We want to provide a basic understanding for those wondering what big data is. Information has been one of the most valuable resources throughout history. Data is a collection of raw, meaningless, and disorganized information. This assumption has become even more important in our age, which is rapidly digitalizing and producing large amounts of data. In this context, the concept of “Big Data”, according to experts, arouses interest in the sense that it reveals highly probable results regarding future events in the real world (1).
While big data can be called “a body of structured, semi-structured and unstructured data produced with high volume, speed, and diversity” (2), big data analytics is basically the process of evaluating large and diverse data sets frequently used by traditional business intelligence and analytical programs. The information evaluated during this time may include social media content, logs, or a mix of unstructured data such as clickstream data made on the internet.
How is Big Data Processed?
When we look at the techniques used in big data analytics, we see that methods such as data mining, machine learning, artificial learning, statistics, and natural language processing are used. Data analysis tools are generally used by analysts, researchers, and engineers to access business data efficiently by companies. The complex process of big data analytics often involves complex applications that include elements such as statistical algorithms and, if available, analysis and predictive models driven by high-power computing systems, high-performance analytical systems, and specialized software.
Another purpose of using Big Data analytics is to discover relevant information (consumer preferences, market trends, unknown correlations) that can help a business or institution make informed decisions. Once done, it can enable a company to increase operational efficiency, improve customer service, develop and implement more effective marketing strategies, find new revenue opportunities, and gain a greater competitive advantage over competitors.
Using Data Analysis in Cyber Security
For a long time, small & medium-sized businesses (SMBs) were considered an unlikely target for sophisticated cyber attacks. But now, both the place of SMEs in supply processes and their weight in economic activities have made them the target of cyber attacks. In short, company size or smallness no longer matters for attacks.
The companies that are exposed to these attacks and are most targeted are those that generally do not use strong encryption technology and focus on doing more business online through cloud services. On the other hand, as cyber-attacks increase in number and complexity, and all types of companies become targets, “traditional” tools and methods may no longer provide adequate protection. At this point, big data analytics comes into play and promises a new potential in terms of cyber security solutions.
Big Data and Cyber Security
Business data and information are among the most valuable assets any organization has. Entrepreneurs have begun to realize the increasing importance of this data for business success in the market. Mobile devices and wearable technologies now help businesses collect large amounts of data about their users. This is where security and privacy really become a concern. In this context, entrepreneurs continue to look for ways to keep sensitive business data away from the access of cyber attackers.
Companies are already using big data analytics to combat ever-evolving, sophisticated cyber threats resulting from the increasing volume of data on a daily basis. The use of big data analytics and machine learning enables businesses to perform a complete analysis of the collected information. The results of the analysis can give clues about possible threats to the integrity of the business.
In this context, for companies and researchers, big data is seen as both a threat and an opportunity. As a matter of fact, while the increase in data volume whets the appetite of cyber attackers, big data analytics stores large amounts of data, making it easier to perform data analysis to help analysts examine, observe, and detect irregularities within a network. On the other hand, security-related information obtained from big data analysis shortens the time required to detect and resolve a problem. In this context, it helps cyber security analysts to predict the possibilities of intrusion and attack while making it easier to protect against attacks.
Big Data and Its Contributions
According to the research of CSO, which conducts research on security and risk management, 84% of institutions and organizations use big data analytics techniques against possible cyber attacks (3). Additionally, according to further research, there is a significant decrease in security breaches after institutions and organizations start using big data analytics. Information from big data analysis tools is also used to detect cybersecurity threats such as malware, ransomware attacks, and vulnerable devices. According to experts, these are the areas where big data analytics is most promising in improving cybersecurity solutions and techniques.
Tools used for big data analysis work in real-time and can create security alerts based on their severity. Alerts can be expanded with more detailed details for rapid detection of cyber violations and mitigation of attacks. Data mining experts have a number of suggestions on how your business can benefit from big data analysis to eliminate cybersecurity threats. These include analysis of historical data, monitoring and automating workflows, and intrusion detection systems.
Cyber Security with Business Data
Analysis of Historical Data
It is possible to use historical data to predict upcoming attacks. Using big data analytics, businesses can develop informative baselines of what is and is not true for businesses. With such a comprehensive analysis, patterns of change can be observed using the data collected. The risk assessment can help businesses take counter-attack measures, along with a quantitative estimate of susceptibility to cyber attacks. In this context, it is also possible to analyze historical data and create statistical models and artificial intelligence-based algorithms.
Big data analysis, when combined with machine learning, opens up a whole new range of possibilities for cybersecurity. While big data analytics collects, analyzes and processes data based on previous cyber attacks, it is possible to use big data analytics and machine learning features together to detect vulnerabilities in networks, discover real-time breaches and correlate information from multiple sources.
Monitoring and Automating Workflows
Studies show that many cybersecurity threat incidents are a result of employee breaches, also known as “insider threats.” With the use of big data analysis, it is possible to significantly reduce the risk of threats originating from within the company. This is because big data techniques play an important role in monitoring workflows and automating jobs.
Another way to reduce insider threats is to limit access to sensitive information only to authorized employees. In other words, one staff member to view files and modify data and a few staff members authorized to use certain system applications may be sufficient for this task. It is important that the system administrator works together with the human resources department, which provides different login information depending on the responsibilities of the authorized personnel to the job and the level of complexity in the job descriptions.
In this context, big data analytics can play a major role in reducing cybersecurity breaches caused by business employees. This method can also help by facilitating the timely and effective submission of any suspicious events to a managed security service for additional analysis. The automation aspect, on the other hand, can enable the system to quickly respond to detected threats such as malware attacks.
Intrusion Detection Systems
Big data analysis can discover irregularities and suspicious activity through network flows, logs, and system events. Considering the increasing complexity of cyber breaches, the use of intrusion detection systems such as NIDS (network-based intrusion detection systems) is highly recommended as they are much more powerful in detecting cyber security threats.
On the other hand, cybersecurity is largely directly related to risk management and actionable intelligence provided by big data analysis. Although it is essential to have tools that can analyze data, the inability of these tools to automate tasks and transfer important data to the right people quickly is an important vulnerability.
Big Data Analysis and Cyber Security
While big data is crucial to the success of your business, these methods may not have much impact for threat analysis if the data is poorly processed or not processed at all. Still, research indicates that big data analytics solutions supported by artificial intelligence and machine learning promise businesses to stay safe against hacking or cybersecurity breaches.
These methods can also enable data analysts to classify cybersecurity threats without the long delays that can be caused by elements unrelated to the attack at hand. By using the potential of big data analytics, it is possible to improve your cyber threat detection mechanisms and data management techniques.
Sometimes vulnerabilities in an infrastructure are right in front of analysts and operators, but they manage to remain in systems unnoticed. Operating systems, services and application errors, improper configurations and risky end-user behavior are some of the most common places where cybersecurity vulnerabilities exist. So, is it possible for your company to stay safe? In this context, there are basic ways to follow according to the opinions of experts.
Steps Towards Industry Security
Risk management
To improve your cybersecurity configuration, your tools need to be supported by intelligent risk management insights that big data experts can easily interpret. The main purpose of using these automation tools is to present data to analysts more easily and quickly. This approach will ensure that your experts can detect, categorize, and manage security threats without delay.
Visualization of Threats
Big data analysis programs can help you predict the class and intensity of cybersecurity threats. By evaluating data sources and patterns, it is possible to weigh and evaluate the complexity of a potential attack. These tools also allow you to use current and historical data to gain a statistical understanding of which trends are acceptable and which are not.
Modeling Forecasts
Intelligent big data analytics allows experts to create a predictive model that can issue an alert as soon as it sees an entry point for a cybersecurity attack. In this context, machine learning and artificial intelligence can play an important role in developing such a mechanism. Analytics-based solutions will allow you to predict and regulate potential events in your process.
Staying Safe
Infrastructure penetration tests provide insight into your business database and process and can help deter hackers away from the system. Penetration testing is a simulated malware attack on your computer system and network to check for exploitable vulnerabilities. Checking the capabilities of your process and your existing analytics solutions can be thought of as a practice exercise. Nowadays, penetration testing has become an important step to protect Information Technology (IT) infrastructure and business data. In this context, after structuring your policies and strengthening your process, it may be the right step to conduct a new penetration test to measure the effectiveness of preventive measures.
Resources:
1) Egis Bilişim, “Büyük veri siber güvenliğin geleceğini nasıl etkileyecek?”, Erişim: http://www.egisbilisim.com.tr/buyuk-veri-siber-guvenligin-gelecegini-nasil-etkileyecek/
2) Ertuğrul AKTAN, “Büyük Veri: Uygulama Alanları, Analitiği ve Güvenlik Boyutu”, Ankara Üniversitesi Bilgi Yönetimi Dergisi, 2018, Erişim:https://dergipark.org.tr/en/download/article-file/482194
4) https://www.dataminingapps.com/2019/05/using-big-data-analytics-in-cyber-security/
5) https://www.csoonline.com/article/3139923/security/how-big-data-is-improving-cyber-security.html