A Comprehensive Insight into Game Theory in relevance to Cyber Security

The progressively ubiquitous connectivity in the present information systems pose newer challenges tosecurity. The conventional security mechanisms have come a long way in securing the well-definedobjectives of confidentiality, integrity, authenticity and availability. Nevertheless, with the growth in thesystem complexities and attack sophistication, providing security via traditional means can beunaffordable. A novel theoretical perspective and an innovative approach are thus required forunderstanding security from decision-making and strategic viewpoint. One of the analytical tools whichmay assist the researchers in designing security protocols for computer networks is game theory. Thegame-theoretic concept finds extensive applications in security at different levels, including thecyberspace and is generally categorized under security games. It can be utilized as a robust mathematicaltool for modelling and analyzing contemporary security issues. Game theory offers a natural frameworkfor capturing the defensive as well as adversarial interactions between the defenders and the attackers.Furthermore, defenders can attain a deep understanding of the potential attack threats and the strategiesof attackers by equilibrium evaluation of the security games. In this paper, the concept of game theoryhas been presented, followed by game-theoretic applications in cybersecurity including cryptography.Different types of games, particularly those focused on securing the cyberspace, have been analysed andvaried game-theoretic methodologies including mechanism design theories have been outlined foroffering a modern foundation of the science of cybersecurity

An enhanced AES-GCM based security protocol for securing the IoT communication

In the recent years, the devices in Internet of Things (IoT) are growing exponentially due to the emergence of many sophisticated applications. This tremendous growth leads to serious security challenges and the devices of Wireless Sensor Networks should be protected from various attacks. IoT can be configured dynamically without fixed infrastructure and the devices are communicated with one another in an Ad-hoc manner. The work presents the classification of various DDoS attacks in the IoT environment and provides a solution for replay attack. All variations of DDoS attacks are modeled using UML based activity modeling. This clearly understands the behavior of each version of attacks and their performance in the environment. The modeling also helps to construct a solution to prevent this attack from its execution. The work also proposed a trust based protocol for replay attacks which allows the attack inside the network and blocks it after identifying the attack based on its specific behavior. The network performance is improved after implementing this proposed protocol inside the network with help of simulation under realistic conditions. The performance metrics considered in the work are energy, packet loss, computational time and throughput. The paper compares the performance with the state-of-the-art schemes such as Efficient Distributed Deterministic Key and Hashbased Message Authentication Code. The experimental analysis proved that the proposed scheme outperforms the other state-of-the-works in terms of computational cost, throughput, and delay

Security Aspects of Internet of Things aided Smart Grids: a Bibliometric Survey

The integration of sensors and communication technology in power systems, known as the smart grid, is an emerging topic in science and technology. One of the critical issues in the smart grid is its increased vulnerability to cyber threats. As such, various types of threats and defense mechanisms are proposed in literature. This paper offers a bibliometric survey of research papers focused on the security aspects of Internet of Things (IoT) aided smart grids. To the best of the authors' knowledge, this is the very first bibliometric survey paper in this specific field. A bibliometric analysis of all journal articles is performed and the findings are sorted by dates, authorship, and key concepts. Furthermore, this paper also summarizes the types of cyber threats facing the smart grid, the various security mechanisms proposed in literature, as well as the research gaps in the field of smart grid security.Comment: The paper is published in Elsevier's Internet of Things journal. 25 pages + 20 pages of reference

Optimal decision making in cognitive radio networks

Cognitive Radio Networks are being researched upon heavily in the various layers of the communication structure. The task of bringing software in the physical layer of communication system led to the concept of a smart radio being able to learn, adapt and make intelligent decisions in an autonomous manner by use of a Software Defined Radio. This work provides novel concepts in the areas of spectrum sensing, learning of ongoing transmissions through Reinforcment learning, use of a game theoretic concept such as Zero-sum game for resilience of authorized users in cases of jamming, and decision making of user transmissions through Markov Decision processes. This is highly applicable in dynamic radio environments such as emergency communications required during natural disasters, large scale events and in mobile wireless communications. Such applications come under the "Internet of Things"

The University Defence Research Collaboration In Signal Processing

This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour