A new approach for formal behavioral

International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.4, No.3, May 2014
DOI:10.5121/ijfcst.2014.4307 77
A NEW APPROACH FOR FORMAL BEHAVIORAL
MODELING OF PROTECTION SERVICES IN
ANTIVIRUS SYSTEMS
Monire Norouzi1
, Saeed Parsa2*
and Ali Mahjur3
1
Department of Computer Engineering, Shabestar Branch, Islamic Azad University,
Shabestar, Iran
2*
Department of Computer Engineering, Iran University of Science and
Technology(IUST), Tehran, Iran
3
Malek Ashtar University of Technology, Tehran, Iran
ABSTRACT
Formal method techniques provides a suitable platform for the software development in software systems.
Formal methods and formal verification is necessary to prove the correctness and improve performance of
software systems in various levels of design and implementation, too. Security Discussion is an important
issue in computer systems. Since the antivirus applications have very important role in computer systems
security, verifying these applications is very essential and necessary. In this paper, we present four new
approaches for antivirus system behavior and a behavioral model of protection services in the antivirus
system is proposed. We divided the behavioral model in to preventive behavior and control behavior and
then we formal these behaviors. Finally by using some definitions we explain the way these behaviors are
mapped on each other by using our new approaches.
KEYWORDS
Antivirus system, protection services, behavioral modeling, formal method;
1. INTRODUCTION
Today, antivirus software is very important in business and software development, because any
computer the system should install a security application on itself to maintain regularize and
security of its data [1]. In the recent years, many attacks are occurred to home the systems, bank
servers and military systems by Viruses and Malwares [2]. Information maintenance and prevent
to data accessibility is the cause of attacking and destroying data which has been occurred by
invasive malware widely and suddenly [3]. So, users need to powerful security applications which
secure their systems against the attack of viruses and malwares [4].
The attacks in the system security section is divided into two parts [5]. The first set of attacks are
enabled to eliminate security files or security applications of a computer the system. After
disabling these files and applications, these attacks access to important data in the computer
system. The second set of attacks can remove a set of special files and destroying important files.
These attacks are executed and handled from intelligence agencies and hacker on computer
systems of the users. These attacks have not needed to destroy the system security of computer.
But, these attacks are executed and run by hiding themselves in URL web addresses or in created
files using routing software such as Microsoft office, adobe reader, win zip, etc. By notice to
these attacks, a computer system need to an online security system. Of course, an offline security

78
system prevent to data accessibility in computer system slightly. Unfortunately, by progression of
technology and software system complexity attackers have discovered some new vulnerable areas
in the systems that informally called "holes". So, by this ways they disturb the system security.
By the above reasons, we find that testing and verifying the security applications such as the
antivirus systems is very important and essential in security discussion of computer systems [6].
There are some antivirus applications in software development market such as Bitdefender,
Kaspersky, Avira and etc. that each application try to compete with each other by presenting more
services and easy updating. Of course, computer viruses [7], Spywares [8], Trojans, Worms [9]
and other new malwares debut every day.
In this paper, a protection services in Antivirus System approach has been presented. In
particular, we separate protection services in Antivirus System into two types: preventive
behavior and control behavior based on behavioral modeling methods of Hansen, Virtanen et al.
(2003). The interactions between these two behaviors are modeled as characteristics process. The
process role is to keep the couple behaviors synchronized. By analyzing logical problems and
checking behavior specifications, it is possible to verify the protection services in Antivirus
System approach. In particular, the contributions of this paper are:
 Proposing an ideal Antivirus System based on Avira Antivirus approach.
 Presenting an Antivirus System behavior model to couple preventive and control
behaviors of Antivirus System approach.
 Facilitating the mapping process between these two behaviors by means of the formal
verification approach based on Binary Decision Diagram (BDD) (Clarke, Grumberg et al.
1999).
The rest of this paper is organized as follows. Section 2 reviews the related works and correlated
studies in formal verification and Antivirus System approach. Section 3 describes theformalizing
preventive and control behaviors of the Antivirus System as well as defining some essential
concepts and notations to formalize these behaviors. Finally, conclusions and future works are
provided in Section 4.
2. RELATED WORKS
Defining an anti-virus model is used to designing antivirus applications in software development,
so it is not a new topic. But, formalizing and verifying an antivirus model by using formal
verification methods is a new idea in software development. Some researchers used to formal
verification methods for analysis and verification of software systems. For example:
C. Livadas and et al investigate how formal techniques can be used for the verifying hybrid
systems. Then by presenting the hybrid I/O automaton model they applied to the specification and
verification of hybrid systems [10].
Qianchuan Zhao and et al in their research show how CTL specifications for a statechart can be
verified using a finite-state model checker. In this paper, authors use to kripke structure that
provides the formal relationship between the proposed model and the statechart structure and
CTL formulas for showing formal semantics in CTL specification [11].
A new model has been presented for verifying a website by using LTL logic and Formal method
techniques. The authors model web pages of a website as states and convert HTTP protocols of
the website to transition between states. This paper shows useful relationships between a
proposed model and kripke structure. Of course, all of these papers have same procedure for
formalizing their models and creating kripke structures. But in our paper, we describe how

79
expected specifications of system verify by using defined kripke structure and proposed relations
between proposed model and CTL logic [12].
J.A. Morales et al present a formal model of virus transformation that enables variation
traceability by using four antivirus solutions for handheld devices. Also, they test their formal
model for antivirus software that some viruses attack to this software. In this paper, formalizing
antivirus model use to testing some solutions against virus attacks [13].
Andronick, J. et al considered a new approach to verification of a smart card embedded operating
system. They proved a C source program against supplementary annotations and generated a
high-level formal model of the annotated C program that was used to verify certain global
security properties. This paper is focused on modeling smart card security in Embedded Source
Codes [14].
Heitmeyer, C.L., et al presented verifying a system’s high-level security properties. Their
approach is focused on computer security by using antivirus systems rather than security
properties of software systems Heitmeyer, et al. [15].
Yeung, W.L. presented a formal verification approach to describe the behavioral problems such
as deadlocks and live locks in multi-agent systems. They proposed a model of the contract
negotiation process in multi-agent systems. The proposed model translated to XML Metadata
Interchange (XMI) documents and converted to Communicating Sequential Processes (CSP)
scripts by using ArgoUML tool. Also the Failures-Divergences Refinement (FDR) tool have been
used to implement the model [16].
Yeung, W.L. described a formal and visual modeling approach for choreography-based web
services composition and conformance verification. Apart from Web Services Choreography
Description Language (WS-CDL) and Web Services Business Process Execution Language (WS-
BPEL), their approach also supported the use of visual modeling notations such as UML in
modeling choreographies and orchestrations. The architecture of proposed web services is
presented based on Simple Object Access Protocol (SOAP) and Web Services Description
Language (WSDL). Then a choreography model of the contract negotiation process is presented
by using Unified Modeling Language (UML) tool. Also they translated proposed model in UML
to CSP by using XMI transformation. After translating, the mathematical approaches executed on
translated model and the model is verified by using FDR model checker. Finally, integration of
semantic web service technologies into the framework is also being considered [17].
As another research in this scope Bentahar, J., et al modeled the composite web services based on
a division of interests between operational and control behaviors. Some favorable properties such
as deadlock freedom, safety and reachability has been analyzed. The proposed behaviors have
been converted to Kripke structure by using model checking techniques based on BDD. Also the
Kripke structure models have been translated to SMV code by means of Java converter tool. Then
the models were verified by using NuSMV model checker [18].
Souri et al proposed an adapted resource discovery approach to address multi-attribute queries in
grid computing. They presented a behavioral model for their proposed approach that separate into
data gathering, discovery and control behaviors. So, they used to kripke structure for modeling
these behaviors and verify their behavioral models by using NuSMV model checker[19].
Shukla, J.B., et al proposed a nonlinear mathematical model and analyzed to study the role of
antivirus program to clean an infected computer network. Their model has been proposed by
employing the concept of epidemics, where nodes in the network are considered as populations.

80
The total numbers of nodes have been separated into vulnerable nodes, infected nodes, and
protected nodes. The variable representing the number of antivirus programs considered in the
model has been assumed to be proportional to the number of infected nodes. Their model has
simulated by MAPLE and MATLAB software and there is no any scientific reason for modeling
spread of viruses and infected files in computer networks [20].
3. FORMALBEHAVIORAL MODELLING
In this paper, a protection services in Antivirus System approaches has been presented. In
particular, we separate protection services in Antivirus System into two types: preventive
behaviorand control behaviorbased on behavioral modeling methods of Hansen, et al. [21]. The
interactions between these two behaviors are modeled as mapping process. The process role is to
keep couplebehaviors synchronized. In particular, the contributions of this paper are:
 Proposing an ideal Antivirus System based on Avira Antivirus approaches.
 Presenting an Antivirus System behavior model to preventive and control.
 Presenting a mapping process between the preventive and control behaviors.
 Formalizing preventive and control behaviors.
Figure 1. Preventive behavior of the antivirus system
Figure 1 describes preventive behavior of AVIRA antivirus software in a simple statechart. The
system utilizes security rules of the antivirus system to establishing four specific approaches that
include Protection, Detection, Identification and Removal. The preventive behavior starts
protection approach by initial state system protection and specifies system mode by checking
online or offline mode. The offline mode shows PC Protection and includes protection approach
for two states Real-time Protection and System Scanner. In the state Real-time Protection, the

81
system executes protection operations automatically in the windows system directory (WSD)
section because the important systematic files hold this path:" C:windowssystem32".
The state detecting file as a type of Detection approach discover any type of infection in
suspicious file and send it to state check cleaning operation as a type Identification approach. If
the infected file deletes then the Removal approach is executed and system receives safe result in
this process. If suspicious file ignore for any reason then system receive unsafe result in this
process. The next section of offline mode is manual scanning that the state system scanner
executes protection process on local drives, removable drives and local hard discs by state
detecting file as a type of Detection approach.
The recycling operation divides to two parts. In the first part, by finding infection position the
system performs Identification approach and removes the infection from file so the file is safe and
Removal approach is executed. In the second part, a suspicious file is found and state check
cleaning operations executes Identification approach. If this file is deleted then Removal approach
receives safe result otherwise the system receives unsafe result.
Figure 2. Control behavior of the antivirus system
The control behavior navigates the execution flow of approaches of antivirus system. In the figure
2, the control behavior of the antivirus system presents a number of states extracted from
preventive behavior by using the Buchi automata. These states include Not Activated, Activated,
Process, and Recognition, Done and Aborted.First, by activating antivirus system the control
behavior will be enabled and the all of protections like system protection, internet protection, web
protection, PC protection and real-time protection are enabled. URL detecting and file detecting
will be checked and by finding suspicious files or suspicious URL or infected file they will be
removed or ignored in Recognition state. The state Done occurs if the action in Recognition state
deliver safe state by removing the infected or suspicious file and the state Aborted occurs when
the action in Recognition state is ignored the infected or suspicious file.
Definition 1 (path in antivirus system behaviors): A path pi→j in an antivirus system behaviors is a
finite sequence of the states and transitions starting from state si and finishing at state sj, denoted
as:
pi→j = si→ si+1→ si+2 …sj-1→ sj such that ∀ m ∈ {i, j-1}: (sm
, lm
, sm+1
) ∈T.
For example, in figure 2, Not Activated→Activate→Process→Recognition → Aborted→End is a
path in the control behavior of the antivirus system.
This section present an antivirus behavior model using formal and model checking techniques,
which guides antivirus approaches for any Coordination scenario and separates its behavior into
preventive behavior and control behavior. We use statechart semantics to modeling preventive
li li+1 lj-1
l1 l2 l3 l4 l5

82
and control behaviors. Of course other formalism approaches can be used such as Petri nets
techniques [Robert G. Pettit, Xitong Li].
Now, we illustrate a formal description of the antivirus behavioral model. The preventive
behaviordemonstrates the security rules that implement specific approaches for functioning of an
antivirus system. Also the control behavior navigates the execution flow of security rules of an
antivirus system. We define these behaviors as follow:
Definition 2 (antivirus system behavior): The antivirus system behavioris a 4-tuple AB= (S, s0, L,
R) where:
- S is a finite set of states.
- s0 S is the initial state.
- L is a set of transition labels.
- T  S  L  S is the transition relation. For showing a transition relation we have t = (si, l, sj)
that t  T, si and sj S and l  L. This transition relation describe existing a relation between si
and sj by label l.
The preventive and control behaviors of an antivirus system have retrieved from the antivirus
system behavior.
Definition 3(preventive behavior): The preventive behavioris a 4-tuple PrB = (Spr, spr, Epr, Apr)
where:
- Spr is a finite set of preventive behaviorstates.
- spr Spr is the initial state.
- Apr is a specific approaches of the antivirus behavior.
- Epr is a finite set of preventive behavior events.
- The approach Apr is demonstrated by spr→ s'pr: apr
Definition 4 (control behavior): The control behavioris a 4-tuple CoB = (Sco, sco, Eco, Aco) where:
- Sco is a finite set of control behaviorstates.
- sco Sco is the initial state.
- Eco is a finite set of control behaviorevents.
- Aco is a specific approaches of the antivirus behavior.
- The approach Aco is denoted by sco → s'co: aco.
In statecharts, a specific approach is depicted by s[e]/a. State s  S presents a specific approach a
 A in each behaviorby the event e  E.
The specific approaches on preventive and control behaviors are the same. The only deference
between these approaches is connection between sates and transitions of each behavior.
Definition 5 (mapping process): Let PCO be the set of paths by starting each state in the control
behaviorof an antivirus system. The mapping process is defined by using the following function
(1):
MP: Sco→ 2 ∨
(1)
e
e

83
Figure 3. The example of mapped behavioral modeling in the antivirus system
The mapping function MP associates each state in the control behavior with a set of possible
paths in the preventive behavior. In figure 3, we see a mapping method in the antivirus system
approach where Not Activated and Activated states in the control behavior is associated with
some states in the preventive behavior which are these: system protection → PC protection →
real-time protection.
In figure 2, the states done and aborted are final state of control behavior (as a leaf node in the
tree). For example, in figure 2 a path tree is: not activated → activated → process → recognition
→ done. In this path states have been terminated to done state at the end. This true path is a
performance of the control behavior. There is a loop for states process → recognition → process
→ recognition → process → recognition → process …., which has not created a performance
process of the control behavior.
In the antivirus system behavior we define four approaches. These approaches are Protection
Approach, Detection Approach, Identification Approach and Removal Approach. Connections
between preventive and control behaviors is possible by these approaches. Each of these approach
are mapped on the states of preventive behavior and control behavior.
As we show in figure 3, the Protection Approach includes Not Activated and Activated states in
control behavior and System Protection, PC Protection and Real-Time Protection in preventive
behavior. Detection Approach includes Process state in control behavior and Detecting Files state
in preventive behavior. Identification Approach includes Recognition state in control behavior
and Check Cleaning Operations in preventive behavior and finally the removal Approach
includes Done state in control behavior and Deliver Safe Status state in preventive behavior.
3. CONCLUSIONS AND FUTURE WORKS
In this paper, we present a behavioral model for the protection services in antivirus system which
has two behavioral models, preventive behavior and control behavior. Then we tried to formal
these models, and finally by explaining some definitions we illustrate the way these two
behavioral models connected together. In future we try to verify these behavioral models by using
NuSMV model checkers and Kripke structure techniques.

84
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Authors
Monire Norouzi received her B.Sc. in Computer Engineering at University College of Nabi Akram,
Tabriz, Iran in 2011. Currently, she receives M.Sc. in Software Engineering from Shabestar Branch,
Islamic Azad University in Iran. Her main research interests are Software Analysis, Wireless Network and
Sensor Network, verification and validation of Software Systems.

85
Saeed Parsa received the B.S. degree in mathematics and computer Science from Sharif University of
Technology in Iran, and the M.S. and Ph.D. degrees in computer science from the University of Salford at
England. He is an associate professor of computer science at Iran University of Science and Technology
(IUST). His research interests include software engineering, soft Computing and algorithms.
Ali Mahjur received his B.S. and M.S. degrees in computer engineering from Sharif University of
Technology (SUT), Iran, in 1996 and 1998, respectively. He has been a Ph.D. student in computer
engineering at SUT since then. His research interests include Computer Architecture, Operating Systems,
Memory Management Systems, and Programming Languages.

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