Data Science Be Used In the Fight against Terrorism

Data Science Be Used In the Fight against Terrorism

In the late 20^th century, the world was fighting a new enemy, Terrorism. The enemy showed no mercy and would go to extreme lengths to make a point. The 9/11 terror attack in the US put an end to negotiating with terrorists.

Data science has come a long way. It has evolved in a manner that it can be used in security prevention.

In the era of big data, it almost impossible not to leave a data print. Terrorist are bound to make mistakes along the way. When they do, Data science pipeline will flag them off.

The RAND Database of Worldwide Terrorism incidents (RDWTI) and THE Global Terror Database contain necessary information on all the terror attacks that happened as far as the 1970s.In the era of Big Data, it is easier to collect, store and analyse data on a large scale. From the data, we can extract an individual’s activities and associations.

To be able to apply data science in the fight against terror, we need first to understand the motivation behind terror ideologies.

Cause/ Motivation for Terror Activities

Political

Initially, terrorism was theorised as an insurgency or guerrilla warfare. An organised non-state group is going on a rampage, causing civic violence. In such a case, the organised groups had settled on terrorism as a means of making right what they considered socially, politically or economically wrong.

The Vietcong chose terrorism as a means to an end.

Religious

The critical thing to note is, religions do not cause terrorism. A few groups of individuals selectively interpret and exploit religious scriptures for their cause. They in the Middle East, a new wave of terrorism was taking root Suicide bombings. The suicide victims were regarded as Islamic martyrs. Form 1980, the severity of terror attacks initiated were deadly. The concepts of Armageddon and martyrdom were the primary motivator.

Social-economic

Social-economic factors like poverty, unemployment and political oppression is a push factor. Vulnerable individuals in society find solace terror organizations. The Shining Path was motivated to liberate the people of Peru from social-economic exploitation.

A critical examination of terror groups and you realise that there is a simple theory motivating them.

Type of Terrorism

Individual or Group Terrorism

Experts in the field of terrorism say that groups and not individuals are more likely to cause terror activities. In the past two decades, experts on terrorism have seen trend activities. Terror groups are operating from societal and organizational networks.

Research in cultic behaviour and authoritarianism tendencies show that individuals are more likely to identify themselves strongly to groups. In the end, they lose their individuality.

Problem Formulation

To first establish the terror issue to predict, we first have to understand the conditionals that make terrorism to happen.

For starters, the history of individuals who join terror groups should be analysed. The trigger factor in one group will provide the needed information as to what to look for in extracting insights in big data.

Some of the conditions are:

• Psychological factors: Individuals may exhibit narcissistic rage that makes them inflict pain to others.
• Social-economic circumstances: poverty and social repression.

Though the existing condition can push an individual into terrorism, there are but, certain situations that convince disturbed individuals that violence against civilians is reasonable, and it is the necessary evil.

Where Can Data Science Be Used To Fight against Terrorism?

Formulation of the statement problem.

Since we know the cause and the motivation for terror attacks. The following are the possible statement we can use data science for prediction:

1. Classify whether an attack is a terror attack or not.
2. Classifying terror attacks to the causes or motivation.
3. Classify whether a lone gunman or a group carried the attack.
4. Classify whether an attack was carried out by local militia or foreign groups.
5. Classify whether an attack could have been detected earlier.
6. Classify whether there is a subsequent attack after an initial attack.
7. Classify signs of radicalization and violent extremism.

Terrorism is a multi-dimensional topic; it covers radicalization, violent extremism recruitment, financing and resource availability.

Since we have data, we can approach each problem one by one. It is now easier to extract trends like the association of data points.

Predicting a terror attack and the location of the attack is a difficult task. Data science can be used for rather simple tasks of detecting and preventing potential terrorism. This is before they actualize and materialize.

Going by this ideology, data science can be used to identify individuals who pose a threat accurately. Thereby reducing the number of individuals put under invasive monitoring.

In the next few months, we will look in detail about how data science can bust plotted terror attacks.