation, correlation, and insights.
Advanced big data analytics solutions for risk management and cyber attack defense should include: unified data representation, zero-day attack detection, data sharing across threat detection systems, real time analysis, sampling and dimensionality reduction, resource-constrained data processing, and time series analysis for anomaly detection.
Automated monitoring and threat detection alerts play a crucial role in modern cyber defense operations. Automated cyber defense facilitates continuous monitoring of the environment, providing real-time actionable, risk management insights, cybersecurity threat intelligence feeds, and alerts for incoming threats. Big data analytics cybersecurity tools such as Endpoint Protection Platforms (EPP) and SIEM platforms provide continuous monitoring capabilities. EPPs are deployed on endpoint devices to detect, respond to, investigate, and prevent threats, incidents, and attacks. SIEMs collect logs and event data created within an organization’s internal infrastructure.
Many cyber threats originate internally and stem from employees accessing sensitive information without authorization. An effective means of mitigating this risk is limiting access to sensitive information by assigning credentials to authorized users. Automated monitoring gathers and analyzes user behavior data, and generates an alert when usual activity is detected.
Cyber Defense is for Businesses of Any Size
Big data is only as effective as our ability to adequately mine and process it. Combined with Machine Learning and AI, next generation accelerated analytics platforms help tame data to tackle cyber threats, and empower businesses to enhance their cyber defenses and keep their networks secure with solutions that can handle big data, such as real-time monitoring, automated tasks, interactive data visualizations, and the ability to explore threat intelligence data at unprecedented scale and speed. Replacing traditional security tools with big data analytics for cyber defense is a crucial measure for businesses of any size looking to prioritize information security with proactive, data-driven cyber defense technologies and procedures.
The OmniSci Difference
There are some major challenges facing cyber security professionals and data scientists in the field of cyber defense. For cyber security professionals, it is the ability to quickly visualize and analyze large amounts of data to identify network patterns and create rules. For data scientists, it is the ability to access the entirety of these large datasets during the feature engineering or data preparation phases.
This OmniSci Cyber Demo shows how OmniSci’s accelerated analytics and Machine Learning-ready platform addresses these challenges. OmniSci helps data scientists and cyber defense analysts access full datasets and advanced computation resources, effectively translate and articulate predictions made by the trained model, and integrate and visualize end-to-end ML pipeline for better understanding of the data.