Project Category
Cybersecurity and Threat Detection System
Objective
A leading Belgium-based tech company requested an approach to provide enhanced cybersecurity and threat detection throughout its digital environment. As the client was
dealing with voluminous sensitive data, its protection against constantly changing cyber threats became crucial. In addition to these, this client was looking for an efficient system with real-time threat detection, in-depth incident analysis, and agile data management. It wanted its data integrity, reduced vulnerabilities, and strong security infrastructure across the system.
Technologies
Java, Ruby on Rails (RoR), Big Data, Apache Spark, Tableau
Country
Belgium
Project Attributes
Engagement Model
Contractual staffing
Duration
8 months
System Users
IT security teams, system administrators, and data analysts
Challenges
- Volume and Complexity of Threats: There was an enormous occurrence of
cyber attacks from unlawful access, leakage of information, and phishing. The
past system did not have enough capacity to monitor the volume of threats and
diversity at sometimes missing real-time attacks to flag innocent occurrences. - Constraints in processing data: As there existed a huge amount of information
from various systems in a log, it required so many hours to handle and try to
analyze them due to their volume. It was virtually impossible to process such
information in real time because the information related to attacks was not
recognized in advance and mitigated in sufficient time. - Very less visibility of threats: This traditional security architecture was not so
great at revealing deep analytical insights into the threat patterns. There was a
need for a centralized solution that would provide actionable insights from a
variety of data sources keeping IT teams ahead of emerging security risks. - No real-time reporting of incidents: Incidents were not reported properly, and
alarms were delayed. This made the security team aware of breaches only after
they had occurred, leaving systems open to potential damage.
Solutions
- Real Time Threat Detection with Big Data and Spark: Adorebits has
implemented Big Data-based real-time threat detection with Apache Spark. It will
have distributed processing of data so that the system can process the enormous
log data in real-time, detect potential threats almost in real-time, and reduce
detection times. - Advanced Data Visualization with Tableau: Adorebits utilized Tableau to
create a centralized dashboard that provided real-time data visualization on
security metrics, including frequency, source, and type of threats. This allowed
the security analysts to monitor and assess the risks visually, getting insight into
threat trends and rapidly addressing critical vulnerabilities. - Automated Anomaly Detection by Java and RoR: This was an anomaly
detection powered system by Java and Ruby on Rails feature. In essence, the
module identified any patterns running across these networks that might be
uncommon or suspicious.
Results:
- Improved Threat Detection Accuracy: The accuracy of the detection of threats improved to 50%, reducing the false positives and providing much more accurate alerts.
- Reduced Response Times: It cut the average response time for the incident by 40% in order to increase system resilience against cyber threats. With Big Data and Spark, the client could process big volumes of log data to increase data handling efficiency by 60%.
- Streamlined Data Management: With the Tableau-powered dashboard, the security teams could now monitor and manage threats efficiently to increase productivity by focusing on high-priority threats.
Conclusion:
Adorebits was able to help the client in strengthening their security infrastructure and protecting sensitive data by implementing a tailored cybersecurity solution. Through this, the client became positioned as a leader in the Tech industry for secure data management.