Charles A. Kamhoua

ProvChain: A Blockchain-based Data Provenance Architecture in Cloud Environment with Enhanced Privacy and Availability

Cloud data provenance is metadata that records the history of the creation and operations performed on a cloud data object. Secure data provenance is crucial for data accountability, forensics and privacy. In this paper, we propose a decentralized and trusted cloud data provenance architecture using blockchain technology. Blockchain-based data provenance can provide tamper-proof records, enable the transparency of data accountability in the cloud, and help to enhance the privacy and availability of the provenance data. We make use of the cloud storage scenario and choose the cloud file as a data unit to detect user operations for collecting provenance data. We design and implement ProvChain, an architecture to collect and verify cloud data provenance, by embedding the provenance data into blockchain transactions. ProvChain operates mainly in three phases: (1) provenance data collection, (2) provenance data storage, and (3) provenance data validation. Results from performance evaluation demonstrate that ProvChain provides security features including tamper-proof provenance, user privacy and reliability with low overhead for the cloud storage applications.

Game Theoretic Modeling of Security and Interdependency in a Public Cloud

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.

Establishing evolutionary game models for CYBer security information EXchange (CYBEX)

Abstract The initiative to protect critical resources against cyber attacks requires security investments complemented with a collaborative sharing effort from every organization. A CYBersecurity information EXchange (CYBEX) framework is required to facilitate cyber-threat intelligence (CTI) sharing among the organizations to abate the impact of cyber attacks. In this research, we present an evolutionary game theoretic framework to investigate the economic benefits of cybersecurity information sharing and analyze the impacts and consequences of not participating in the game. By using micro-economic theory as substrate, we model this framework as human-society inspired evolutionary game among the organizations and investigate the implications of information sharing. Using our proposed dynamic cost adaptation scheme and distributed learning heuristic, organizations are induced toward adopting the evolutionary stable strategy of participating in the sharing framework. We also extend the evolutionary analysis to understand sharing nature of participants in a heterogeneous information exchange environment.

Cyber-Threats Information Sharing in Cloud Computing: A Game Theoretic Approach

Cybersecurity is among the highest priorities in industries, academia and governments. Cyber-threats information sharing among different organizations has the potential to maximize vulnerabilities discovery at a minimum cost. Cyber-threats information sharing has several advantages. First, it diminishes the chance that an attacker exploits the same vulnerability to launch multiple attacks in different organizations. Second, it reduces the likelihood an attacker can compromise an organization and collect data that will help him launch an attack on other organizations. Cyberspace has numerous interconnections and critical infrastructure owners are dependent on each others service. This well-known problem of cyber interdependency is aggravated in a public cloud computing platform. The collaborative effort of organizations in developing a countermeasure for a cyber-breach reduces each firms cost of investment in cyber defense. Despite its multiple advantages, there are costs and risks associated with cyber-threats information sharing. When a firm shares its vulnerabilities with others there is a risk that these vulnerabilities are leaked to the public (or to attackers) resulting in loss of reputation, market share and revenue. Therefore, in this strategic environment the firms committed to share cyber-threats information might not truthfully share information due to their own self-interests. Moreover, some firms acting selfishly may rationally limit their cybersecurity investment and rely on information shared by others to protect themselves. This can result in under investment in cybersecurity if all participants adopt the same strategy. This paper will use game theory to investigate when multiple self-interested firms can invest in vulnerability discovery and share their cyber-threat information. We will apply our algorithm to a public cloud computing platform as one of the fastest growing segments of the cyberspace.

Game theoretic attack analysis in online social network (OSN) services

In the social media era, the ever-increasing utility of Online Social Networks (OSN) services provide a variety of benefits to users, organizations, and service providers. However, OSN services also introduce new threats and privacy issues regarding the data they are dealing with. For instance, in a reliable OSN service, a user should be able to set up his desired level of information sharing and securely manage sensitive data. Currently, few approaches exist that can model OSNs for the purpose, let alone model the effects that attackers can have on these networks. In this work a novel OSN modeling approach is presented to fill the gap. This model is based on an innovative game theoretical approach and it is analyzed both from a theoretical and simulation-oriented view. The game theoretic model is implemented in order to analyze several attack scenarios. As the results show, there are several scenarios where OSN services are very vulnerable and hence more protection mechanisms should be provided in order to secure the data contained across these networks.

Modeling cooperative, selfish and malicious behaviors for Trajectory Privacy Preservation using Bayesian game theory

As new mobile Wireless Sensor Networks (mWSNs) for location-aware applications are emerging, trajectory privacy invasion is becoming an indispensable issue. Many promising techniques are under development. Considering the decentralized network architecture, most of Trajectory Privacy Preservation (TPP) techniques rely on the cooperation from peer nodes, cluster headers, or a third party. However, only a few works have addressed the issue of selfish behaviors in such cooperation required techniques. Nevertheless, the problem of facing selfish and compromised nodes in the noncooperative and hostile environment is rarely touched. In this paper, we apply Bayesian game theory to model cooperative, selfish and malicious behaviors of autonomous mobile nodes in decentralized mWSNs. We formulate and analyze the TPP game among peer nodes in both strategic and dynamic forms. The equilibrium strategies for users to evaluate the degree of trust in participating in in-network TPP activities are provided and analyzed in theoretical and simulation results.

Optimal State Management of Data Sharing in Online Social Network (OSN) Services

Although Online Social Network (OSN) services offer users a variety of benefits, they also bring new threats and privacy issues to the community. In this paper, we first define the data types in OSN services and the states of shared data with respect to Optimal, Under-shared, Over-shared, and Hybrid states. We also identify the facilitating, detracting, and preventive parameters that are responsible for the state transition of the data. We address that, in a reliable OSN service, a user should be able to set up his or her desired level of information sharing with a certain group of other users. However, it is not always clear to the ordinary users how to decide how much information they should reveal to others. Therefore, we propose an approach for helping OSN users determine their optimum levels of information sharing, taking into consideration the payoffs (potential Reward or Cost) based on the Markov decision process (MDP).

Testing for Hardware Trojans: A Game-Theoretic Approach

The microcircuit industry is witnessing a massive outsourcing of the fabrication of ICs (Integrated Circuit), as well as the use of third party IP (Intellectual Property) and COTS (Commercial Off-The-Shelf) tools during IC design. These issues raise new security challenges and threats. In particular, it brings up multiple opportunities for the insertion of malicious logic, commonly referred to as a hardware Trojan, in the IC. Testing is typically used along the IC development lifecycle to verify the functional correctness of a given chip. However, the complexity of modern ICs, together with resource and time limitations, makes exhaustive testing commonly unfeasible. In this paper, we propose a game-theoretic approachfor testing digital circuits that takes into account the decision-making process of intelligent attackers responsible for the infection of ICs with hardware Trojans. Testing for hardware Trojans is modeled as a zero-sum game between malicious manufacturers or designers (i.e., the attacker) who want to insert Trojans, and testers (i.e., the defender) whose goal is to detect the Trojans. The game results in multiple possible mixed strategy Nash equilibria that allow to identify optimum test sets that increase the probability of detecting and defeating hardware Trojans in digital logic.

G-Storm: GPU-enabled high-throughput online data processing in Storm

The Single Instruction Multiple Data (SIMD) architecture of Graphic Processing Units (GPUs) makes them perfect for parallel processing of big data. In this paper, we present the design, implementation and evaluation of G-Storm, a GPU-enabled parallel system based on Storm, which harnesses the massively parallel computing power of GPUs for high-throughput online stream data processing. G-Storm has the following desirable features: 1) G-Storm is designed to be a general data processing platform as Storm, which can handle various applications and data types. 2) G-Storm exposes GPUs to Storm applications while preserving its easy-to-use programming model. 3) G-Storm achieves high-throughput and low-overhead data processing with GPUs. We implemented G-Storm based on Storm 0.9.2 and tested it using two different applications: continuous query and matrix multiplication. Extensive experimental results show that compared to Storm, G-Storm achieves over 7x improvement on throughput for continuous query, while maintaining reasonable average tuple processing time. It also leads to 2.3x throughput improvement for the matrix multiplication application.

Surviving in Cyberspace: A Game Theoretic Approach

As information systems become ever more complex and the interdependence of these systems increases, a mission-critical system should have the fight-through ability to sustain damage yet survive with mission assurance in cyberspace. To satisfy this requirement, in this paper we propose a game theoretic approach to binary voting with a weighted majority to aggregate observations among replicated nodes. Nodes are of two types: they either vote truthfully or are malicious and thus lie. Voting is strategically performed based on a node’s belief about the percentage of compromised nodes in the system. Voting is cast as a stage game model that is a Bayesian Zero-sum game. In the resulting Bayesian Nash equilibrium, if more than a critical proportion of nodes are compromised, their collective decision is only 50% reliable; therefore, no information is obtained from voting. We overcome this by formalizing a repeated game model that guarantees a highly reliable decision process even though nearly all nodes are compromised. A survival analysis is performed to derive the total time of mission survival for both a one-shot game and the repeated game. Mathematical proofs and simulations support our model.