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In this paper, we systematically explore the attack surface of the Blockchain technology, with an emphasis on public Blockchains. Towards this goal, we attribute attack viability in the attack surface to 1) the Blockchain cryptographic constructs, 2) the distributed architecture of the systems using Blockchain, and 3) the Blockchain application context. To each of those contributing factors, we outline several attacks, including selfish mining, the 51% attack, Domain Name System (DNS) attacks, distributed denial-of-service (DDoS) attacks, consensus delay (due to selfish behavior or distributed denial-of-service attacks), Blockchain forks, orphaned and stale blocks, block ingestion, wallet thefts, smart contract attacks, and privacy attacks. We also explore the causal relationships between these attacks to demonstrate how various attack vectors are connected to one another. A secondary contribution of this work is outlining effective defense measures taken by the Blockchain technology or proposed by researchers to mitigate the effects of these attacks and patch associated vulnerabilities.

In this survey, we review the existing game-theoretic approaches for cyber security and privacy issues, categorizing their application into two classes, security and privacy. To show how game theory is utilized in cyberspace security and privacy, we select research regarding three main applications: cyber-physical security, communication security, and privacy. We present game models, features, and solutions of the selected works and describe their advantages and limitations from design to implementation of the defense mechanisms. We also identify some emerging trends and topics for future research. This survey not only demonstrates how to employ game-theoretic approaches to security and privacy but also encourages researchers to employ game theory to establish a comprehensive understanding of emerging security and privacy problems in cyberspace and potential solutions.

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Network attacks are serious concerns in today's increasingly interconnected society. Recent studies have applied conventional machine learning to network attack detection by learning the patterns of the network behaviors and training a classification model. These models usually require large labeled datasets; however, the rapid pace and unpredictability of cyber attacks make this labeling impossible in real time. To address these problems, we proposed utilizing transfer learning for detecting new and unseen attacks by transferring the knowledge of the known attacks. In our previous work, we have proposed a transfer learning-enabled framework and approach, called HeTL, which can find the common latent subspace of two different attacks and learn an optimized representation, which was invariant to attack behaviors' changes. However, HeTL relied on manual pre-settings of hyper-parameters such as relativeness between the source and target attacks. In this paper, we extended this study by proposing a clustering-enhanced transfer learning approach, called CeHTL, which can automatically find the relation between the new attack and known attack. We evaluated these approaches by stimulating scenarios where the testing dataset contains different attack types or subtypes from the training set. We chose several conventional classification models such as decision trees, random forests, KNN, and other novel transfer learning approaches as strong baselines. Results showed that proposed HeTL and CeHTL improved the performance remarkably. CeHTL performed best, demonstrating the effectiveness of transfer learning in detecting new network attacks.

Abstract-As cloud computing thrives, many small organizations are joining a public cloud to take advantage of its multiple benefits. Cloud computing is cost efficient, i.e., cloud user can reduce spending on technology infrastructure and have easy access to their information without up-front or long-term commitment of resources. Moreover, a cloud user can dynamically grow and shrink the resources provisioned to an application on demand. Despite those benefits, cyber security concern is the main reason many large organizations with sensitive information such as the Department of Defense have been reluctant to join a public cloud. This is because different public cloud users share a common platform such as the hypervisor. A common platform intensifies the well-known problem of cyber security interdependency. In fact, an attacker can compromise a virtual machine (VM) to launch an attack on the hypervisor which if compromised can instantly yield the compromising of all the VMs running on top of that hypervisor. Therefore, a user that does not invest in cyber security imposes a negative externality on others. This research uses the mathematical framework of game theory to analyze the cause and effect of interdependency in a public cloud platform. This work shows that there are multiple possible Nash equilibria of the public cloud security game. However, the players use a specific Nash equilibrium profile depending on the probability that the hypervisor is compromised given a successful attack on a user and the total expense required to invest in security. Finally, there is no Nash equilibrium in which all the users in a public cloud will fully invest in security.

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