FEATURE EXTRACTION AND FEATURE SELECTION: REDUCING DATA COMPLEXITY WITH APACHE SPARK

International Journal of Network Security & Its Applications (IJNSA) Vol.9, No.6, November 2017
DOI: 10.5121/ijnsa.2017.9604 39
FEATURE EXTRACTION AND FEATURE SELECTION:
REDUCING DATA COMPLEXITY WITH APACHE
SPARK
Dimitrios Sisiaridis and Olivier Markowitch
QualSec Research Group, Departement d’Informatique, Université Libre de Bruxelles
ABSTRACT
Feature extraction and feature selection are the first tasks in pre-processing of input logs in order to detect
cyber security threats and attacks while utilizing machine learning. When it comes to the analysis of
heterogeneous data derived from different sources, these tasks are found to be time-consuming and difficult
to be managed efficiently. In this paper, we present an approach for handling feature extraction and
feature selection for security analytics of heterogeneous data derived from different network sensors. The
approach is implemented in Apache Spark, using its python API, named pyspark.
KEYWORDS
Machine learning, feature extraction, feature selection, security analytics, Apache Spark
1. INTRODUCTION
Today, a perimeter-only security model in communication system is insufficient. With the Bring
Your Own Device (BYOD) and IoT, data now move beyond the perimeter. For example, threats
to the intellectual property and generally to sensitive data of an organization, are related either to
insider attacks, outsider targeted attacks, combined forms of internal and external attacks or
attacks, performed over a long period. Adversaries can be either criminal organizations, care- less
employees, compromised employees, leaving employees or state-sponsored cyber espionage [6].
The augmentation of cyber security attacks during the last years emerges the need for automated
traffic log analysis over a long period of time at every level of the enterprise or organizations
information system. Unstructured, semi- structured or structured data in time-series with respect
to security-related events from users, services and the underlying network infrastructure usually
present a high level of large dimensionality and non-stationarity.
There is a plethora of examples in the literature as well as in open-source or commercial threat
detection tools where machine learning algorithms are used to correlate events and to apply
predictive analytics in the cyber security landscape.
Incident correlation refers to the process of comparing different events, often from multiple data
sources in order to identify patterns and relationships enabling identification of events belonging
to one attack or, indicative of broader malicious activity. It allows us to better understand the
nature of an event, to reduce the workload needed to handle incidents, to automate the
classification and forwarding of incidents only relevant to a particular consistency and to allow
analysis to identify and reduce potential false positives.
Predictive Analytics, using pattern analysis, deals with the prediction of future events based on
previously observed historical data, by applying methods such as Machine Learning [9]. For
example, a supervised learning method can build a predictive model from training data to make
predictions about new observations as it is presented in [7].

40
We need to build autonomous systems that could act in response to an attack in an early stage.
Intelligent machines could implement algorithms designed to identify patterns and behaviors
related to cyber threats in real time and provide an instantaneous response with respect to their
reliability, privacy, trust and overall security policy framework.
By utilizing Artificial Intelligence (AI) techniques leveraged by machine learning and data
mining methods, a learning engine enables the consumption of seemingly unrelated disparate
datasets, to discover correlated patterns that result in consistent outcomes with respect to the
access behavior of users, network devices and applications involved in risky abnormal actions,
and thus reducing the amount of security noise and false positives. Machine learning algorithms
can be used to examine, for example, statistical features or domain and IP reputation as it
proposed in [1] and [12].
Along with history- and user-related data, network log data are exploited to identify abnormal
behavior concerning targeted attacks against the underlying network infrastructure as well as
attack forms such as man-in-the-middle and DDoS attacks [3] [9].
Data acquisition and data mining methods, with respect to different types of attacks such as
targeted and indiscriminate attacks, provide a perspective of the threat landscape. It is crucial to
extract and select the right data for our analysis, among the plethora of information produced
daily by the information system of a company, enterprise or an organization [8]. Enhanced log
data are then analyzed for new attack patterns and the outcome, e.g. in the form of behavioral risk
scores and historical baseline profiles of normal behavior is forwarded to update the learning
engine. Any unusual or suspected behavior can then be identified as an anomaly or an outlier in
real or near real-time [11].
We propose an approach to automate the tasks of feature extraction and feature selection using
machine learning methods, as the first stages of a modular approach for the detection and/or
prediction of cybersecurity attacks. For the needs of our experiments we employed the Spark
framework and more specifically its python API, pyspark.
Section 2 explains the difficulties of these tasks especially while working with heterogeneous data
taken from different sources and of different formats. In section 3 we explain the difference
between two common approaches in feature extraction by utilizing machine learning techniques.
Section 4 deals with the task of extracting data from logs of increased data complexity. In section
5 we propose methods for the task of feature selection, while our conclusions are presented in
section 6.
2. EXTRACTING AND SELECTING FEATURES FROM HETEROGENEOUS DATA
In our experiments, we examine the case where we have logs of records derived as the result of an
integration of logs produced by different network tools and sensors (heterogeneous data from
different resources). Each one of them monitors and records a view of the system in the form of
records of different attributes and/or of different structure, implying thus an increased level of
interoperability problems in a multi-level, multi-dimensional feature space; each network
monitoring tool produces its own schema of attributes.
In such cases, it is typical that the number of attributes is not constant across the records, while
the number of complex attributes varies as well [8]. On the other hand, there are attributes, e.g.,
dates, expressed in several formats, or other attributes referred to the same piece of information
by using slightly different attribute names. Most of them are categorical, in a string format while
the inner datatype varies from nested dictionaries, linked lists or arrays of further complex
structure; each one of them may present its own multi-level structure which increases the level of
complexity. In such cases, a clear strategy has to be followed for feature extraction. Therefore, we
have to deal with flattening 1 and interoperability solving processes (Figure 1).

In our experiments, we used as input data an integrated log of recorded events produced by a
number of different network tools, applied on
stage was used a single server of 2xCPUs, 8cores/CPU, 64GB RAM, running an
installation v2.7 with Apache Spark v2.1.0
as a low-cost configuration for handling data exploration when dealing with huge amount of
inputs; raw data volumes, for batch analysis, were approximately
Figure 1. Logs from different input sources
3. GLOBAL FLATTENING V
The first question to be answered is related to the ability to define an optimal way to handle such
complex inputs. Potential solutions may include:
• use the full number of dimensions (i.e. all the available features in each record), defined
as global flattening
• decomposing initial logs into distinct baseline structures derived by each sensor/tool,
defined as local flattening
3.1. LOOKING FOR THREATS
In order to answer to these questions, we should
next steps. While working with the analysis of heterogeneous data taken from different sources,
pre-process procedures, such as
need to be carefully designed in order not to miss any security
tasks are usually time-consuming producing thus significant delays to the overall time of th
analysis.
That is our main motivation in this work: to reduce the time needed for feature extraction in data
exploration analysis by automating the process. In order to achieve it, we
onal Journal of Network Security & Its Applications (IJNSA) Vol.9, No.6, November 2017
we used as input data an integrated log of recorded events produced by a
tools, applied on a telco network. For the pre-processing analysis
stage was used a single server of 2xCPUs, 8cores/CPU, 64GB RAM, running an Apache Hadoo
installation v2.7 with Apache Spark v2.1.0 as a Standalone Cluster Mode, which can be regarded
cost configuration for handling data exploration when dealing with huge amount of
; raw data volumes, for batch analysis, were approximately 16TBytes.
Figure 1. Logs from different input sources
VS. LOCAL FLATTENING
complex inputs. Potential solutions may include:
the full number of dimensions (i.e. all the available features in each record), defined
decomposing initial logs into distinct baseline structures derived by each sensor/tool,
defined as local flattening
HREATS AND ATTACKS IN A KILL CHAIN
In order to answer to these questions, we should also take into account the rationale behind the
edures, such as feature extraction, feature selection and feature transformation,
need to be carefully designed in order not to miss any security-related significant events. These
consuming producing thus significant delays to the overall time of th
exploration analysis by automating the process. In order to achieve it, we utilize the data model
41
we used as input data an integrated log of recorded events produced by a
processing analysis
Apache Hadoop
, which can be regarded
cost configuration for handling data exploration when dealing with huge amount of
the full number of dimensions (i.e. all the available features in each record), defined
decomposing initial logs into distinct baseline structures derived by each sensor/tool,
tionale behind the
tion, feature selection and feature transformation,
nt events. These
consuming producing thus significant delays to the overall time of the data
the data model

42
abstractions and we keep to a minimum any access to the actual data. The key characteristics of
data inputs follow:
• logs derived from different sources
• heterogeneous data
• high-level of complexity
• information is usually hidden in multi-level complex structures
In the next stage, features will be transformed, indexed and scaled to overcome skewness, by
following usually a normal distribution under a common metric space, in the form of vectors. As
in our experiments we processed mainly un-labelled data (i.e. lack of any labels or any indication
of a suspicious threat/attack), clustering techniques will be used to define baseline behavioural
profiles and to detect outliers [1]. The latter may correspond to rare, sparse anomalies, that can be
found by either first-class detection of novelties,n-gram analysis of nested attributes and pattern
analysis using Indicators of Compromise (IoCs) [8] [6]. A survey on unsupervised learning
outlier detection algorithms is presented in [14]. Finally, semi-supervised or/and supervised
analysis can be further employed by using cluster labels, anomalous clusters, or experts feedback
(using active learning methods), in order to detect and/or predict threats and attacks in near- and
real-time analysis [3].
Outliers in time-series are expected to be found for a:
• single network sensor or pen-tester
• a subset of those, or
• by taking into account the complete available set of sensors and network monitoring tools
These time-series are defined in terms of either:
• time spaces as the contextual attributes
• date attributes will be decomposed to time windows such as year, month, day of a
week, hour and minute, following the approach proposed in [13]
• statistics will be calculated either for batch or online mode and then will be stored
in HIVE tables, or in temporary views for ad-hoc temporal real-time analysis.
• a single time space (e.g. a specific day)
• a stable window time space (e.g. all days for a specific month)
• a user-defined variable window time space
Our approach serves as an adaptation of the kill-chain model. The kill chain model [2] is an
intelligence-driven, threat-focused approach to study intrusions from the adversaries’ perspective.
The fundamental element is the indicator which corresponds to any piece of information that can
describe a threat or an attack. Indicators can be either atomic such as IP or email addresses,
computed such as hash values or regular expressions, or behavioral which are collections of
computed and atomic indicators such as statements.
Thus, in our proposal, contextual attributes represent either time-spaces in time-series, as the first
level of interest, single attributes (e.g. a specific network protocol, or a user or any other atomic
indicator), computed attributes (e.g. hash values or regular expressions), or even behavioral
attributes of inner structure (e.g. collections of single and computed attributes in the form of
statements or nested records). Then, outliers can be defined for multiple levels of interest for the
remain behavioral attributes, by looking into single vector values [4], or by looking for the
covariance and pairwise correlation (e.g. Pearson correlation) in a subset of the selected features
or the complete set of features [5].

43
Experiments with data received from different network monitoring tools regarding the system of a
telco enterprise, at the exploratory data stage revealed that the number of single feature attributes
in this log were between a range of 7 (the smallest number of attributes of a distinct feature space)
up to 99 attributes (corresponding to the total number of the overall available feature space). A
fact, that led us to carry on with feature extraction by focusing on flattening multi-nested records
separately for each different structure (under a number of 13 different baseline structures).
Thus, the main keys in the proposed approach for feature extraction are:
• extract the right data
• correlation of the ’right data’ can reveal long-term APTs
• re-usable patterns and trend lines as probabilities are indications of zero-day
attacks
• trend lines may also be used to detect DDoS attacks
• handle interoperability issues
• handle time inconsistencies, date formats, different names for the same piece of
information, by extending the NLP python library [2]
3.2. GLOBAL FLATTENING OF INPUT DATA
By following this approach, we achieve a full-view of entities behavior as each row is represented
by the full set of dimensions. On the other hand, the majority of the columns do not have a value
or it is set to Null. A candidate solution would be to use sparse vectors in the next stage of feature
transformation, which in turn demands special care for NaN and Null values (for example,
replace them either with the mean, the median, or with a special value). Most of the data in this
stage are categorical. We need to convert them into numerical in the next stages, as in Spark,
statistical analytics are available only for data in the form of a Vector or of the DoubleType.
This solution performs efficiently for a rather small number of dimensions while it suffers from
the well-known phenomenon of the curse of dimensionality for a high number of dimensions,
where data appear to be sparse and dissimilar in several ways, which prevents common data
modelling strategies from being efficient.
3.3. LOCAL FLATTENING OF INPUT DATA
By following this approach, we identify all the different schemas in input data. First, it is a
bottom-up analysis by re-sythesing results to answer to either simple of complex questions. In the
same time, we can define hypotheses to the full set of our input data (i.e. top-down analysis) thus,
it is a complete approach in data analytics, by allowing data to tell their story, in a concrete way,
following a minimum number of steps. In this way, we are able to:
• keep the number of assumptions to a minimum  
• look for misconfigurations and data correlations into the abstract dataframes definitions
• keep access to the actual data to a minimum  
• provide solutions in interoperability problems, such as:
• different representations of date attributes
• namespace inconsistencies (e.g. attributes with names such as prot, protocol,
connectionProtocol)  
• cope with complex structures of different number of inner levels  
• deal with event ordering and time-inconsistencies (as it is described in [10])

4. FEATURE EXTRACTION
In Apache Spark, data are organized
relational tables: there are columns (aka attributes or features or dimensions) and rows (i.e. events
recorded, for example, by a network sensor, or
corresponded datatypes define the schema of a dataframe. In each dataframe, its columns and
rows i.e. its schema is unchangeable. Thus, an example of a schema could be the following:
DataFrame[id: string, @timestamp: string, honeypot: string, payloadCommand: string]
A sample of recorded events of this dataframe schema is shown in Figure 2:
Figure 2. A sample of recorded events, having 4 columns/dimensions
The following steps refer to the case in which logs/datasets are ingested i
approach examines the data structures on their top
synthesis of previous and new dataframes, in an automatic way. Access to the actual data is only
taken place when there is a need to find schemas in
the records (thus, even if we have a dataframe of million/billions of events, we only examine the
schema of the first record/event).
data: a data frame column.
1) load the log file in a spark data
2) find and remove all single-valued attributes
section)
3) flatten complex structures
a) find and flatten all columns of complex structure (th
the lowest complex attribute of the hierarchy of complex attributes)
i) e.g. struct, nested dictionaries, linked
their value is of Row
b) remove all the original columns o
4) convert all time-columns into timestamps, using distinct time features in the data
5) integrate similar features in the list of data
Each attribute of a complex structure, such as of a
array, is handled in such way, which
the elements of an array, a dictionary, a list, etc.) will be
way, we manage to transform the schema of the
each one corresponding to a schema that refers to a single network sensor or other input data
source, as it is illustrated in the following figures (Figures 3, 4,
different cases follow:
• struct – RowType
• use the leaf column at the last level of this struct
column
• list: add list elements as new columns
XTRACTION IN APACHE SPARK
organized in the form of dataframes, which resemble the well
recorded, for example, by a network sensor, or a specific device). The list of columns and their
fine the schema of a dataframe. In each dataframe, its columns and
DataFrame[id: string, @timestamp: string, honeypot: string, payloadCommand: string]
corded events of this dataframe schema is shown in Figure 2:
Figure 2. A sample of recorded events, having 4 columns/dimensions
The following steps refer to the case in which logs/datasets are ingested in json
approach examines the data structures on their top-level, focusing on abstract schemas and re
taken place when there is a need to find schemas in dictionaries and only by retrieving just one of
vent). The words field, attribute or column refer to the same piece of
file in a spark data frame, in json format
valued attributes (this step applies also to the feature selectio
find and flatten all columns of complex structure (the steps are run recursively, down to
the lowest complex attribute of the hierarchy of complex attributes)
uct, nested dictionaries, linked lists, arrays, etc. (i.e. currently those which
their value is of Row Type)
remove all the original columns of complex structure
columns into timestamps, using distinct time features in the data
features in the list of data frames
Each attribute of a complex structure, such as of a struct, nested dictionary, linked
, is handled in such way, which ensures that all single attributes of the lowest data level (i.e.
the elements of an array, a dictionary, a list, etc.) will be flattened and expressed in 2
way, we manage to transform the schema of the original dataframe to a number of dataframes,
source, as it is illustrated in the following figures (Figures 3, 4, 5 and 6). The steps, for
use the leaf column at the last level of this struct-column to add it as a new
add list elements as new columns
44
, which resemble the well-known
a specific device). The list of columns and their
fine the schema of a dataframe. In each dataframe, its columns and
json format. Our
level, focusing on abstract schemas and re-
dictionaries and only by retrieving just one of
refer to the same piece of
feature selection
e steps are run recursively, down to
lists, arrays, etc. (i.e. currently those which
columns into timestamps, using distinct time features in the data frames
linked list or an
ensures that all single attributes of the lowest data level (i.e.
flattened and expressed in 2-D.In this
original dataframe to a number of dataframes,
The steps, for these
column to add it as a new

• array: split array’s elements and add the relevant new columns
• dictionary - steps:
• find all inner schemas for attributes of type Dict, as a list
• add the schemaType as an ind
• create a list of dataframes, where each one has its own distinct schema
• flatten all dictionary
the list of dataframes by adding them as new columns
In Figure 3, in the left-hand schema
value is given by the inner-level attribute
the actual date value can be searched in the inner
$oid and $date are extracted in the form of two new columns, named
original attributes _id and timestamp are then de
side, Schema#2. In this way, we achieved to reduce the complexity of the original input schema to
a new one of lower complexity.
Figure 3. Transforming complex fields (i)
In Figure 3, the exploratory analysis has revealed that the
dictionary as a list of multi-nested dictionaries; each one of the latter present a complex structure
with further levels. These different schemas found in payload are presented in Figure 4.
Figure 4
split array’s elements and add the relevant new columns
find all inner schemas for attributes of type Dict, as a list
add the schemaType as an index to the original dataframe
create a list of dataframes, where each one has its own distinct schema
ionary attributes, according to their schemas, in each dataframe of
dataframes by adding them as new columns
hand schemaSchema#1, attributes _id is of the datatype struct
level attribute, $oid. The same stands for the outer attribute
the actual date value can be searched in the inner-level attribute $date. In both cases, attributes
are extracted in the form of two new columns, named _id_ and
original attributes _id and timestamp are then deleted, having thus a new schema on the right
. In this way, we achieved to reduce the complexity of the original input schema to
Figure 3. Transforming complex fields (i) – attributes _id and timestamp are of the datatype
In Figure 3, the exploratory analysis has revealed that the payload attribute represents actually a
nested dictionaries; each one of the latter present a complex structure
e different schemas found in payload are presented in Figure 4.
Figure 4. Different schemas in the payload attribute
45
create a list of dataframes, where each one has its own distinct schema
each dataframe of
struct. The actual
ribute timestamp:
. In both cases, attributes
and dateOut; the
leted, having thus a new schema on the right-
. In this way, we achieved to reduce the complexity of the original input schema to
are of the datatype struct
represents actually a
nested dictionaries; each one of the latter present a complex structure
e different schemas found in payload are presented in Figure 4.

In Figure 5, we illustrate the new
schemas of the payload attribute in Figure 4. By following this approach, data are easier to be
handled: in the next stages, they will be cleaned, transformed from categorical to numerical and
then they will be further analyzed in order to detect anomalies in entities
The dataframe schema in Figure 6
payload attribute (Figure 5) into its inner
an array. By applying consecutive transformations automatically, we manage to extract all inner
attributes, which simplifies the process of correlating data in the next stage. Thus, by looking into
the raw_sig column, we identify inner v
new features derived by the inner levels, as it is depicted e.g. for column
be further split by leading to two new columns (e.g. with values
process is recursive and automated; special care is given how we name the new columns, in order
to follow the different paths of attributes decomposition.
Figure 5. The new-created dataframes which correspond
In Figure 5, we illustrate the new-created dataframes schemas which correspond to the different
rther analyzed in order to detect anomalies in entities behavior.
The dataframe schema in Figure 6 is the second of the dataframes derived by flattening the
attribute (Figure 5) into its inner-level attributes. Here, feature raw_sig is in the form of
column, we identify inner values separated by ‘:’, which further are decomposed into
new features derived by the inner levels, as it is depicted e.g. for column attsCol5; the latter could
be further split by leading to two new columns (e.g. with values 1024 and 0, respectively), as
to follow the different paths of attributes decomposition.
created dataframes which correspond to the different schemas in payload
46
schemas which correspond to the different
flattening the
is in the form of
, which further are decomposed into
; the latter could
, respectively), as this
payload

Figure 6. Transforming complex fields (ii
5. FEATURE SELECTION
The process of feature selection
3.1, our motivation in our approach is to
reduction of the time needed for applying security analytics in un
ultimately to detect anomalies as strong form
such as, to increase accuracy and
need to select the data that are more related to our questions.
significant role in complex event processing, especially when d
sources and different forms.
We present four methods to achieve this goal:
1. Leave-out single-value attributes
2. Namespace correlation
3. Data correlation using the actual values
4. FS in case of having a relative small number of
5.1. LEAVE-OUT SINGLE-VALUE
The first method is quite simple: all single
dataframe. For example, consider the
datatype Boolean takes the value
drop the relevant column, which leads to a new dataframe schema.
. Transforming complex fields (iii) – attributes raw_sig is of the datatype array
The process of feature selection (FS) is crucial for the next analysis steps. As was explained in
3.1, our motivation in our approach is to reduce data complexity in parallel with a significant
reduction of the time needed for applying security analytics in un-labelled data. As we are aiming
ultimately to detect anomalies as strong form of outliers in order to improve quantitative metrics
accuracy and detection rates or to decrease security noise to a minimum
need to select the data that are more related to our questions. Dimensionality reduction can play a
significant role in complex event processing, especially when data are coming from different
We present four methods to achieve this goal:
value attributes
Data correlation using the actual values
FS in case of having a relative small number of categories
ALUE ATTRIBUTES
The first method is quite simple: all single-valued attributes are removed from the original
example, consider the dataframe schema in Figure 7. Attribute normalized
takes the value True for all the events in our integrated log and therefore we
drop the relevant column, which leads to a new dataframe schema.
47
array
is crucial for the next analysis steps. As was explained in
reduce data complexity in parallel with a significant
labelled data. As we are aiming
in order to improve quantitative metrics
detection rates or to decrease security noise to a minimum, we
Dimensionality reduction can play a
ata are coming from different
valued attributes are removed from the original
normalized of
for all the events in our integrated log and therefore we

Figure 7. Attribute normalized
5.2. NAMESPACE CORRELATION
It is quite common when data inputs are coming from different sources to deal with entity
attributes which refer to the same piece of information although their names are slightly different.
For example, attributes proto and
communication channel. Different tools used by experts to monitor network traffic do not follow
a unified namespace scheme. This fact, could lead to misinterpretations, information redundancy
and misconfigurations in data modelling, among other obstacles in data exploration stage; all
these refer mainly to problems in interoperability, as can be seen in Figure 8. By solving such
inconsistencies, we achieve to further reduce data complexity as well as to reduce
for data analysis. In [10] we have presented an approach to
utilizing means derived by the theory of categories.
Figure 8. Attributes proto
5.3. USING PEARSON CORRELATION
As long as data inputs, in the for
into their corresponding numerical values, and before the process of forming the actual
vectors that will be used in clustering, by using data correlation, we are able to achieve a further
reduction of the dimensions that will be used for the actual security
The outcome of applying this technique, using
Attributes highly correlated may be omitted while defining the relevant clusters; the choice of the
particular attribute to be left out is strongly related to the actual research of interest. For example,
normalized is left out as it presents a constant value of True in all records
ORRELATION
and connection protocol refer to the actual protocol used in a
Different tools used by experts to monitor network traffic do not follow
tions in data modelling, among other obstacles in data exploration stage; all
inconsistencies, we achieve to further reduce data complexity as well as to reduce the overa
] we have presented an approach to handle such interoperability issues by
utilizing means derived by the theory of categories.
proto and connection_protocol refer to the same piece of data
ORRELATION TO REDUCE THE NUMBER OF DIMENSIONS
in the form of dataframes, are cleaned, transformed, indexed and scaled
into their corresponding numerical values, and before the process of forming the actual
reduction of the dimensions that will be used for the actual security analytics.
The outcome of applying this technique, using Pearson correlation, is presented in Figure 9.
48
in all records
refer to the actual protocol used in a
Different tools used by experts to monitor network traffic do not follow
tions in data modelling, among other obstacles in data exploration stage; all
the overall time
handle such interoperability issues by
refer to the same piece of data
IMENSIONS
m of dataframes, are cleaned, transformed, indexed and scaled
into their corresponding numerical values, and before the process of forming the actual feature
, is presented in Figure 9.

we are interested to monitor the
patterns of normal behavior.
Figure 9. Applying Pearson correlation to indexed and scaled data for feature selection
5.4. FEATURE SELECTION IN
CATEGORIES
In case where we deal with categorical attributes presenting a relative small number of categories,
i.e. numberOfCategories<= 4, we propose the following steps in order to achieve a further
feature reduction. We distinguish the case
a security-related event) and the case where some or all the labels are available
mention that in real scenarios,
highly-unbalanced data (i.e. where only few instances of the rare/anomalous class are available).
Working with un-labelled data:
• For the set of these features, select each one of them as the feature
either:
• Use a decision tree with a
number of the dimensions (by following one or more of the aforementioned techniques)
• Create 2n
sub-dataframes with respect to the number of categories
• Calculate features importance using a
• Use an ensemble technique in the form of a
classifier, running a combination of the above techniques to optimize results in the next
levels of the analysis (e.g. to further optimize detection rates)
nitor the behavior in local hosts and to detect any anomalies deviate by
N CASE OF HAVING A RELATIVE SMALL N
, we propose the following steps in order to achieve a further
feature reduction. We distinguish the cases where data are un-labelled (lack of any indication f
related event) and the case where some or all the labels are available.
mention that in real scenarios, usually we need to cope with either fully un-labelled data or
nced data (i.e. where only few instances of the rare/anomalous class are available).
For the set of these features, select each one of them as the feature-label attribute and then
Use a decision tree with a multi-class classification evaluator to further reduce the
dataframes with respect to the number of categories
Calculate features importance using a Random Forest classifier
technique in the form of a Combiner e.g. a neural network
, running a combination of the above techniques to optimize results in the next
levels of the analysis (e.g. to further optimize detection rates)
49
in local hosts and to detect any anomalies deviate by
NUMBER OF
, we propose the following steps in order to achieve a further
labelled (lack of any indication for
. We need to
labelled data or
nced data (i.e. where only few instances of the rare/anomalous class are available).
label attribute and then
to further reduce the
l network or a Bayes
, running a combination of the above techniques to optimize results in the next

50
Working with labelled data:
• Select features using the Chi-Square test of independencies. In our experiments, with
respect to the input data, we have used four different statistical methods, available in
Spark ML library:
• The number of top features
• A fraction of the top features
• p-values below a threshold to control the false positive rate
• p-values with false discovery rate below a threshold
6. CONCLUSIONS
We have presented an approach to handle efficiently the tasks of feature extraction and feature
selection while working with security analytics by utilizing machine learning techniques. It is an
automated solution to handle interoperability problems. It is based on a continuous transformation
of the abstract definitions of the data inputs.
In the case of feature extraction, access to the actual data is limited to a minimum read actions of
the first record of a dataframe and only when it is needed to extract the inner schema of a
dictionary-based attribute. In the case of feature selection, the actual data are accessed only to
find correlations between them, before we apply clustering or any other method for threat
detection.
By following the proposed approach, we managed to achieve our primary objectives: reduce
computational time, reduce data complexity and provide solutions to interoperability issues, while
analyzing vast amount of heterogeneous data from different sources.
The approach can be formalized in the next steps by utilizing novel structures derived from the
theory of categories as it has been presented in [10] towards an overall optimization, in terms of
quantitative and qualitative metrics.
REFERENCES
[1] C. C. Aggarwal and C. K. Reddy, Data clustering: algorithms and applications. CRC press, 2013.
[2] S. Bird, E. Klein, and E. Loper, Natural language processing with Python: analyzing text with the
naturallanguage toolkit. ” O’Reilly Media, Inc.”, 2009.
[3] P. Duessel, C. Gehl, U. Flegel, S. Dietrich, and M. Meier, “Detecting zero-day attacks using context-
aware anomaly detection at the application-layer,” International Journal of Information Security, pp.
1– 16, 2016.
[4] N. Goix, “How to evaluate the quality of unsupervised anomaly detection algorithms?” arXiv preprint
arXiv:1607.01152, 2016.
[5] E. M. Hutchins, M. J. Cloppert, and R. M. Amin, “Intelligence-driven computer network defense in-
formed by analysis of adversary campaigns and intrusion kill chains,” Leading Issues in Information
Warfare & Security Research, vol. 1, no. 1, p. 80, 2011.
[6] V. N. Inukollu, S. Arsi, and S. R. Ravuri, “Security issues associated with big data in cloud
computing,” International Journal of Network Security & Its Applications, vol. 6, no. 3, p. 45, 2014.
[7] S. P. Kasiviswanathan, P. Melville, A. Banerjee, and V. Sindhwani, “Emerging topic detection using
dictionary learning,” in Proceedings of the 20th ACM international conference on Information and
knowledge management, pp. 745–754, ACM, 2011.
[8] M. Lange, F. Kuhr, and R. M ̈oller, “Using a deep understanding of network activities for security
event management,” International Journal of Network Security & Its Applications (IJNSA), 2016.

51
[9] M.-L. Shyu, Z. Huang, and H. Luo, “Efficient mining and detection of sequential intrusion patterns
for network intrusion detection systems,” in Machine Learning in Cyber Trust, pp. 133–154, Springer,
2009.
[10] D. Sisiaridis, V. Kuchta, and O. Markowitch, “A categorical approach in handling event-ordering in
distributed systems,” in Parallel and Distributed Systems (ICPADS), 2016 IEEE 22nd International
Conference on, pp. 1145–1150, IEEE, 2016.
[11] D. Sisiaridis, F. Carcillo, and O. Markowitch, “A framework for threat detection in communication
systems,” in Proceedings of the 20th Pan-Hellenic Conference on Informatics, p. 68, ACM, 2016.
[12] M. Talabis, R. McPherson, I. Miyamoto, and J. Martin, Information Security Analytics: Finding Secu-
rity Insights, Patterns, and Anomalies in Big Data. Syngress, 2014.
[13] K. Veeramachaneni, I. Arnaldo, V. Korrapati, C. Bassias, and K. Li, “Aiˆ 2: training a big data
machine to defend,” in Big Data Security on Cloud (BigDataSecurity), IEEE International Conference
on High Performance and Smart Computing (HPSC), and IEEE International Conference on
Intelligent Data and Security (IDS), 2016 IEEE 2nd International Conference on, pp. 49–54, IEEE,
2016.
[14] A. Zimek, E. Schubert, and H.-P. Kriegel, “A survey on unsupervised outlier detection in high-
dimensional numerical data,” Statistical Analysis and Data Mining: The ASA Data Science Journal,
vol. 5, no. 5, pp. 363–387, 2012.
AUTHORS
Dimitrios Sisiaridis received his BSc in Information Engineering by ATEI of
Thessaloniki. He received his MSc in Computing (advanced databases) and PhD in
applied maths and security by Northumbria University in Newcastle. He is a member of
the Qualsec Research Group, in Université Libre de Bruxelles, working as a researcher
in projects related to big data security analytics
Prof. Olivier Markowitch is associated Professor of the Departement d’ Informatique,
in Université Libre de Bruxelles. He is a member of the QualSec Research Group,
working on the design and analysis of security protocols and digital signature schemes

FEATURE EXTRACTION AND FEATURE SELECTION: REDUCING DATA COMPLEXITY WITH APACHE SPARK

Recommended

More Related Content

What's hot (18)

Similar to FEATURE EXTRACTION AND FEATURE SELECTION: REDUCING DATA COMPLEXITY WITH APACHE SPARK (20)

Recently uploaded (20)

FEATURE EXTRACTION AND FEATURE SELECTION: REDUCING DATA COMPLEXITY WITH APACHE SPARK