Social Engineering Optimization (SEO)

Jan , 2016
www.iiec2016.com
Mostafa Hajiaghaei-Keshteli
Amir Mohammad Fathollahi Fard
Social Engineering Optimization
(SEO)
University of Science & Technology of Mazandaran, Behshahr, Iran
February 2017

Content
 Meta-heuristics
 Social Engineering
 Social Engineering Optimization (SEO)
 Benchmark functions
 Engineering problems
 Conclusion
 Questions
2
Content
A. M. Fathollahi Fard Social Engineering Optimization (SEO)

Evolutionary Algorithms
 Optimization techniques can be divided into
two groups, mathematical programming and
Meta-heuristic algorithms (Gandomi, 2014).
 In general, the existing Meta-heuristic
algorithms are divided into two main
categories as follows:
1) Single-solution
2) Population-based (Zheng, 2015).
3
Meta-heuristics

4
Meta-heuristics
Metaheuristics
Population-based
metaheuristics
Single-solution
metaheuristics
Scatter Search (SS)
Genetic Algorithm (GA)
Red Deer Algorithm (RDA)
Ant Colony Optimization (ACO)
Particle Swarm Optimization (PSO)

5
Meta-heuristics
Metaheuristics
Population-based
metaheuristics
Single-solution
metaheuristics
Tabu search (TS)
Variable neighborhood search (VNS)
Social Engineering Optimization (SEO)
Simulated annealing (SA)

‌‫‌های‬‫م‬‫یت‬‫ر‬‫الگو‬‌‫ی‬‫فراابتکار‬
No. Year Algorithm
1 1975 Holland introduced the Genetic Algorithm (GA).
2 1977 Glover proposed Scatter Search (SS).
3 1980 Smith elucidated genetic programming.
4 1983 Kirkpatrick et al. proposed Simulated Annealing (SA).
5 1986 Glover and McMillan offeredTabuSearch(TS).
6 1986 Farmer et al. suggested the Artificial immune system (AIS).
7 1988 Koza registered his first patent on genetic programming.
8 1989 Moscato presented Memetic Algorithm.
9 1992 Dorigo proposed the Ant Colony Algorithm (ACO).
10 1993 Fonseca and Fleming provided Multi-Objective GA (MOGA).
11 1994 Battiti and Tecchiolli introduced Reactive Search Optimization (RSO).
12 1995 Kennedy and Eberhart proposed Particle Swarm Optimization (PSO).
13 1997 Storn and Price suggested Differential Evolution (DE).
14 1997 Rubinstein presented the Cross Entropy Method (CEM).
15 2001 Geemetal.providedHarmonySearch(HS).
16 2001 Hanseth and Aanestad offered Bootstrap Algorithm (BA).
17 2004 Nakrani and Tovey presented Bees Optimization (BO).
18 2005 Krishnanand and Ghose introduced Glowworm Swarm Optimization(GSO).
19 2005 Karaboga proposed Artificial Bee Colony Algorithm (ABC).
20 2006 Haddad et al. suggested Honey-bee Mating Optimization (HMO).
21 2007 Hamed Shah-Hosseini offered Intelligent Water Drops (IWD).
Meta-heuristics
6

22 2007 Atashpaz-Gargari and Lucas introduced Imperialist CompetitiveAlgorithm (ICA).
23 2008 Yang presented Firefly Algorithm (FA).
24 2008 Mucherino and Seref suggested Monkey Search (MS).
25 2009 Husseinzadeh- Kashan provided League Championship Algorithm (LCA).
26 2009 Rashedi et al. introduced Gravitational Search Algorithm (GSA).
27 2009 Yang and Deb offered Cuckoo Search (CS).
28 2010 Yang developed Bat Algorithm (BA).
29 2011 Shah-Hosseini introduced the Galaxy-based Search Algorithm (GbSA).
30 2011 Tamura and Yasuda designed Spiral Optimization (SO).
31 2011 Rajabioun developed Cuckoo Optimization Algorithm (COA)
32 2011 Rao et al. presented Teaching-Learning-Based Optimization (TLBO) algorithm.
33 2012 Gandomi and Alavi proposed the Krill Herd (KH) Algorithm.
34 2012 Çivicioglu introduced Differential Search Algorithm (DSA).
35 2012 Hajiaghaei-Keshteli and Aminnayeri proposed the Keshtel Algorithm (KA).
36 2013 Husseinzadeh- Kashan provided Optics Inspired Optimization(OIO).
37 2013 Husseinzadeh- Kashan proposed Grouping Evolution Strategies (GES).
38 2014 Gandomi presented Interior Search Algorithm (ISA).
39 2014 Salimi proposed Stochastic Factal Search (SFS).
40 2015 Zheng developed Water Wave Optimization (WWO).
41 2016 Fathollahi Fard and Hajiaghaei-Keshteli proposed Red Deer Algorithm (RDA).
42 2017
Fathollahi Fard and Hajiaghaei-Keshteli presented Social Engineering Optimization
(SEO).
Meta-heuristics
7

Iran
Other
Summary
Other 0.65
Iran 0.35
Meta-heuristics
8A. M. Fathollahi Fard Social Engineering Optimization (SEO)

 Social Engineering (SE) is defined as indirect
attacks to obtain the people and reveal their
information by using certain techniques
(Granger, 2002).
 Sarah Granger acknowledged that defense
against the Social Engineering attacks has a
common pattern such as a mass. In addition,
she claimed that the Social Engineering attack
procedure has a four step cycle.
9
Social Engineering

 The phases of Social Engineering are as
follows:
1) Training and Retraining
2) Spotting an attack
3) Responding to attack
4) Selecting a new way or choosing some one
else
A. M. Fathollahi Fard Social Engineering Optimization (SEO) 10
Social Engineering

Scottish Red Deer
 In the first phase, the training and retraining
from attacker to defender is considered. In this
regard, the attacker wants to obtain some
special information from defender.
 This information can be put in different
contents. Perhaps, the first questions are about
famous special clip video, music, sport and
public events that occur in the community and
other dimension of personal family systems.
11
Training and Retraining

Red Deer’s Characteristics
 It is obvious that before carrying out an attack,
on the point where the probability of success is
more than other points, it must be identified.
 The attacker takes you (defender) to a position
that he/she prefers the defender to be placed in
the way of his/her goals.
 The most important thing to prevent an attack
on his/her main demand is that defender can
understand and think like an attacker.
12
Spotting an attack

Red Deer’s Roaring
 The next phase is how to respond this SE
attack and react of defender and how much
information it wishes to make striker. In SE
attacks, the most important phase is how to
respond to an attack.
 The pervious experiences and his/her
knowledge about this kind of attack may be
helped him/her.
13
Responding to an attack

Red Deer’s Mating
 At the least, in final step, the attacker try to
steal each thing that is may be useful and strike
to defender. Or if an attack is not achieved
satisfactory results or otherwise offensive
repeats or stopped by person and someone else
chooses.
14
Selecting a new way or choosing
some one else

 In this algorithm each solution is counterpart
to a person and traits of each person such as
his/her ability in contents of mathematical,
sport, business and so on are the counterpart of
all variables of each solution in search space.
15
SEO

16A. M. Fathollahi Fard Social Engineering Optimization (SEO)
Start
Initialize the attacker and the defender
Train and retrain
Spot an attack
Respond to attack
Is the number of
attacks ended?
Select a new person as defender
Stopping
condition
End

Start
Train and retrain
Spot an attack
Respond to attack
Is the number of
attacks ended?
Stopping
condition
End
LS

Start
Train and retrain
Spot an attack
Respond to attack
Is the number of
attacks ended?
Stopping
condition
End
Int.

Start
Train and retrain
Spot an attack
Respond to attack
Is the number of
attacks ended?
Stopping
condition
End
Div.

1. Initialize the attacker and the defender
20
SEO

2. Train and retrain
21
SEO

3. Spot an attack
 Obtaining
 Phishing
 Diversion theft
 Pretext
22
SEO

23
Obtaining

24
Phishing

25
Diversion theft

26
Pretext

Experiments
27
SEO
4. Respond to attack
New position of the defender is evaluated and
compared with the old position of the
defender.

Experiments
28
SEO
5. Select a new person as the defender
Here, the attacker annihilates the defender and
selects a new person randomly to perform SE
rules.
6. Stopping condition
Like other meta-heuristics, the stop condition
may be maximum time of the simulation or
the quality of the best solution ever found or
other selected condition by user.

Experiments
29
SEO

Conclusion
 In this part, six famous benchmark functions
which have been used in pervious researches
are utilized.
 These problems are numbered as P1 to P6 and
all of them are minimization problems. In all
standard functions, the optimum value is equal
to zero. Each of them has certain
characteristics.
 The details on these functions are show in
Table 1.
30
Benchmark functions

Conclusion
31
Benchmark function

Conclusion
32
Benchmark function

Conclusion
33
Benchmark function

Conclusion
34
Benchmark function
The results show the superiority of SEO and its behavior to find the
optimal solution. Four versions of SEO are presented in this study
based on four proposed techniques. All of them are reached optimal
solutions and the second technique is most successful in among other
four techniques. In addition, it seems user can choice suitable
technique by considering the type and dimension of the problems.

Conclusion
35
Engineering problem
In this part, we consider a single machine
scheduling problem. Single-machine
scheduling or single-resource scheduling is the
process of assigning a group of tasks to a single
machine or resource.

Conclusion
36
Engineering problem

Conclusion
37
Engineering problem
To compare the proposed algorithms, we utilized three
powerful methods. GA, ICA and RDA are used in this
study. In order, the problems are sorted as well. We define
6 different sizes of problems and assign equal time
-0.01
0.01
0.03
0.05
0.07
0.09
0.11
50 100 150 200 250 300
GA
ICA
RDA
SEO_1
SEO_2
SEO_3
SEO_4

Conclusion
38
Conclusion
 In this work, a new optimization algorithm, inspired by
Social Engineering is developed.
This algorithm is so simple and intelligent. Besides, it
makes a balance between the phases by creative
methods.
The result of experiments shows the performance and
efficiency of proposed method.
For future studies, more comprehensive analyses on the
SEO may still required to be explored.
Moreover, we can employ some other real techniques in
SE attacks to develop the proposed SEO.

References
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39
References

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Social Engineering Optimization (SEO)

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