# (Ijcsis) International Journal Of Computer Science And Information Security, Vol. 7, No. 1, 2010 Modeling Of Human Criminal Behavior Using Probabilistic Networks

Ramesh Kumar Gopala Pillai Research Scholar R.V. Center for Cognitive Technologies Bangalore, India 
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Abstract— *Currently, criminal's profile (CP) is obtained from* investigator's or forensic psychologist's interpretation, linking crime scene characteristics and an offender's behavior to his or her characteristics and psychological profile. This paper seeks an efficient and systematic discovery of non-obvious and valuable patterns between variables from a large database of solved cases via a probabilistic network (PN) modeling approach. The PN structure can be used to extract behavioral patterns and to gain insight into what factors influence these behaviors. Thus, when a new case is being investigated and the profile variables are unknown because the offender has yet to be identified, the observed crime scene variables are used to infer the unknown variables based on their connections in the structure and the corresponding numerical (probabilistic) weights. The objective is to produce a more systematic and empirical approach to profiling, and to use the resulting PN model as a decision tool. Keywords-component; Modeling, criminal profiling, criminal behavior, probabilistic network, Bayes Rule 

## I. Introduction

Modeling human criminal behavior is challenging due to many variables involved and the high degree of uncertainty surrounding a criminal act and the corresponding investigation. Probabilistic graphs are suitable modeling techniques because they are inherently distributed and stochastic. In this paper, the system variables comprising the PN are offender behaviors and crime scene evidence, which are initialized by experts through their professional experience or expert knowledge. The mathematical relationships naturally embedded in a set of crimes [3, 4, 8] are learned through training from a database containing solved criminal cases. The PN model is to be applied when only the crime scene evidence is known to obtain a useable offender profile to aid law enforcement in the investigations. A criminal profile is predicted with a certain quantitative confidence. 

The PN approach presented here seeks to build on the ideas of behavior correlations in order to obtain a usable criminal profile when only crime scene evidence is known from the investigation. 

 Dr. Ramakanth Kumar .P 
Professor R.V. Center for Cognitive Technologies Bangalore, India 
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This paper proposes a systematic approach for deriving a multidisciplinary behavioral model of criminal behavior. The proposed offender behavioral model is a mathematical representation of a system comprised of an offender's actions and decisions at a crime scene and the offender's personal characteristics. The influence of the offender traits and characteristics on the resulting crime scene behaviors is captured by a probabilistic graph or PN that maps cause-and-effect relationships between events, and lends itself to inductive logic for reasoning under uncertainty [1]. The use of PNs for CP may allow investigators to take into consideration various aspects of the crime and discover behavioral patterns that might otherwise remain hidden in the data. The various aspects of a crime include a victimology assessment (victim's characteristics, e.g., background characteristics, age, gender, and education), 
crime scene analysis (evidence from the crime scene, e.g., 
time and place where the crime occurred), and a medical report (autopsy report, e.g., type of non-deadly and deadly lesions and signs of self defense). The PN approach to criminal profiling is demonstrated by learning from a series of crime scene and offender behaviors. 

The learning techniques employed in this modeling research are evaluated on a set of validation cases not used for training by defining a prediction accuracy based on the most likely value of the output variables (offender profile) and its corresponding confidence level. 

## Ii. Approach

To start with, a graphical model of offender behavior is learned from a database of solved cases. The resulting CP 
model obtained through training is then tested by comparing its predictions to the actual offenders' profiles. 

Let the database sample space = D, 
Let D consist of 'd' solved cases {C1, ...,Cd}, where Ci is an instantiation of X, which is randomly partitioned into two independent datasets such as a training set 