Cyber security is among the most complex and rapidly evolving issues and has been the focus of present day organizations. Cyber security risk management is the process of managing or reducing potentially harmful and uncertain events that posse as threats to cyber security. It involves looking at what could go wrong on the cyber space and deciding on ways to prevent or minimize their occurrences or effects. One of the prominent cyber security risk management techniques is the Game Theoretic Approach (GTA), which focuses on the use of resources, internal controls, information sharing, technical improvements, behavioral or organizational scale-ups and cyber insurance for cyber risk management. This paper presents a review of game theoretic-based model for cyber security risk management. Specifically, issues on modeling, some related works and significance of game theoretic approach to cyber security risk management are presented. Findings from the review revealed the peculiarities and specificity of each model. It is also revealed that the models are just evolving and require much improvement.
This study designed and simulated cyber situation awareness model for gaining experience of cyberspace condition. This was with a view to timely detecting anomalous activities and taking proactive decision safeguard the cyberspace. The situation awareness model was modelled using Artificial Intelligence (AI) technique. The cyber situation perception sub-model of the situation awareness model was modelled using Artificial Neural Networks (ANN). The comprehension and projection submodels of the situation awareness model were modelled using Rule-Based Reasoning (RBR) techniques. The cyber situation perception sub-model was simulated in MATLAB 7.0 using standard intrusion dataset of KDD'99. The cyber situation perception sub-model was evaluated for threats detection accuracy using precision, recall and overall accuracy metrics. The simulation result obtained for the performance metrics showed that the cyber-situation sub-model of the cybersituation model better with increase in number of training data records. The cyber situation model designed was able to meet its overall goal of assisting network administrators to gain experience of cyberspace condition. The model was capable of sensing the cyberspace condition, perform analysis based on the sensed condition and predicting the near future condition of the cyberspace.
Security (privacy, confidentiality and integrity) of pre-electoral, electoral and post electoral phases of the electioneering process is fundamental to the success of Electronic Voting (E-Voting) Systems. Crystography, which is the combination of cryptography and steganography could be a fitting ‘tool kit’ for enhancing the security of sensitive election-related information transmitted over public networks, thereby also ensuring free, fair and credible election/voting. Most of the existing secure e-voting systems are based on public key cryptographic schemes like RSA and Elliptic Curve Cryptography (ECC), whose security depends on the difficulty of solving Integer Factorization Problem (IFP) and Discrete Logarithm problem (DLP) respectively. However, techniques for solving IFP and DLP problems, improves continually. One of such is the quantum algorithm discovered by Peter Shor in 1994, which can solve both IFP and DLP problems in polynomial time. Consequently, the existence of quantum computers in the range of 1000 bits would spell doom to systems based on those problems. This paper presents the development of a new crystographic system that combines Post Quantum Cryptography with steganography to ensure that the security of e-voting is maintained both in classical computing era as well as post-quantum computing era. Our experiments’ results shows that our proposed system performed better than existing ones.
The unpredictable cyber attackers and threats have to be detected in order to determine the outcome of risk in a network environment. This work develops a Bayesian network classifier to analyse the network traffic in a cyber situation. It is a tool that aids reasoning under uncertainty to determine certainty. It further analyze the level of risk using a modified risk matrix criteria. The classifier developed was experimented with various records extracted from the KDD Cup '99 dataset with 490,021 records. The evaluations showed that the Bayesian Network classifier is a suitable model which resulted in same performance level for classifying the Denial of Service (DoS) attacks with Association Rule Mining while as well as Genetic Algorithm, the Bayesian Network classifier performed better in classifying probe and User to Root (U2R) attacks and classified DoS equally. The result of the classification showed that Bayesian network classifier is a classification model that thrives well in network security. Also, the level of risk analysed from the adapted risk matrix showed that DoS attack has the most frequent occurrence and falls in the generally unacceptable risk zone.
The higher subscription, relative importance of voice calls, client's complaints and economy depression that now make clients to get value for money paid and need for more revenue by mobile network operator (MNOs) justified this work. The objective of this work is to measure, analyse, evaluate the performance of MNOs, and to recommend ways to improving their quality of service (QoS). Drive test approach was used for the measurements and statistical methods for the analysis. Results of the analysis shows that the quality of Voice service offered by MNOs is not optimal and there is room for improved quality service. Based on the key performance indicators, the mobile operators did not perform up to expectation. There are differences in the quality of voice service offered across mobile network operator networks based on the time of the day and the area under consideration. MNO1, MNO2, MNO3 and MNO4 gave varying quality of service. MNO4 had absolutely no dropped calls and performed best and consistently gave a retainability ratio above the target. MNO1 performance metrics were below the key performance indicator thresholds given by the Nigerian Communication Commission. subscribers every year, by March 2017; there were 152,467,198 subscribers  and four mobile network operators. MTN remained the largest provider, and accounted for 36% of subscriber base, Airtel, Globalcom and others have 22.80%, 24.56% and 12.91% subscribers' base respectively. Teledensity grew from 16.27% in 2010 to 103.61% for the year 2017 .Mobile communication is also a major source of Gross Domestic Product (GDP) for the country. Figure 2 shows Teledensity growth curve of Nigeria in relation to the GDP contribution by the telecommunication industry. GDP data in the telecommunication sector for the year 2004 -2009 was unavailable from. Data for years 2010 to 2017 (which is in presented in Figure 2) show that the telecommunication industry is a major player in the country's economy . The telecommunication sector experiences various challenges despite numerous applications and advantages of mobile telephony.
Game-theoretic modeling of computer security views security attack scenarios as an optimization game comprising of multiple players notably the attackers and the defenders (system administrators). This paper first presents theoretically, a two-player zero-sum stochastic game model of the interaction between malicious users and network administrators and secondly introduces a hypothetical network of a typical scenario to show the applicability of our model within that scenario. State games are encoded using a binary scheme in order to properly capture components of the underlying network environment. Our solution involves reducing each state game into a min and max linear programming problems for both the defender and attacker respectively. Game costs, rewards and outcomes are modeled to closely match real world measurements. We propose the use of a combination of the pivotal algorithm and a custom stochastic algorithm to compute the optimal (best-response) strategies for the players at each state. We also describe how the results can be analyzed to show how the optimal strategies can be used by the network administrators to predict adversary's actions, determine vulnerable network assets and suggest optimal defense strategies.
Unexpectedly, the level of patronage being experienced in Global System for Mobile Communication (GSM) in Nigeria is overwhelming. This is as a result of freedom of calling from anywhere at anytime and clarity of the voice enjoyed in GSM since it is on a digital technology platform. This has brought a lot of congestion in the network resulting in poor services by the operators. This research has developed a management algorithm for the management of the congestion experienced in the GSM network in Nigeria. It explores the use of Erlang-B in determining the appropriate probability level for some range of subscribers. Thereafter, when there is congestion, block time sharing, dynamic allocation without time slicing, dynamic allocation time slicing with signal sensing, frequently recent call allocation, and priority allocation algorithms were developed to manage the congestion. Furthermore, a hybrid algorithm was developed that integrates all the algorithms together in other to manage the congestion considering all the strengths and constraints of each algorithm. If the recommended congestion management algorithm is followed comprehensibly, the congestion problem on the GSM network will be reduced drastically.
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