Analysis of Information Security Technologies and Cyber Attack Impacts

This report examines two key areas within information security. The first part investigates how machine learning techniques, such as SVM classifiers and logistic regression, can be used to detect cyber recruitment by violent extremists, focusing on data from online forums and employing methods like cross-validation and ROC curves to evaluate performance. The second part analyzes the impact of false data injection attacks (FDIA) on power systems, proposing scenarios and mathematical models to understand how attackers can manipulate system data, leading to potential issues like unnecessary load shedding and increased costs. The report utilizes techniques like differential evolution (DE) algorithms and simulations to assess the vulnerabilities and potential consequences of such attacks, emphasizing the need for enhanced cybersecurity measures.

Running head: INFORMATION SECURITY TECHNOLOGIES
Information Security Technologies
Name of the Student
Name of the University
Author’s note

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Table of Contents
Paper 7.................................................................................................................................2
Problem............................................................................................................................2
Methodology Adopted.....................................................................................................2
Techniques Used in the Solution.....................................................................................2
Paper 11...............................................................................................................................3
Problem............................................................................................................................3
Methodology Adopted.....................................................................................................4
Techniques used in the Solution......................................................................................4
References............................................................................................................................6

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Paper 7
Problem
According to Scanlon and Gerber (2014), Internet has facilitated the terrorist
communities and groups to recruit violent extremists and expand their scale of activities and size
of their groups. The potential of the online communities is more as they can use and exchange
various online resources for the purpose of carrying out illegal activity.
Methodology Adopted
This article said that in order to classify forum posts certain criteria needs to be fulfilled.
The data must be collected from popular VE groups, the data should be within a time span like
one decade and the data must be in English language or some language that can be translated in
English. This article identified Dark Web Portal to be a storage space of messages from 28
online forums that focus on Islamic discussions and extremist religious (Ríos and Munoz 2012).
The forum posts from Ansar1 have been collected and pre processed by manually annotating the
collected information as ‘a’ if it is related to VE recruitment and as ‘b’ if it a non VE recruitment
post. This article focused on an analytical approach to find out whether forum post is meant for
VE recruitment or not. A probability model has been used.
Techniques Used in the Solution
Classification functions like naïve bayes, classification trees, boosting, logistic regression
and support vector machines (SVM) were used for the purpose of classifying forum posts. The
annotated information was utilized by randomly segmenting the data. The data was broken into
ten folds (Scanlon and Gerber 2014). Cross validation have been applied. The classification
methods have been evaluated by ROC curves. The ROC curves are known for showing trade-

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offs between FPR and TPR. An area under ROC curve called AUC has been employed for the
purpose of comparing the performance of each method by making the use of single measure. The
best performance was given by SVM classifier with 0.89 AUC. This article also mentioned the
accuracy variation of all the methods. The range was 0.2 to 0.3 AUC. SVM showed the least
variation in its performance. Boosting showed the highest variation. Turkey’s range test has been
used for the purpose of determining the difference of AUC among the models. Classification
trees method gave the worst AUC performance. The task of classification is best performed by
logistic regression as well as SVM methods. The result of this article provides performance
benchmark for comparing it with future methods. This article provided a clear evidence of the
fact that the conflicts that took place in Somalia and Nigeria were the main topic of forum posts
in Ansar1 data. Logit and SVM methods can detect any VE recruitment with accuracy where the
mean AUC is greater than 0.85. The main aim of this research report was to use the methods of
data collection and analytical efforts for developing supervised learning methods for the purpose
of identifying the process of cyber recruitment that is carried out by violent extremists. This
report suggested that the detection methods can be improved in the future by incorporating non
English language in the detection as well as classification methods.
Paper 11
Problem
According to Jiongcong et al. (2016), cyber attack has the ability to cause critical hazards
to the economic as well as secure operations and functioning of the power system. This research
report has focused on the influence of FDIA on power systems. FDIA is a type of cyber attack

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where malicious data is injected into the meters. It results in fake estimations and it stops the
detection of bad data (Liang et al. 2017).
Methodology Adopted
This paper proposes two types of scenarios. One scenario is talks about a signal attack
that is fake and secure. The other scenario talks about a fake signal attack that is insecure in
nature. The first scenario is dangerous as it deceives the operator of the system and shows that
the system is secure. The second scenario will initiate the operator to take corrective steps that
are not necessary like load shedding and rescheduling (Jiongcong et al. 2016). This will affect
the system performance and consume a lot of time. Security assessment is an essential need of
the organizations that is used for monitoring and controlling the power system. This paper was
intended to perform economy and security analysis of SSA. Two main assumptions under the
methodologies used in this paper are: the attacker completely knows about system parameters
etc, the attacker has the capability to falsify analog measurements. State estimator is known for
providing state variables depending on the meter measurement combination. FDIA is analyzed
under AC and DC models.
Techniques used in the Solution
In the first scenario, faulty condition of the open circuit is the base case. A mathematical
model is proposed that can be solved by the attacker to convert insecure signal to secure signal.
In the second scenario, unnecessary rescheduling is carried out. Here the main focus of the
attacker is on online SSA. A mathematical model is formulated for the purpose of injecting
malicious data. This scenario of fake insecure signal can lead to unnecessary load shedding as
well. The attacker performs manipulation even after the rescheduling process. This paper uses
differential evolution or DE method for the purpose of solving the fake signal problem. DE

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method is a heuristic algorithm. The FDIA problem has been simulated on SSA on modified
benchmark system of IEEE-39. Real power flows are compared in case of a fake secure signal.
The fault condition of the open circuit is said to take place on 30h line of transmission that is
present in the bus system of IEEE-39. The overload of the real power system is of major
concern. In case of a fake insecure signal problem or attack the attacker manipulates normal
situations for the purpose of overloading situations by the method of solving equations that are
based on corresponding measurements. The attacker inserts malicious codes into the original
power flow to cause fake overload. The online SSA will then send insecure signal to carry out
rescheduling. The value of load scheduling can be calculated by using the DE algorithm. This
paper showed that unnecessary load shedding increases the cost by 3.71X10^6 dollar. This paper
concludes that several researches on cyber attacks on power system would be helpful in
enhancing the security of power system.

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References
Jiongcong, C.H.E.N., Liang, G., Zexiang, C.A.I., Chunchao, H.U., Yan, X.U., Fengji, L.U.O.
and Junhua, Z.H.A.O., 2016. Impact analysis of false data injection attacks on power system
static security assessment. Journal of Modern Power Systems and Clean Energy, 4(3), pp.496-
505.
Liang, G., Zhao, J., Luo, F., Weller, S. and Dong, Z.Y., 2017. A review of false data injection
attacks against modern power systems. IEEE Transactions on Smart Grid.
Ríos, S.A. and Muñoz, R., 2012. Dark Web portal overlapping community detection based on
topic models. In Proceedings of the ACM SIGKDD Workshop on Intelligence and Security
Informatics.
Scanlon, J.R. and Gerber, M.S., 2014. Automatic detection of cyber-recruitment by violent
extremists. Security Informatics, 3(1), p.5.