IRJET- Security Attacks Detection in Cloud using Machine Learning Algorithms

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 02 | Feb 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 223
Security Attacks Detection in Cloud using Machine Learning Algorithms
Dhivya R1, Dharshana R2, Divya V3
1Associate Professor, Dept. of Computer Science and Engineering, KPRIET, Tamilnadu, India
2,3Student, Dept. of computer science and Engineering, KPRIET, Tamilnadu, India
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Abstract - Cloud computing is an evolving technology that provides reliable and scalable on-demand resources and
different services to users with less infrastructure cost. Even though the cloud has many advantages it faces many
drawbacks like vulnerability to attacks, network connectivity dependency, downtime, vendor lock-in, limited control.
From the above-mentioned disadvantages, a security attack is the main drawback in the cloud. There are various security
attacks like Denial-of-service (DOS) attack, Malware injection attack, Side channel attack, Man-in-the-middle attack,
Authentication attack. To detect this attack in the cloud the machine learning algorithm like Support vector machine
(SVM), Naive Bayes, Decision tree, Logistic regression, Ensemble methods can be used. In this paper, we have mainly
focused on various security attacks in the cloud and the machine learning algorithms used for detecting the attacks.
Key Words: Security attacks, Machine learning algorithms, Detection.
1. INTRODUCTION
The cloud is a booming technology in the computer sector. It refers to the accessing of the information technology and the
software applications through the internet connection. The Software as a service (SAAS), Platform as a service (PAAS) and
the Infrastructure as a service (IAAS) all together encapsulate to form the cloud. All the above services are the three types
of services that is been provided by cloud computing. The services are hosted at the data centre by the cloud service
providers for the organization or the individual users to utilize the services through a network connection. The cloud
service providers are the companies that offer different services in the cloud. The major cloud service providers include
AWS, Sales force, Cisco, Apple, Google, IBM (Soft Layer), Oracle, Microsoft (Azure), and SAP, Rack space, and Verizon
(which acquired Terre mark. But the Sales force and the Apple are interested in providing their own application rather
than hosting applications for others. The companies like Google, IBM, Microsoft, SAP provides all the three services of the
cloud while the other companies provide either two or one of the cloud services. One of the disadvantages in cloud
computing is security attacks. This drawback is due to the data storage at different geographical areas in cloud computing.
Fig.1

The above chart describes the various security threats in public clouds as per the cloud security report provide by cloud
security insiders, thus from the chart the misconfiguration of the cloud platform is about 62%, unauthorized access is
about 55%, Insecure interfaces /APIs is about 50%, Hijacking of accounts, services or traffic is about 47%.
In section 2, we discussed different types of attacks on the cloud such as denial of service, malware injection attack, side
channel attack, authentication attack, a man in the middle attack. Section 3, describes various machine learning algorithm
used in security attack to detect like naive Bayes, support vector machine (SVM), K-means clustering, fuzzy logic, decision
tree, and genetic algorithm.
2. ATTACKS ON CLOUD
The cloud encounters many security attacks due to its disadvantages. The various cloud attacks like Denial of service
attack, Malware injection attack, side channel attack, Man in the middle attack and the authentication attack are discussed
below. The attacks may happen at different parts of the cloud like the data storage, during a transaction, during resource
utilization and sharing. The loss of the attack can be lower to higher based on the type of attack. The reason for the attack
in the cloud is due to the huge increase in the use of cloud services.
Fig.2 Attacks on cloud
2.1 Denial of service attack
Denial of service attack the targeted cloud system is overloaded with the service requests from the attacker that stops it
from responding to the upcoming new requests and to its users. According to some of the cloud security alliance, this
cloud is very much vulnerable to this Dos attack. The Denial of service attack can be categorized into the DoS attack and
the DDoS (Distributed denial of service attack). The attack was done using the single system and the single network is
known as the DoS attack. The attack was done using multiple systems and the multiple networks are known as the
Distributed denial of service attack (DDoS). The different types of the DDoS attacks are Volume based attack, protocol
attacks, Application layer attack.
2.2 Malware injection attack
Malware injection attack the attacker injects the victim system with the malicious service or the malicious virtual
machine. Here the attacker creates its own malicious virtual machine or the malicious service module and tries to add it
into the cloud system. Then the attacker must behave so as to make the cloud system believe that it is a valid service. If
the attacker succeeds then the cloud automatically redirects all the requests to this malicious service. Now the attacker
can access the service requests of the victim services.
2.3 Side channel attack
The attacker tries to compromise the cloud system by placing a malicious virtual machine nearby to the target cloud
system then it dispatches the side channel attack. These channels are created in the software implementation of
cryptographic algorithms. Its impact may be greater than any other attacks as they attempt to retrieve secret data
without any special privileged access and in a non-exhaustive manner. There are different categories of side channel
attack like Timing attacks, Cache attacks, Electromagnetic attacks, and Power-monitoring attacks. Electromagnetic

attacks and power – monitoring attacks are mostly applicable to physical devices such as smart cards. The cache attacks
and the Timing attacks are mainly applicable to the cloud computing.
2.4 Authentication attack
The Authentication attack mainly focuses on the authentication part of the cloud services. The primary authentication in
most of the services is the username and the password which is a type of the knowledge-based authentication. The
secondary authentication like shared secret questions, site keys, virtual keyboards is used by secure functioning
organizations like the financial company. Some of the authentication attacks are the Brute Force Attacks, Dictionary
Attack, Shoulder Surfing, Replay Attacks, Phishing Attacks, Key Loggers.
a) Brute force attack: This attack is like a trial and error method; all possible combinations of the password are
applied to break the password.
b) Keyloggers: It is a form of a software program, where it monitors the actions of the user by recording each and
every key pressed by the user.
c) Phishing attack: In this attack, the attacker redirects the user to the fake websites to get the passwords and the
pin codes of the user, it is a kind of the web-based attack.
2.5 Man-in-the-middle-attack
Man-in-the-middle attack the attacker intercepts the message in the public key exchange and retransmits it by
substituting its own public key for the requested one, but the two original are still communicating normally. The sender
does not know that the messages sent by him is received by an attacker and he can access data, modify the message
before retransmitting it to the receiver. Some of the man-in-the-middle attacks are Address Resolution Protocol
Communication (ARP), ARP Cache Poisoning, DNS Spoofing, Session Hijacking.
3. A MACHINE LEARNING ALGORITHM FOR DETECTION
The machine learning algorithm allows software applications to produce accurate predicting outcomes without being
explicitly programmed. The machine learning algorithm can be divided into classification algorithms and clustering
algorithms. Some of the classification algorithms are the Naïve Bayes, support vector machine (SVM), decision tree, logistic
regression, and ensemble methods. In this paper, we are going to use the classification algorithm.
3.1 Naïve Bayes
Naive Bayes depends on the Bayesian technique for playing out the classification process. It is a basic and most straight
forward procedures for building classifiers models that allocate class labels to issue instance, represented as vectors of
highlight values when the class labels are drawn from some limited set. The use of hidden Naive Bayes (HNB) gives exact
outcomes than the traditional naive Bayes model. HNB can be connected to intrusion detection issues that experience the
ill effects of dimensionality exceptionally related highlights and high system information stream volumes. Dos attack is
distinguished utilizing 3 system: Multilayer perceptron (MLP), Naive Bayes and Random forest. MLP demonstrated the
most elevated exactness rate 98.63% when contrasted with different systems. Display utilized naive Bayes classifier with
k2 learning process on decreased NSL KDD dataset for each attack class. In the proposed model each layer is prepared to
dataset a solitary sort of attack. The result of one layer is passed on to another layer to build the identification rate. It
distinguishes attack that happens in an unverifiable circumstance.
3.2 Support Vector Machine (SVM)
SVM is used in classification and regression. classification can be viewed as the task of separating classes in feature space.
It became famous when using the image as input, it gave good accuracy. Currently, SVM used in object detection and
recognition, content-based image retrieval, text recognition, biometrics, speech recognition etc. Svm is a practical learning
method based on statistical learning theory. Construct a hyperplane in the decision surface in such a way that the margin
of separation between positive and negative. The goal of SVM is to find the particular hyperplane of which the margin is
maximized. The particular data point for which the first or second line of the equation is satisfied with the equality sign is
called a support vector.

3.3 Decision Tree
The decision tree algorithm is a kind of the classification-based machine learning algorithm. A decision tree is a flow-chart-
like hierarchical tree structure which is composed of three basic elements: decision nodes corresponding to attributes,
edges or branches which correspond to the different possible attribute values. The third component is leaves including
objects that typically belong to the same class or that are very similar. Tree induction algorithms like Id3 and C4,5 create
decision trees, it takes only one attribute at a time. The decision tree nodes are created by choosing an attribute from the
feature space of the dataset that brings maximum information gain by splitting the data on its distinct value. After the split,
the information gain is calculated as the difference between the entropy of the initial dataset and the sum of the entropies
of each of the subsets.
3.4 Logistic Regression
The logistic regression is the commonly used tool for discrete data analysis. It uses an equation as the representation.
Logistic regression is used for predicting the probabilities of the various classes does an analysis and give a group of
independent variables. It makes use of a linear equation with independent predictors for predicting a value. The predicted
value can be anywhere from negative infinity to positive infinity of the system. We can squash the output of the linear
equation into a range of [0,1]. For squashing the predicted value from 0 to 1, we make use of the sigmoid function. It
provides a solution for the classification problem that assumes that a linear combination of the observed features can be
used to determine the probability of each particular outcome of the dependent variable.
3.5 Ensemble Method
Ensemble methods is a learning algorithm that constructs a group of classifiers and then by using the weighted vote of
their predictions we classify new data points. The original ensemble method is Bayesian averaging but recent algorithms
include error-correcting output coding Bagging and boosting. The various types of ensemble methods are Bootstrap
AGGregating, Random Forest Models.
1. Bootstrap AGGregating
BAGGing name is given because it combines Bootstrapping and Aggregation to form one ensemble model. When a sample
of data is given, many bootstrapped subsamples are taken from the sample. In each bootstrapped samples a decision tree
is formed. After decision tree subsamples are formed, an algorithm is used to aggregate over the Decision Trees to form
the most efficient predictor.
2. Random Forest Models
Random Forest models will implement differentiation levels because based on different features each tree is splitted. This
differentiation levels provides a greater ensemble to aggregate over, ergo producing a more accurate predictor.
4. THE VARIOUS SECURITY ATTACKS DETECTION IN CLOUD BY OTHER AUTHORS ARE STUDIED BELOW
Paper Title Algorithm Security Advantages Limitations
used attack
DDoS Attack C4.5 Denial of • The article discusses about the The C4.5 algorithm
Detection using algorithm service objective of the Denial-of service alone cannot detect the
Machine and decision attack attack and had proposed an DDoS DDoS attack, it must be
Learning tree model using the C.4.5 algorithm to coupled with the
Techniques in mitigate the DDoS threat. Signature detection
Cloud • In this the algorithm is coupled technique.
Computing with the signature detection
Environments techniques that generates the

decision tree for detecting the DDoS
attacks.
• It also discusses about the three
methodologies of the Intrusion
detection and demonstrates about
the C.4.5 model.
An Efficient FireCol Distributed • Proposed about the detection and In this paper the
Detection and algorithm denial of prevention of DDoS attacks in cloud existing accuracy is
Prevention of service environment. better than the
DDoS Attacks in • The article says that internet is most proposed accuracy.
Cloud popular technology and cloud
Environment computing is an internet-based
computing.
• The DDoS attacks are increasing in
the cloud computing due to the
essential characteristics of the
cloud. It, Address the problem of
DDoS attacks and present the
theoretical foundation, architecture,
and algorithms of FireCol.
• It discusses about detection and
prevention of the attack using
FireCol algorithm.

Prevent DDOS Distributed Artificial • Says about the cloud computing and Here it provides
Attack in Cloud denial of neural its one of its security attacks that is methods for the
Using Machine service network the DDoS. detection, it mainly
Learning attack • The attack is simple but it is very focuses on the
much powerful attack that makes detection part.
resources unavailable to legitimate
users.
• It discusses about the prevention of
DDoS attack in cloud using machine
learning.
• It also discusses about various
machine learning algorithms.
• It mainly focuses on the artificial
neural network.
A Fuzzy Logic Distributed Fuzzy Logic • The article discusses about DDoS In this paper it uses the
based Defence denial of attacks, Types of DDoS attacks, trained data-based
Mechanism service Motivation behind the DDoS attack, defence system but if
against attack DDoS attack generation tools. any new type of attack
Distributed • It says about the Defence happens it is difficult.
Denial of Service mechanism against distributed
Attack in Cloud denial of service attack in cloud
Computing computing and fuzzy based defence
Environment mechanism against distributed
denial of service
SYN Flood SYN flood Support • Proposed an automated In this paper single
Attack Detection attack Vector classification system for DoS attack attack is detected using
in Cloud Machine detection in cloud computing. a single algorithm, it
Computing using (SVM) • They collected the network data cannot detect any other
Support Vector using the Wireshark and extracted attacks.
Machine the necessary features using the
Tshark. Used the Support Vector
Machine for the data classification
and provided with an 100%
accuracy.
An SVM-based Denial of Support • The article provides a svm based Here they use a filter
framework for service vector framework for Dos attack detection that removes the noisy
detecting DoS attack machine and in virtualized cloud under the data which decreases
attacks in decision tree changing environment. the accuracy of the
virtualized • They use a filter to remove the noisy detection.

clouds under data in the pre-processing step.
changing • Their results show them that the
environment proposed framework is better than
the traditional support vector
machine and the decision tree.
5. CONCLUSIONS AND FUTURE WORK
The Security attack detection is a very difficult problem in cloud computing. Different machine learning algorithms can be
used to detect the attack but the Naïve Bayes, support vector machine (SVM), decision tree, logistic regression, and
ensemble methods. Machine learning algorithms provide with the efficient output. Our future work is to enhance the
system by preventing the DoS attack using the machine learning algorithm.
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