An Overview Of Analyze Data Sources In Cybersecurity
By Tom Seest
Data analysis is the practice of turning unstructured information into meaningful and useful knowledge, helping businesses identify cybersecurity threats, risks, and incidents to make more informed decisions that reduce their risk of security breaches.
Data analytics for cyber security has an established history and remains highly relevant today as networked systems become more susceptible to attacks that disrupt or compromise functionality. With increasing threats against networked systems coming from within or without, detecting and preventing attacks requires statistical methods with which researchers can detect attacks rapidly – an area of research where scalable statistical approaches play a vital role.
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Table Of Contents
Computers, networks and other IT systems produce log records – also called audit trail records or logs – which record system activities. Organizations use them to monitor risks proactively and reactively mitigate them while complying with security policies, audits, regulations and understand user behaviors online.
To be useful, data must be clean, organized, and structured – that means filtering, normalizing and tagging before analysis so analysts can more efficiently find patterns and trends within logs.
Log analysis can be time-consuming and laborious without an effective log management tool, but there are solutions out there that can make this task simpler and faster.
Sematext Logs, for instance, is a log management platform which collects and searches logs from multiple sources – libraries, platforms and frameworks alike. It enables efficient anomaly detection as well as real-time alerting capabilities due to correlation of logs with infrastructure metrics and application metrics.
Correlation analysis can also help decode events not visible in one log. This technique is especially helpful during and after cyber attacks when correlating logs from network devices, servers, firewalls, and storage systems can reveal information pertinent to one specific event.
Rule-based alerting cannot detect all threats; rule-free detection provides more reliable alerting that reduces alert fatigue. Rule-free detection also offers the potential of discovering threats which are otherwise missed with traditional rule-based alerting systems.
SIEM solutions go beyond log management by performing advanced data analysis, automating threat hunting and incident response; making them ideal choices for larger organizations needing a stronger cybersecurity posture.
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As cyberattacks increase, security teams must remain vigilant for suspicious activities. One effective method for doing this is through behavioral analytics tools.
User behavior refers to the actions taken by individuals and other entities within an enterprise network – this could include servers, endpoints, applications and any digitally connected devices such as phones.
These activities are measured against a standard format known as a profile and any deviations can serve as a warning sign that something might be amiss. UEBA solutions collect, analyze, and track this data to detect anomalies which could pose potential threats.
UEBA stands for User and Entity Behavior Analytics, a subset of cybersecurity practices used to detect insider threats. Often utilized alongside other security tools like SIEM technologies.
These solutions can help identify unauthorized access to business-critical data by employees or external hackers and assist organizations in complying with data privacy regulations.
However, detecting security threats such as these can be challenging; according to research conducted at Columbia University it’s more challenging than ever before to detect an insider threat than an external attacker.
Therefore, it’s essential to implement a system capable of analyzing user activity and other behaviors that might indicate cyberattacks. Such technology allows you to pinpoint potential sources of attacks before they even happen, potentially saving both money and time in the process.
To properly analyze user behavior, it is crucial to recognize individual differences among people. There are various psychological factors which can impede compliance with security policies – these may include optimism bias, procrastination and risk taking among others.
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Network traffic analysis is a measure that monitors data flowing across a network to detect security threats such as malware, data exfiltration and attacks. In addition, network traffic analysis can identify bottlenecks and find solutions for network issues.
Step one of analyzing network traffic involves identifying all of its data sources, and application and network discovery is often the solution for doing this effectively. Examples of such discovery mechanisms include SNMP, flow-based protocols, Windows Management Instrumentation (WMI), and transaction tracing.
Once the data sources have been identified, you can choose how you’d like to collect and analyze network traffic. Two broad methods exist – agent-based collection or agentless collection.
If you don’t like agents, agentless data collection could be an ideal solution for you. This method involves tapping existing sources within your enterprise without adding new infrastructure.
At its core, this process involves employing network sensor technologies to capture live network traffic and feed it to your analytics tools for analysis. If any suspicious or malicious traffic is identified through this approach, providing a feedback path if appropriate action need to be taken against it.
Consider setting your network traffic analysis system so that more alerts will come through when something goes wrong – this will allow you to detect issues quicker and decrease IT team workload.
When analyzing network traffic, it’s important to pay attention to both its content and context. For example, when investigating an SQL injection attack against a website service, network flow data as well as pieces of network information like firewall records or IDS alerts must all be reviewed closely.
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Analyst Security Events can be an essential process in helping organizations understand their risks. It involves collecting data from systems, tools, and people both inside and outside the organization as well as detecting precursors (signs that an attack might happen in the near future) and indicators (data showing an attack has occurred or is occurring now).
Discovering cybersecurity incidents requires creating a baseline of normal activity on affected systems, correlating related events and monitoring whether they differ from that baseline. With this knowledge in hand, one can be better prepared and respond more swiftly when an incident arises.
Monitor firewall logs carefully as an early indication of potential cyber attacks, and watch out for spikes in either outgoing or incoming traffic, or packets from IP addresses unfamiliar to your organization that suggest any possible security vulnerabilities.
As well as monitoring for security events, an incident management system must also be in place. Depending on the nature of an incident, alerts may need to be sent out to relevant parties who must take immediate action.
An information security incident can cause serious problems for a company, from data loss and destruction of reputation to loss of credibility and business continuity. Therefore, it’s essential that events are closely monitored so as to avoid these incidents altogether.
Common security events include changes to user permissions on servers; modifications made to firewall configurations and database tables, as well as modifications made by hackers that compromise sensitive information such as employee biometric data or customer records. Such attacks could prove fatal.
As cybersecurity threats increase in frequency and sophistication, businesses and organizations require effective ways to manage security incidents. They require cybersecurity data and metrics which allow them to effectively identify risks within vendor networks and protect sensitive data more securely while mitigating risks more efficiently in order to prevent future incidents from arising.
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Business analytics is an emerging discipline, which relies on data mining techniques and insights gleaned from historical records in order to create actionable business strategies. Businesses using analytics can utilize this information to increase operational efficiencies, gain greater customer insight, project future outcomes more accurately, gain decision-making insights that aid decision-making processes more easily, measure performance more precisely, drive growth more swiftly and discover hidden trends more readily – ultimately leading them towards leads generation and conversions.
Business analytics involves applying statistical techniques, modeling, and optimization techniques to gather, organize, manage, and interpret business data in order to predict future results and present it to non-technical employees in a format they can understand; such as through visualization.
Business analytics was traditionally limited to those with computer science and programming backgrounds; however, new tools make it easy for workers without these backgrounds to perform meaningful analyses. One such tool is Alteryx which offers an end-to-end self-service analytics platform which eliminates barriers and allows employees with different skill sets to quickly analyze data and make decisions efficiently.
As well, business analysts can apply their skills to cybersecurity by identifying potential threats, risks, and incidents – this helps business professionals assess what changes may be necessary in order to safeguard their systems while keeping them running optimally.
Descriptive analytics utilizes historical data to reveal trends and key performance indicators (KPIs), giving an in-depth picture of what has occurred. This approach can help organizations discover new opportunities while optimizing existing processes – leading to faster analysis, higher data quality and better decision-making processes.
As cybersecurity attacks proliferate, business analysts must equip themselves with the skills and knowledge to properly analyze and address them. With that goal in mind, IIBA and IEEE Computer Society have collaborated on providing robust learning and certification on cybersecurity analysis.
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