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Forecasting Future Terrorist Warfare
Dr. Joshua Sinai

It is important to be able to forecast the categories of warfare that one’s terrorist adversary is likely to conduct. Such a forecasting capability is especially crucial when complete strategic, operational and tactical information about one’s terrorist adversaries is lacking because of the difficulty in covertly penetrating them with ‘human intelligence’ (HUMINT) or even with ‘electronic intelligence’ (ELINT). These are the information gaps that forecasting methodologies and technologies are intended to fill.

At the same time, however, it is important to distinguish between the categories of terrorist warfare that can be forecasted at the strategic and operational levels, even when only incomplete information is available, such as whether an adversary is a centrally organized group, a loosely affiliated local network, or a lone actor; whether a terrorist adversary is likely to employ weapons that will result in conventional low impact (CLI), conventional high impact (CHI), or weapons of mass destruction (WMD) consequences; whether the general tactics, techniques, and procedures (TTPs) will involve firearm shootings or martyrdom suicide explosive-type attacks; and whether the attacks will focus on strategic targets that are generally hardened or targets of opportunity that are considered “soft” because of the relative ease of attacking them. Forecasting likely terrorist warfare at the tactical level, however, is more difficult, such as the specific types of weapons and devices that would be selected, specific targets to be attacked, and the specific dates and timing of such attacks.

This is also one of the distinctions between forecasting and prediction, with prediction involving asserting, at the tactical level, the likely occurrence of an event with certainty by focusing on the where, how and when of potentially imminent attacks, while forecasting is conducted at a more general level, although it still aims to uncover tactical level activities.
The development and deployment of effective forecasting methodologies and technologies that can provide as complete as possible information about the terrorist adversary at the strategic, operational, and tactical levels are, therefore, the goals of effective counterterrorism. The development of such a forecasting capability is important because numerous high visibility terrorist attacks could have been preempted and thwarted if the law enforcement and intelligence agencies that had attempted to track these groups and operatives had possessed such a forecasting capability to thwart them during their formative pre-incident planning and preparation phases.

Examples in the United States of such missed opportunities for preemptive intervention in the past where the “dots were not connected” prior to terrorist attacks include 9/11’s attacks against the World Trade Center and the Pentagon; Major Nidal Hasan’s shooting rampage at Fort Hood, Texas in November 2009; the April 2013 Boston Marathon bombing; the husband’s and wife’s shooting rampage in San Bernardino, CA, in December 2015; and Omar Mateen’s shooting rampage at the Pulse Nightclub in Orlando, Florida, in June 2016. In each of these incidents some or all of the perpetrators were known to counterterrorism authorities but their suspicious pre-incident indicators did not arouse sufficient actionable “red flags” to prevent them from slipping under the radar to “go operational” in their attacks.

In such situations where attack-related information may be incomplete about a suspect’s potential violent activities, forecasting methodologies and technologies are especially crucial because they can identify and map the trajectory of intentions, agendas, and preparatory activities that terrorists, whether centrally organized groups, loosely affiliated local networks, and lone actors undertake to carry out their planned operations.
Forecasting methodologies and technologies that are valuable in enabling counterterrorism practitioners to forecast likely terrorism by their adversaries include (although not limited to) the following:

Geo-spatial analysis – applies statistical analysis and other informational techniques to predict the geographic location and frequency of future terrorist attacks by accumulating such data on a group’s previous operations. One drawback of this methodology is that it may have a higher predictive value in regions where terrorist incidents are frequent, such as Afghanistan, Iraq, Nigeria, or Somalia, than in Western countries where such incidents are rare events.

Data mining surveillance – forecasts terrorism by “mining” large volumes of data in open source and classified databases. It attempts to uncover anomalies and potentially suspicious behaviors and transactions that might indicate a progression towards possible terrorist activity by individuals, whether operating as centrally organized groups, loosely-affiliated networks, or lone actors (or a mix of them), including other associates who might be part of such plots.

Project management tools – terrorist operations are approached as a project management model, consisting of estimated tasks that need to be completed (whether in sequence or in parallel with each other), precedent relations between tasks, and task schedules. These tasks are operationalized through PERT and Gantt charts, with Monte Carlo sampling and Bayesian probability formulas applied to estimate the likely activities involved in planning an attack (e.g., intent, planning, preparation, attack, and the aftermath) and the schedule or timeframe of a potential terrorist operation and the intervention points required to thwart it.

Social network analysis (SNA) tools – these tools extract, correlate, and then visualizes sets of data that are mined from various databases about different types of relationships and transactions between individuals at various levels in an organization who might form terrorist networks, whether as networked clusters (region based), hubs (group-based), or nodes (cell-based). Although they may not have attained such a capability at present, eventually SNA tools are expected to incorporate biographic, psychological, demographic, religio-ethnic, and other relevant social data in order to map more complete dynamic relationships and profiles of individuals who might comprise a terrorist network.

Analysis of competing hypotheses (ACH) – an analytical methodology that is not yet fully developed as a software application. It attempts to generate the best available projection about a terrorist adversary’s potential warfare trajectory based on uncertain or incomplete data by using a matrix to identify at several alternative potential hypotheses (or scenarios) for each possible threat outcome, against each of which data is collected to validate or disprove them.

Red teaming – A group formed to forecast likely warfare by an adversarial terrorist group in which a team is designated as a terrorist adversary (Red Team), another team is designated as the counter-terrorist team (Blue Team), and a team that oversees the speculative exercise (Control Team). The objective is to improve a counter-terrorist organization’s effectiveness by testing how it would likely anticipate and respond to an adversary’s offensive actions.

Artificial intelligence - machine-learning algorithms, that, when combined with other software tools, such as social network analysis (SNA), can harvest vast amounts of data in multi-databases to quickly uncover and correlate potentially suspicious information such as an individual’s association with other terrorists (for example, by identifying such an individual’s contacts on the Internet with other accounts that might point to larger networks), suspicious financial transactions, foreign travel for training purposes, or purchases of weapons.


About the Author

Dr. Joshua Sinai is a senior analyst in terrorism and counterterrorism studies at Kiernan Group Holdings (KGH), in Alexandria, VA.



 

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