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Register a new userSecuring Accelerators with Dynamic Information Flow Tracking
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Added by
Luca Piccolboni
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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Systems-on-chip (SoCs) are becoming heterogeneous: they combine general-purpose processor cores with application-specific hardware components, also known as accelerators, to improve performance and energy efficiency. The advantages of heterogeneity, however, come at a price of threatening security. The architectural dissimilarities of processors and accelerators require revisiting the current security techniques. With this hardware demo, we show how accelerators can break dynamic information flow tracking (DIFT), a well-known security technique that protects systems against software-based attacks. We also describe how the security guarantees of DIFT can be re-established with a hardware solution that has low performance and area penalties.
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Advanced persistent threats (APTs) are organized prolonged cyberattacks by sophisticated attackers. Although APT activities are stealthy, they interact with the system components and these interactions lead to information flows. Dynamic Information Flow Tracking (DIFT) has been proposed as one of the effective ways to detect APTs using the information flows. However, wide range security analysis using DIFT results in a significant increase in performance overhead and high rates of false-positives and false-negatives generated by DIFT. In this paper, we model the strategic interaction between APT and DIFT as a non-cooperative stochastic game. The game unfolds on a state space constructed from an information flow graph (IFG) that is extracted from the system log. The objective of the APT in the game is to choose transitions in the IFG to find an optimal path in the IFG from an entry point of the attack to an attack target. On the other hand, the objective of DIFT is to dynamically select nodes in the IFG to perform security analysis for detecting APT. Our game model has imperfect information as the players do not have information about the actions of the opponent. We consider two scenarios of the game (i) when the false-positive and false-negative rates are known to both players and (ii) when the false-positive and false-negative rates are unknown to both players. Case (i) translates to a game model with complete information and we propose a value iteration-based algorithm and prove the convergence. Case (ii) translates to a game with unknown transition probabilities. In this case, we propose Hierarchical Supervised Learning (HSL) algorithm that integrates a neural network, to predict the value vector of the game, with a policy iteration algorithm to compute an approximate equilibrium. We implemented our algorithms on real attack datasets and validated the performance of our approach.
Every organisation today wants to adopt cloud computing paradigm and leverage its various advantages. Today everyone is aware of its characteristics which have made it so popular and how it can help the organisations focus on their core activities leaving all IT services development and maintenance to the cloud service providers. Application Programming Interfaces (APIs) act as the interface between the CSPs and the consumers. This paper proposes an improved access control mechanism for securing the Cloud APIs.
Information flow between components of a system takes many forms and is key to understanding the organization and functioning of large-scale, complex systems. We demonstrate three modalities of information flow from time series X to time series Y. Intrinsic information flow exists when the past of X is individually predictive of the present of Y, independent of Ys past; this is most commonly considered information flow. Shared information flow exists when Xs past is predictive of Ys present in the same manner as Ys past; this occurs due to synchronization or common driving, for example. Finally, synergistic information flow occurs when neither Xs nor Ys pasts are predictive of Ys present on their own, but taken together they are. The two most broadly-employed information-theoretic methods of quantifying information flow---time-delayed mutual information and transfer entropy---are both sensitive to a pair of these modalities: time-delayed mutual information to both intrinsic and shared flow, and transfer entropy to both intrinsic and synergistic flow. To quantify each mode individually we introduce our cryptographic flow ansatz, positing that intrinsic flow is synonymous with secret key agreement between X and Y. Based on this, we employ an easily-computed secret-key-agreement bound---intrinsic mutual information&mdashto quantify the three flow modalities in a variety of systems including asymmetric flows and financial markets.
Advanced Persistent Threats (APTs) are stealthy customized attacks by intelligent adversaries. This paper deals with the detection of APTs that infiltrate cyber systems and compromise specifically targeted data and/or infrastructures. Dynamic information flow tracking is an information trace-based detection mechanism against APTs that taints suspicious information flows in the system and generates security analysis for unauthorized use of tainted data. In this paper, we develop an analytical model for resource-efficient detection of APTs using an information flow tracking game. The game is a nonzero-sum, turn-based, stochastic game with asymmetric information as the defender cannot distinguish whether an incoming flow is malicious or benign and hence has only partial state observation. We analyze equilibrium of the game and prove that a Nash equilibrium is given by a solution to the minimum capacity cut set problem on a flow-network derived from the system, where the edge capacities are obtained from the cost of performing security analysis. Finally, we implement our algorithm on the real-world dataset for a data exfiltration attack augmented with false-negative and false-positive rates and compute an optimal defender strategy.
Advanced Persistent Threats (APTs) infiltrate cyber systems and compromise specifically targeted data and/or resources through a sequence of stealthy attacks consisting of multiple stages. Dynamic information flow tracking has been proposed to detect APTs. In this paper, we develop a dynamic information flow tracking game for resource-efficient detection of APTs via multi-stage dynamic games. The game evolves on an information flow graph, whose nodes are processes and objects (e.g. file, network endpoints) in the system and the edges capture the interaction between different processes and objects. Each stage of the game has pre-specified targets which are characterized by a set of nodes of the graph and the goal of the APT is to evade detection and reach a target node of that stage. The goal of the defender is to maximize the detection probability while minimizing performance overhead on the system. The resource costs of the players are different and the information structure is asymmetric resulting in a nonzero-sum imperfect information game. We first calculate the best responses of the players and characterize the set of Nash equilibria for single stage attacks. Subsequently, we provide a polynomial-time algorithm to compute a correlated equilibrium for the multi-stage attack case. Finally, we experiment our model and algorithms on real-world nation state attack data obtained from Refinable Attack Investigation system.
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