A major challenge that security teams face is to operationalize the massive volume of threat indicators of compromise (IOCs) ingested from multiple threat intel sources. These IOCs usually include many false positives and noisy data points. Deriving contextual and actionable threat intelligence from this raw threat information can be a laborious process if these ingested IOCs are not automatically correlated. Security teams need to be quick and meticulous in decision-making. To correlate IOCs for threat intelligence contextualization, confidence scoring plays a significant role. Confidence level helps eliminate false positives and prioritize activities related to rising threats. With the aggregation of massive threat intelligence, confidence scoring has become essential for security teams who must leverage robust threat intelligence platforms that can help them improve their threat detection and operationalize the intelligence in a useful way.
What is a Confidence Score?
Based on their maliciousness, IOCs are classified and assigned a rating, which is known as a confidence score. A confidence score is a value ranging between 0 to 100; while 0 confidence suggests that a IOC is non-malicious, a score of 100 suggests the indicator is highly malicious.
The confidence score allows security teams to filter through vast volumes of information from multiple sources and focus on relevant threats.
To calculate the confidence score, security teams need to have the following in mind:
Continuous Collection of Threat Feeds
Calculating confidence score with precision needs continuous flow of threat intelligence from external and internal sources that helps validate an indicator of compromise based on previous sightings. An automated threat intelligence platform enables such continuous ingestion to score threat data continuously based on validated intelligence.
Automating Confidence Score Calculation
With a vast ocean of threat indicators, the manual process of calculating confidence scores and prioritizing the relevant data is impossible in a timely manner. Therefore, automation is needed to help security teams utilize their time for value-added threat analysis. Such capabilities are offered by automated threat intelligence platforms (TIPs) which automatically ingest, enrich, correlate, and score threat data.
Parameters Affecting Confidence Score
The confidence score is calculated by a mathematical model that combines several parameters. Some of them are relations, source scoring, external enrichment, and sightings.
For each parameter, an individual score is calculated and the combined sum of these scores is the overall confidence score. The weightage of every score depends on the significance of the parameter and the availability of data.
Relations between objects based on the relation type provide input for the maliciousness factor in a connected threat intelligence platform (TIP). Relations represent the STIX relationship objects and connect multiple indicators and describe their relationship with each other.
This is a parameter that security teams use to add values to define the reliability of sources and subscribers that allows for more insightful inputs for confidence score evaluation. A cyber threat intelligence platform helps ingest such threat feeds from multiple sources, including APIs, RSS feeds, STIX sources, email, Twitter, etc.
External Third-Party Enrichment
The external enrichment score is calculated based on the data received from the enrichment feed sources. This is a parameter that allows security teams to define an enrichment policy and configure the tools that will be used for enrichment to boost confidence score.
Sighting of indicators reported by feeds or identified in the application gives assertions about how critical an indicator is while evaluating the confidence score. Source sightings indicate the unique number of times the indicators are seen in a threat intelligence platform and from different threat feed providers.
The Need to Automate Confidence Score
Connected threat intelligence platforms (TIPs) provide an intuitive approach to intel scoring, minimizing the need for customizing an extensive list of parameters manually. Influenced by multiple parameters, the complexity of confidence score calculation is automatically handled by a connected threat intel platform, making intel scoring effortless.
With the help of an automated confidence score, security teams can quickly determine the relevance of a threat based on a set of parameters. Moreover, automation creates a fine balance between control and convenience, fulfilling the needs of security teams looking for a seamless experience. Automated confidence scoring complements threat intelligence ingestion and enrichment processes and further reduces the burden on security analysts by eliminating manual intervention in threat intel scoring.
For any given threat data, a connected TIP calculates a confidence score between 0 to 100. The higher the score, the more significance it holds regarding the relevance of the threat, frequency of the threat, quality of the threat data, and its relation to a threat environment.
For example, a Florida-based organization wants to focus on IOCs that were shared by their trusted sources and were sighted more than 20 times in their region with red or amber TLP ratings. Automation of confidence scoring allows complete management of this and other such complex scenarios with ease.
Security teams must focus on how they can leverage threat intelligence platforms to automate confidence scores to prioritize their threat feeds and vulnerabilities. While confidence scores are great for reference, security teams must find a way to better utilize the threat indicators to create contextualized threat intelligence.
Benefits of Automated Confidence Scoring
Automated Threat Actioning
Advanced threat intelligence platforms enable security teams to automate actioning based on confidence scores. Security teams can build rules to automate proactive threat mitigation tasks such as blocking of IP in firewalls based on confidence scores.
Faster Threat Investigations
Confidence scores allow security analysts to generate finished intel reports by including tags TLP, MITRE ATT&CK mapping, and investigations. These reports can be employed to create contextualized and rich intel, helping analysts to expedite their threat investigations.
Contextual Threat Information Sharing
With confidence scores in hand, security analysts can create and share threat bulletins with their subscribers, members, or other organizations, equipping them with the right threat data for investigations. Threat Bulletins enable security teams and stakeholders to make smarter business decisions while helping them keep pace with the evolving threat landscape.
Automate Proactive Threat Actioning with Cyware’s Confidence Scoring Engine
Cyware Threat Intelligence eXchange (CTIX) is a next-generation connected threat intelligence platform that comes with Cyware's Confidence Score Engine enabling security teams to automate actioning, sharing and investigation of threat data. The Confidence Score Engine runs on proprietary statistical data models that take into account several factors influencing the relevance and malignancy of cyber threat intelligence. Using the Confidence Score Engine, security teams can automate threat intelligence actioning to proactively neutralize threats even before they impact. The platform enables security teams to derive contextual intelligence by scoring IOCs against tonnes of threat data and sightings from external and internal threat intelligence and enrichment sources. To learn more about confidence scoring, book a free demo!