International Journal of Mechanical Engineering and Technology (IJMET)
Volume 10, Issue 01, January 2019, pp. 1540–1546, Article ID: IJMET_10_01_156
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=1
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication
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USING BIG DATA ANALYTICS TO PREDICT
AND REDUCE CYBER CRIMES
Dr. Fatma Mohamed Abdullah
College of law, Prince Sultan University, Saudi Arabia
Fellowship from Higher Education Academy, UK
Fabdullah@psu.edu.sa
ABSTRACT
The challenges of crime prevention in today's world are increasingly complex and
require new technologies that can handle the huge amount of confidential data
generated through different sources. Cybercrime is an important issue for research as
it affects many mainstream sectors such as defences, social media, government,
industry, private, military and scientific sectors. etc. Internet criminals use distorted
or hacked data to capture their actions. The development of the unpredictability of
digital interventions requires efficiency specialized and judicial examination. The
cybercrime rate is increasing and challenging the investigative personnel. Crimerelated data is increasingly being generated nowadays and is often digital in nature.
At present, data generated cannot be efficiently handled using traditional analysis
techniques. Instead of using traditional data analysis techniques it would be useful to
use big data analyses for this massive data. This paper will examine the analyse of
such data that were generated largely using big data analyses to provide
their analysis groups to the artificial neural network which in turn produces
a crime prediction pattern. So The police department can use the prediction
pattern to allocate its resources in order to reduce the crime rate.
Keywords: Cybercrimes, Big data, Crime Predictive Model, Cybercriminals, attacks,
artificial neural networks
Cite this Article: Dr. Fatma Mohamed Abdullah, Using Big Data Analytics to Predict
and Reduce Cyber Crimes, International Journal of Mechanical Engineering and
Technology 10(1), 2019, pp. 1540–1546.
http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=1
1. INTRODUCTION
In today's digital age, most people are heavily dependent on technology for everyday tasks.
With the rise of technological advances, cyber-attacks also increased. Over the past few years,
there have been numerous security infractions. When the penetration of sensitive data, both
enterprises and consumers are affected.
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Using Big Data Analytics to Predict and Reduce Cyber Crimes
Increasing crime day by day is the main issue facing human society. The crime occurs
when the personal space or the workspace of the criminal and the target intersects at one
point. The target may be one person or group of people or it can be a territory. The crime may
be accidental or planned. The accidental crime is regrettable and unexpected. An unintended
crime occurs in many places. The group of people fights with others because of a small issue
that may harm people who have nothing to do with it. Planned crime is a crime committed
intentionally. The person who intends to commit the offense, primarily research the target or
target area and accordingly study them for implementation. Segregated places have greater
chances of crime, with police patrols less than. [1]
Earlier, data on crime are mostly police complaints, news reports and articles available in
handwritten or printed form, but with technological development, crime data is available in
hard copy as well as electronic version format. The previous scenarios differ with the low
crime rate, and the data generated on criminal activities were also low. That the amount of
less traditional data analysis techniques are effective data to analyse and predict crime.
Previous data on criminal activities play a vital role in mapping crime and predicting where
crime can occur. Analysing those previously available data was a very tedious and timeconsuming task by traditional data extraction techniques although data were much lower. Data
generation nowadays is vast due to the increase in the crime rate, which cannot be addressed
by conventional data analysis techniques. These large generated data are large data that can be
easily processed with Big Data Analytics [2]. Digital data may be organized, semi-structured
or unorganized. The digital data analysed so far has been a systematic type of data to predict
crime. Structured data can be considered as ordered data in tabular format with the help of
appropriate rows and columns. Previous data is useful for predicting volatile places or saying
hot spots. After applying some data extraction techniques such as aggregation, classification
and other techniques, locations where there were more opportunities for crime were
identified, and police capacity could be allocated there. At present, Internet usage is
increasing rapidly. The use of the Internet is also responsible for providing communication
between criminals to complete their targeted mission. Thus, data generation is large and often
consists of semi-structured or unstructured data and can be analysed using large data
collection [3]. To analyse such a huge amount of data either in a semi-structured or
unorganized form, traditional data mining techniques are not capable of doing so. For this
purpose, large data analysis data is used.
Crime mapping is used to analyse, map and visualize crime events or crime patterns to be
an idea of predicting crime. Thus mapping security crime as well as the police will help to
absorb their resources accordingly to prevent crime. Earlier crime maps could be made by a
few people who owned special tools. At present, both scientists and practitioners have the
ability to map the crime using available spatial data and with the help of advanced
technology. Crime mapping is therefore carried out mainly to reduce crime from society by
identifying hotspots places where crime can occur at a higher rate. [4]
2. THEORY OF CRIME PREDICTION
Many researchers tried to explain why crime in areas or are there any style can be concluded
from past events. One of these theories that answering these questions is the theory of crime
prevention [5]. According to the crime did not happen at random, either planned or
opportunism. Provides that any criminal activity occurs when there is an intersection in the
workspace with the goal and the aggressor. People’s place consists of places visited by the
daily routine, like the workplace, educational institutions and shopping centers etc. These are
called selected sites of the offender or the victim, called nodes too. The paths that people take
each day are connected to many nodes that create an area of personal space. This personal
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area is also a person's awareness space. Thus, the crime pattern theory states that a crime
involving two persons can occur only when the personal space intersects at one point or
another. Thus we can say that crimes are not entirely random, but can be studied and analysed
to provide favourable expectations. May not be as accurate as those appearing in the film
Minority but nevertheless some expectations can be expected. [5]
Simply put, if the area provides an opportunity for the offender to commit the crime, and
within the victim's personal awareness, then the crime will occur. Thus, isolated areas that do
not have any proper patrols provide a greater chance of crime. Although places like shopping
and entertainment are places where the culprit and the victim are likely to meet. The reason
for this is that there are a large number of people visiting places there and criminals can easily
place their potential victims. The study of human behaviour is beyond our scope of study, as
we are only interested in finding a pattern that can prevent further crimes. Such as one
example of identifying places where many people are victims of a kidnapping chain or pocket
selection. This is concentrated mainly in certain areas only [1]. Thus, the crime pattern theory
provides an organized way to proceed in predicting the exploration and analysis of crime
patterns. [5]
3. CLASSIFICATIONS AND TYPES OF CYBERCRIME
The concept of cybercrime is very different from conventional crime. Also due to the growth
of Internet technology, this crime has gained serious and unrestricted attention compared to
traditional crime. It is therefore necessary to examine the peculiarities of cybercrime.
Gordon and Ford tried to create a conceptual framework that is the law
Manufacturers can be used when compiling meaningful legal definitions from a technical
and social perspective. Under their scheme, electronic crimes are classified into two types:
1. The first type has the following characteristics:

It is usually an individual event, or separate, from the point of view victim.

This is often facilitated by the introduction of crime programs. Such as keystrokes, viruses,
rootkits, or Trojans on a computer used in the system.

Introductions (but not necessarily) of weaknesses can be facilitated.
At the opposite finish of the spectrum is that the second sort of crime, which Includes,
however, isn't restricted to, activities like cyber stalking and harassment, Extortion and
manipulation of the stock market, corporate spy complex Planning or carrying out terrorist
activities on the Internet. Properties of this Type as follows:

Generally facilitated by programs that do not fit the classification Of crime. For example,
conversations may occur using IM , and clients or files may be transferred using the FTP
protocol.

There are frequent contacts or events from a point of view the user. [6]
As we mentioned earlier that cybercrime is different from Traditional crime. The same as
the traditional cybercrime. Crime also forms of many kinds. Some of the types Cybercrime as
described in Figure bellow as cybercrime Evolve with the invention of a new technique itself.
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Using Big Data Analytics to Predict and Reduce Cyber Crimes
4. REASONS BEHIND CYBERCRIME
There are many reasons why cybercriminals do cybercrime;

For quick money.

To fight an issue that one believes he believes in.

Low marginal cost of online activity due to global access.

Catching by law enforcement agency is less effective and more expensive.

New opportunity to do legal work using technical engineering.

Official investigation and criminal prosecution are rare. There is no concrete regulatory
measure.

Lack of reports and criteria

Difficult to identify

Limited media coverage.

Internet companies are committed collectively and not by individuals [8, 7].
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5. THE CHALLENGES OF CYBERCRIME
An endless discussion there regarding the pros and cons Cyber Crime. There are many
challenges ahead Combating cybercrime. Some of them are here Annotated below:

Lack of trained and qualified manpower Implementation of countermeasures.

There is no policy for a special email account to defend Troops, police and security agency
personnel.

The cyber-attacks did not occur only by terrorists Also from neighbouring countries unlike our
National interests.

Lack of awareness and cybersecurity culture, Both individually and organizationally.

Minimum eligibility to join the police Does not include any knowledge of the computer
sector. They are almost illiterate for cybercrime.

Speed changes Internet technology always beats Provide government. Sector so that they
cannot Identify the origin of these cybercrime [8].

Promotion of research and development in ICT is not Follow the mark

Security forces and law enforcement personnel are Not equipped to handle high-tech crimes.

The current protocols are not self-sufficient, which Establishes investigative responsibility for
crimes This extends internationally.

Budgets for security purposes by the government Especially for law enforcement training,
Security officers and ICT investigators are less Compared to other crimes. [10].
6. BIG DATA MECHANISM IN ANALYTICS CYBERCRIME
The crime analysis tool must be able to accurately and efficiently identify crime patterns for
future prediction and the pattern of micro-crime. The use of hotspot groups was identified as
the basis for the predictive algorithm. Thus, the Hotspot concept helps predict crime.
Depending on the obsolete events, they are used to identify areas where crime incidents are
rising so that appropriate police resources can be deployed at designated locations.
Analysis of crime data can be performed using data extraction techniques using tools such
as the Weka tool, Quick Tool, R Tool, KNIME, ORANGE, Tanagra, q-radar etc. The crime
data analysis is performed using the k-means assembly technique to extract the data. Because
of technological development, criminals use their technological equipment to carry out the
crime. These digital data are used to analyse crime. The crime analysed will be useful in
predicting hot spots. Again, the data used in the analysis and for the purpose of predicting the
use of data mining are structured data when there is unorganized or semi-structured data, and
data mining techniques consume a lot of time at that moment. Obtained criminal data,
preparing that data for a simple quick tool and performing k-clustering on that data for
groups. After obtaining the clusters, analyse these clusters to predict the crime [3]
Other data extraction techniques can also be applied to the analysis of crime data and can
be predicted to identify hotspots. The other technology mainly includes classification, the Kassembly algorithm means the expectation algorithm, etc. After applying the k algorithm, it
may provide improved results and what we get after applying k-means clustering only. The KMeans algorithm can be applied to Big Data Analytics. These are some of the traditional and
time-consuming methods of crime mapping because they require more structured data.[11,12]
The distribution of data related to the crime geographically is also a daunting task but can
now be implemented using tools such as the R tool. With some spatial geospatial packages
that need to be installed and run with the R tool will significantly affect the data to be
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Using Big Data Analytics to Predict and Reduce Cyber Crimes
distributed to geographic regions. These criminal data can be compiled using appropriate
technology. In Big Data Analytics, the GA-based assembly (genetic algorithm) can also be
performed to analyse or perform the assembly. The R tool is used to distribute the data
geographically. This tool is capable of generating geographic representation of geographically
distributed data. Different packages are available with this tool that need to be installed for the
data distribution procedure. Data analysis as well as different types of distributed data can be
obtained from this tool. [13]
Artificial neural network is a group of different nerve cells or nodes (treatment elements)
that give predictions based on available data or data collected. The prediction accuracy of the
artificial neural network is often very high compared to other systems such as Fuzzy Logic
Series or Bayesian Network. The main drawback of the artificial neural network is that it
takes some time to learn the implementation of the artificial neural network. [14]
Finally, we can have summarized the procedures to: the data collected will be distributed
primarily on the geographical location and on the basis of group creation. In the second stage,
groups created using Big Data Analytics are analysed. Finally, clusters are analysed into the
artificial neural network that will produce the prediction pattern. This pattern of predictions
can be used by the security authorities to allocate resources that help to reduce crime.
7. CONCLUSIONS
Through the proposed system, it is presented as to how big data in the field of cybercrimes
recognition can be accommodated Often it says about how to manage things and become easy
when part analysis Become robust while analysing complex data sets and a variety of data. It
usually becomes Compelling improved techniques that can be included to avoid or prevent
cyber-attacks and cybercrime as well. Also, the analysis of the data by the previously
mentioned techniques, there can be many threats without specific problems before eliminating
by the use of Weka tool, Quick Tool, R Tool, KNIME, ORANGE, Tanagra and q-radar
mechanism. Data can also be analysed for Knowledge and Implementation result in various
applications. This process adds an effective approach that can eliminate cybercrime.
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