Intelligence analysis methods include induction, deduction, abduction, and the scientific method to derive conclusions from data.
Induction
Inductive reasoning is central to intelligence analysis: here, analysts start from specific, concrete stories in order to construct more general explanations and theories. It is a great method to get connected when we are in environments that are data-rich but knowledge-poor.
Mapping out the cybersecurity threat
Induction is critical in cybersecurity for understanding potential threats. An example would be if an intelligence analyst at a financial institution sees an uptick in phishing attempts against their own employees. Upon further examination, the analyst discovers that these emails are both more frequent and increasingly devious, often mimicking internal communications. However, this observation in particular supports the theory that the force is being targeted by a financial fraud-based
The analyst supports this hypothesis using data on where the infiltrated emails were coming from, when attacks occur, and which departments are targeted. From this data a number of patterns emerge, one being attack times peak at a particular point in the financial quarter, which can indicate that the attackers are aware of a company’s financial cycle.
Data Analysis in Induction
Good inductive reasoning in intelligence hinges to a great extent on careful data analysis. They collect large datasets from various places such as network traffic logs, previous phases of compromised security documents, employee reports about the suspicious activity and the feeds of external threat intelligence. Advanced statistical tools and data mining techniques are used to identify patterns that may not be readily apparent upon cursory review.
If an analyst sees that industries like theirs have gotten hit with ransomware, they might conclude their own org is at a higher chance of getting attacked. A plausible alternative is that the software in use within both their organization and those attacked is so widely deployed and provisioned that this truth could be backed up by the data.
Inductive Reasoning Process
Inductive Reasoning as a Process in Intelligence AnalysisInductive reasoning is used not as an endpoint or end result, but rather as a process that systematically iterates through several critical steps.
- Data Collection-Aggregating data from verified sources creates a massive surface area for pattern recognition.
- Pattern Recognition – Analysts will use analytical tools to hunt for specific patterns or anomalies within the data. For example, this might mean spotting a pattern of out-of-sorts outbound network traffic or seeing several late-at-night unsuccessful access trials.
- Develop Hypotheses: From observed patterns, analysts generate hypotheses. One example of this would be a significant % data packet loss, which could perhaps lead you to believe you are breached.
- Test and retest: Hypotheses are retested using new and existing data. The latter will be explored through iterative refinement until either the hypothesis is validated or adjusted Efficient Testing usually means simulating specific scenarios or doing a post-mortem analysis of similar (earlier) incidents.
Deduction
In intelligence analysis, deductive reasoning begins with a general theory or hypothesis, and examines the specific instance to see if the established rule holds. This is crucial, especially when models are validated against observed events or data.
Terrorism Threat Assessment
One common intelligence application of deduction is in evaluation of terrorist threats. Intelligence agencies’ starting point might have as a general hypothesis: “All groups that have a motive hostile to our country polices are potential threats” This blanket statement is then interpreted by analysts to identify key groups who have historically opposed these policies, and intelligence is collected against those specific groups in order to ascertain if they are planning an attack.
How It Works
Intelligence analysts use protocol to implement deductive reasoning:
- Start with an Established Theory or Understanding. A hypothesis, for example, could be that in case extremist groups are known to use a specific encrypted messaging app then the new groups using this app may tend to act as the former.
- Data Collection: If changed a lot, it should be about the data relative to the hypothesis. This could mean listening into communications, tracking financial transactions or keeping an eye on travel – all of which might lead to suspicious associations being identified.
- Explanation: Is the Data In Line with the Hypothesis Whether as specific as decrypting messages, following the money trail or connecting travel data with past events.
- Validation: Confirm results with cross-referencing of other intelligence reports or through joint work with foreign intelligence agencies.
Deduction in Cybersecurity
When it comes to cybersecurity, deduction helps us anticipate the behavior of an attacker. To cite an example, if the hypothesis is “Attackers generally open up more access within 24 hours of initial breach,” then security teams can pay closer attention to fresh breaches detected to pre-empt additional unauthorized activities.
Abduction
Abduction (also called abductive reasoning or abduction hypothesis) is a critical thinking process for intelligence analysis when you have an incomplete set of observations and you need to infer the most likely explanation. More-reluctant interpolations, related to less concrete instances are especially good cases for this mode of theory building.
Unidentified Aerial study
An example of abduction in intelligence is apparent in analyzing Unidentified Aerial Phenomena (UAP). Analysts may base their work on unexplained aerial sightings, such cases are often promulgated by military or intelligence prospects as possible unidentified flying invasion when conventional explanation can’t explain the evidence such as dinosaur fossil aircraft-hunting platform or atmosphere observation balloon.
- Finding: Intelligence receives comprehensive briefings on UAP reports, which include detailed reporting of incidents, intelligence collection, analysis and signals intelligence.guilden_rose/U.S. Marine Corps.
- Hypothesising: Many different hypotheses are put forth by analysts to reasonably account for the observations – from experimental military technology, foreign espionage devices and un-logged atmospheric phenomena.
- Most Plausible Explanation: The most plausible explanation is the hypothesis that fits better with all data and supports the current information. E.g., if a UAP exhibits technology well/beyond known modern tech, the hypothesis may trend toward experimental military tech.
Implement in a Spy Detector
It is also used to detect espionage through an abduction. When an unusual communication pattern occurs or a critical secret is leaked, the intelligence service disappears to solve the problem.
- How was data obtained: Gather all available data regarding the incident such as access logs, email traffic and security camera footage.
- Pattern Analysis: Recognition of any anomalous patterns that could be pertaining to espionage activities, for e.g. accessing a system at very odd hours, unusually high volumes of downloads or speaking /communicating with foreign numbers you have never seen before.
- Generating Explanations: Create a broad-based account for the patterns found. For example, if a pattern shows there was a security breach at certain times each day and they match up with the schedule of one employee in particular, the hypothesis could zero in on that person.
Scientific Method
Europe sketches out scientific method The resolution the European Union approved suggests a scientific approach be used to analyze intelligence. The approach is aimed at basing conclusions on trustworthy and repeatable data.
Signal Intelligence Experimentation
Signal intelligence (SIGINT) employs the scientific method to prove/disprove our theories around how adversaries communicate and hide their communications.
The Analyst will make note of an anomaly in the suspect foreign entity transmission pattern.
- Question and Hypothesis formulation: Analysts could question if this anomalous is a reflection of a new method of encryption. The speculation states that maybe the organization has created a new security encryption for its communication streams.
- Experiments: For instance, analysts could perform the encryption that they think might happen based on data patterns and see if they can decrypt it (or somehow reproduce the anomaly).
- Outcome Analysis: Results of these experiments are analyzed. The ability to successfully replicate the communication pattern would either confirm that hypothesis, or if not, require a change in our theory.
- Iterative testing: More than one round of tests helps sharpen the analysts’ thinking and may or may not cause them to revise their original idea or come up with new ones.
Use in Counter Terrorism Operations
An effective and necessary piece in the war on terrorism: The scientific method is very important to counterterrorism, where it is used to gauge how effectively a broad array of security measures actually contributes to protecting life and property.
- Collection of data: Collection of data of previous terrorist attacks on modalities, regularity, spots attacked and the reply options obtained by safety mechanisms.
- Analysts come up with a hypothesis that adding particular security mechanisms at critical points… – in which they think it may reduce frequency or severity of certain types of attack.
- Testing & Implementation: Begin a pilot implementation of security initiatives.
- Observation and Data Collection: Lookout for variations in the pattern of attacks no also obtain data on accurate number of incidents before and after implementation of measure.
- Conclusion: Analyze and Review the numbers to deduce how useful your security measures are. Tackle Approach based on the outcome to infuse efficiency in counterterrorism strategies.