this high unpredictability. The suicide attackers are extremely motivated either by ideological and/or religious impulses. An *overt attack* **i.e. an attack clearly recognizable by**
law enforcement agencies and other responding agencies is seldom the choice of attackers.

In almost all cases a *covert attack* **strategy where a carefully disguised dispersal means**
with no intentional indication, nor a threat or warning being issued is chosen. Until getting to the specific spot where a suicide attacker will operate the explosives, only a highly trained eye would notice anything suspicious. Thus the absence of sufficient empirical data about suicide attacks presents a peculiar decision making problem characterized by information which is incomplete, approximate and vague.

As counter-terrorism policies and strategies are being developed across the globe, prediction of suicide attacks forms an important contribution towards **this end. Besides the**
incomplete information, predicting suicide attacks is typically subjective, because it uses the value judgments of experts, qualitative variables and variables **of relative quantification. These elements make this effort extremely uncertain and prevent the tools and**
procedures based on traditional methods from being fully applied. The decision making environment typically encountered while attempting some prediction **of suicide attacks involves imprecise data and their solution involves the use of mathematical principles based**
on uncertainty and imprecision. Some of these problems are essentially humanistic and thus subjective in nature, while others are objective, yet they are firmly embedded in an imprecise environment. Linguistic terms are used by counter-terror experts and officials but linguistic terms do not hold exact meaning and may be understood **differently by different people. The boundaries of a given term are rather subjective, and may also depend**
on the situation. Linguistic terms therefore cannot be expressed **by ordinary set theory;** rather, each decision factor is associated with some linguistic term. **The thought process** involved in the act of decision making in a scenario as defined above is a complex array of streaming possibilities in which a person selects or discards information made available from diverse sources. In doing so one is led by a meaningful analysis of available information and optimal selection out of several apparently equi-efficient decisions. Information is available on the basis of some criteria and the criterion-values are not sharply defined but are vague, incomplete and approximate.

Soft sets introduced by D. Molodtsov [9], is state-of-the-art approach to deal with incomplete knowledge within information systems. However, in practical applications of soft sets, the situation is generally more complex and calls for attributes having non-sharp or fuzzy boundaries, as is the case with prediction of suicide attacks. That is why many authors have contributed towards the fuzzification of the notion **of soft set e.g. [1, 3, 4, 5,** 8, 11, 13]. Fuzzy soft sets is an attractive extension of soft sets, enriching the latter with extra features to represent uncertainty and vagueness on top **of incompleteness. It is now** an area of active research in decision making [7] process under vague and approximate conditions.

In this paper we present a fuzzy soft set based decision making model for terror attack prediction for a given collection of cities. The paper is arranged as follows: Section 2 gives a detailed discussion of the factors considered important by human analysts for making prediction of such attacks. After clearly delineating the factors in subsection 2.1 to be considered Section 3.1 first develops a general algorithm using **fuzzy soft sets for the** prediction. This section besides explaining the working of algorithm and making notes about the computer implementation, presents a detailed illustrative **example. Section 4** exhibits the results of simulation results and provides different insights into FSP. Section 5 discusses different aspects of the model; its strengths and weaknesses besides suggesting