Exam (elaborations)
CTI Midterm 2
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Exam of 26 pages for the course CTI - at CTI - (CTI Midterm 2)
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CTI Midterm 2-a simple and efficient algorithm for partitional clustering
-runs quickly and handles a variety of datasets well - ANS-K-means
-a stand-alone tool to get insight into data distribution
-a preprocessing step for other algorithms - ANS-typical applications of clustering in CTI
(general)
-all instances for a given node belong to the same class
-there are no remaining attributes for further partitioning
-there are no instances left - ANS-Decision Tree - When to stop partitioning?
-common to be shared via web or email
-not a standardized approach
-threat data structures vary
-threat data sharing protocols vary - ANS-intelligence sharing: malicious IP's
-convey the malware to the target, have launched their operation - ANS-Delivery: Adversary
(description)
-could be shared via web or email
-not a standardized approach
-threat data structures are standardized
-threat data sharing protocols vary - ANS-intelligence sharing: STIX data
-deploying a firewall rule based on abnormal activity on a port
-flagging specific emails as phishing based on its features
-automatically blocking a user account based on abnormal activity - ANS-examples of
automated proactive defenses
-detecting reconnaissance as it happens can be very difficult
-discovering recon (even well after the fact) can reveal the adversaries' intent -
ANS-Reconnaissance: Defender (description)
-detecting your devices exposed on Shodan --> putting them behind a firewall/NAT
-phishing emails being sent out --> notifying employees accordingly
-identifying suspicious user login --> blocking user login attempts
,-malware targeting your systems in forums --> updating anti-virus definitions - ANS-examples of
actionable intelligence
-Determining if an email is malicious or not
-Identifying a phishing website from a real one
-Internal hacker profiling
-Identifying malicious vs. benign network traffic
-Categorizing threat actors in social media - ANS-example applications of classification in CTI
-Determining if email is malicious or not
-Identifying phishing website from real - ANS-which of the following CTI apps can benefit from
classification?
-Determining if email is malicious or not
-Identifying phishing website from real
-Grouping similar types of network events together
-Grouping similar threat actors together in social media
-endpoint instrumentation to detect and log installation activity
-analyze installation phase during malware analysis to create new endpoint mitigations -
ANS-Installation: Defender (description)
-essential phase for defenders to understand
-though they can't detect weaponization as it happens, they can infer it by analyzing malware
artifacts - ANS-Weaponization: Defender (description)
-grouping similar types of network events together
-grouping similar threat actors together in social media
-categorizing similar log files together - ANS-clustering applications in CTI (specific)
-highlights attacker preferences alongside possible alternative paths
-enables better mitigation strategies by mitigating current threats and taking into account
reactions or alternate adversary tactics - ANS-key benefits of an activity-attack graph
-How it works
-How to identify it
-How to eliminate or defeat it - ANS-malware analysis aims to dissect malware to understand:
-huge volume of data, difficult to manually analyze, requires highly efficient computational
analysis
-the concept of an anomaly varies among application domains
-labeled anomalies are not readily available for training/validation
, -may trigger high false alarm rates
-hackers often modify their behaviors to make them similar to normal patterns - ANS-challenges
of anomaly detection
-identify files or registry keys on a victim computer that indicate an infection
-focus on what the malware did to the system, NOT the malware itself - ANS-host-based
signatures
-Intra-cluster distances are minimized
-Inter-cluster distances are maximized - ANS-What is good clustering?
-it is recommended to share within your industry (with partners, suppliers, customers, regulators,
etc)
-ex: if one university has details on attacks on/from another, then sharing that info will improve
the intelligence of both - ANS-intelligence sharing
-leverage collected data for relevant, timely, and actionable threat intelligence AND
provide insight and value out of the data to enhance cybersecurity decision-making - ANS-CTI
analytics aims to _
-malware visualizations
-threat dashboards
-network flow visualizations
-geospatial analytics - ANS-Visualization - example CTI applications
-must exploit a vulnerability to gain access
-'zero day' refers to the exploit code used in just this step - ANS-Exploitation: Adversary
(description)
-Naive Bayes
-Decision Tree
-Random Forest
-K-Nearest Neighbor
-Support Vector Machine
-Artificial Neural Networks - ANS-some of the more classic and state-of-the-art classification
techniques:
-not everyone within an organization needs to know about the threats
-depends on the types of threats detected - ANS-threat dissemination
-not suitable for datasets with differing sizes, densities, or odd shapes
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