Biblio

In this paper, we propose a relational anonymous P2P communication network evaluation model based on Markov chain (AEMC), and show how to extend our model to the anonymous evaluation of sender and receiver relationship anonymity when the attacker attacks the anonymous P2P communication network and obtains some information. Firstly, the constraints of the evaluation model (the attacker assumption for message tracing) are specified in detail; then the construction of AEMC anonymous evaluation model and the specific evaluation process are described; finally, the simulation experiment is carried out, and the evaluation model is applied to the probabilistic anonymous evaluation of the sender and receiver relationship of the attacker model, and the evaluation is carried out from the perspective of user (message).

By leveraging the advancements in Information and Communication Technologies (ICT), Smart Grid (SG) aims to modernize the traditional electric power grid towards efficient distribution and reliable management of energy in the electrical domain. The SG Advanced Metering Infrastructure (AMI) contains numerous smart meters, which are deployed throughout the distribution grid. However, these smart meters are susceptible to cyberthreats that aim to disrupt the normal operation of the SG. Cyberattacks can have various consequences in the smart grid, such as incorrect customer billing or equipment destruction. Therefore, these devices should operate on a trusted basis in order to ensure the availability, confidentiality, and integrity of the metering data. In this paper, we propose a Markov chain trust model that determines the Trust Value (TV) for each AMI device based on its behavior. Finally, numerical computations were carried out in order to investigate the reaction of the proposed model to the behavior changes of a device.

A recent research shows that 88 % of Android applications that use cryptographic APIs make at least one mistake. For this reason, several tools have been proposed to detect crypto API misuses, such as CryptoLint, CMA, and CogniCryptSAsT. However, these tools depend heavily on manually designed rules, which require much cryptographic knowledge and could be error-prone. In this paper, we propose an approach based on probabilistic models, namely, hidden Markov model and n-gram model, to analyzing crypto API usages in Android applications. The difficulty lies in that crypto APIs are sensitive to not only API orders, but also their arguments. To address this, we have created a dataset consisting of crypto API sequences with arguments, wherein symbolic execution is performed. Finally, we have also conducted some experiments on our models, which shows that ( i) our models are effective in capturing the usages, detecting and locating the misuses; (ii) our models perform better than the ones without symbolic execution, especially in misuse detection; and (iii) compared with CogniCryptSAsT, our models can detect several new misuses.

The complexity and ubiquity of modern computing systems is a fertile ground for anomalies, including security and privacy breaches. In this paper, we propose a new methodology that addresses the practical challenges to implement anomaly detection approaches. Specifically, it is challenging to define normal behavior comprehensively and to acquire data on anomalies in diverse cloud environments. To tackle those challenges, we focus on anomaly detection approaches based on system performance signatures. In particular, performance signatures have the potential of detecting zero-day attacks, as those approaches are based on detecting performance deviations and do not require detailed knowledge of attack history. The proposed methodology leverages an analytical performance model and experimentation, and allows to control the rate of false positives in a principled manner. The methodology is evaluated using the TPCx-V workload, which was profiled during a set of executions using resource exhaustion anomalies that emulate the effects of anomalies affecting system performance. The proposed approach was able to successfully detect the anomalies, with a low number of false positives (precision 90%-98%).

The blockchain is a storage technology and transmission of information, transparent, secure, and operating without central control. In this paper, we propose a new decentralized trust management and cooperation model where data is shared via blockchain and we explore the revenue distribution under different consensus schemes. To reduce the power calculation with respect to the control mechanism, our proposal adopts the possibility of Proof on Trust (PoT) and Proof of proof-of-stake based trust to replace the proof of work (PoW) scheme, to carry out the mining and storage of new data blocks. To detect nodes with malicious behavior to provide false system information, the trust updating algorithm is proposed..

In decision-making problems, the actions of an agent may reveal sensitive information that drives its decisions. For instance, a corporation's investment decisions may reveal its sensitive knowledge about market dynamics. To prevent this type of information leakage, we introduce a policy synthesis algorithm that protects the privacy of the transition probabilities in a Markov decision process. We use differential privacy as the mathematical definition of privacy. The algorithm first perturbs the transition probabilities using a mechanism that provides differential privacy. Then, based on the privatized transition probabilities, we synthesize a policy using dynamic programming. Our main contribution is to bound the "cost of privacy," i.e., the difference between the expected total rewards with privacy and the expected total rewards without privacy. We also show that computing the cost of privacy has time complexity that is polynomial in the parameters of the problem. Moreover, we establish that the cost of privacy increases with the strength of differential privacy protections, and we quantify this increase. Finally, numerical experiments on two example environments validate the established relationship between the cost of privacy and the strength of data privacy protections.

Differentially private location trace synthesis (DPLTS) has recently emerged as a solution to protect mobile users' privacy while enabling the analysis and sharing of their location traces. A key challenge in DPLTS is to best preserve the utility in location trace datasets, which is non-trivial considering the high dimensionality, complexity and heterogeneity of datasets, as well as the diverse types and notions of utility. In this paper, we present OptaTrace: a utility-optimized and targeted approach to DPLTS. Given a real trace dataset D, the differential privacy parameter ε controlling the strength of privacy protection, and the utility/error metric Err of interest; OptaTrace uses Bayesian optimization to optimize DPLTS such that the output error (measured in terms of given metric Err) is minimized while ε-differential privacy is satisfied. In addition, OptaTrace introduces a utility module that contains several built-in error metrics for utility benchmarking and for choosing Err, as well as a front-end web interface for accessible and interactive DPLTS service. Experiments show that OptaTrace's optimized output can yield substantial utility improvement and error reduction compared to previous work.

We study the optimal control problem of the maximum a posteriori (MAP) state sequence detection of an adversary using smart meter data. The privacy leakage is measured using the Bayesian risk and the privacy-enhancing control is achieved in real-time using an energy storage system. The control strategy is designed to minimize the expected performance of a non-causal adversary at each time instant. With a discrete-state Markov model, we study two detection problems: when the adversary is unaware or aware of the control. We show that the adversary in the former case can be controlled optimally. In the latter case, where the optimal control problem is shown to be non-convex, we propose an adaptive-grid approximation algorithm to obtain a sub-optimal strategy with reduced complexity. Although this work focuses on privacy in smart meters, it can be generalized to other sensor networks.

In order to improve the buffering performance of the data encrypted by CP-ABE (ciphertext policy attribute based encryption), this paper proposed a Markov prefetching model based on attribute classification. The prefetching model combines the access strategy of CP-ABE encrypted file, establishes the user relationship network according to the attribute value of the user, classifies the user by the modularity-based community partitioning algorithm, and establishes a Markov prefetching model based on attribute classification. In comparison with the traditional Markov prefetching model and the classification-based Markov prefetching model, the attribute-based Markov prefetching model is proposed in this paper has higher prefetch accuracy and coverage.

A cyber-physical system (CPS) is expected to be resilient to more than one type of adversary. In this paper, we consider a CPS that has to satisfy a linear temporal logic (LTL) objective in the presence of two kinds of adversaries. The first adversary has the ability to tamper with inputs to the CPS to influence satisfaction of the LTL objective. The interaction of the CPS with this adversary is modeled as a stochastic game. We synthesize a controller for the CPS to maximize the probability of satisfying the LTL objective under any policy of this adversary. The second adversary is an eavesdropper who can observe labeled trajectories of the CPS generated from the previous step. It could then use this information to launch other kinds of attacks. A labeled trajectory is a sequence of labels, where a label is associated to a state and is linked to the satisfaction of the LTL objective at that state. We use differential privacy to quantify the indistinguishability between states that are related to each other when the eavesdropper sees a labeled trajectory. Two trajectories of equal length will be differentially private if they are differentially private at each state along the respective trajectories. We use a skewed Kantorovich metric to compute distances between probability distributions over states resulting from actions chosen according to policies from related states in order to quantify differential privacy. Moreover, we do this in a manner that does not affect the satisfaction probability of the LTL objective. We validate our approach on a simulation of a UAV that has to satisfy an LTL objective in an adversarial environment.

Caching strategies have been evaluated and compared in many studies, most often via simulation, but also in analytic methods. Knapsack solutions provide a general analytical approach for upper bounds on web caching performance. They assume objects of maximum (value/size) ratio being selected as cache content, with flexibility to define the caching value. Therefore the popularity, cost, size, time-to-live restrictions etc. per object can be included an overall caching goal, e.g., for reducing delay and/or transport path length in content delivery. The independent request model (IRM) leads to basic knapsack bounds for static optimum cache content. We show that a 2-dimensional (2D-)knapsack solution covers arbitrary request pattern, which selects dynamically changing content yielding maximum caching value for any predefined request sequence. Moreover, Belady's optimum strategy for clairvoyant caching is identified as a special case of our 2D-knapsack solution when all objects are unique. We also summarize a comprehensive picture of the demands and efficiency criteria for web caching, including updating speed and overheads. Our evaluations confirm significant performance gaps from LRU to advanced GreedyDual and score-based web caching methods and to the knapsack bounds.

There are a lot of emerging security equipment required to be tested on detection rate (DR) and false alarm rate (FAR) for prohibited items. This article imports Bayesian approach to accept or reject DR and FAR. The detailed quantitative predictions can be made through the posterior distribution obtained by Markov chain Monte Carlo method. Based on this, HDI + ROPE decision rule is established. For the tests that need to make early decision, HDI + ROPE stopping rule is presented with biased estimate value, and criterial precision rule is presented with unbiased estimate value. Choosing the stopping rule according to the test purpose can achieve the balance of efficiency and accuracy.

Many important security informatics problems require consideration of dynamical phenomena for their solution; examples include predicting the behavior of individuals in social networks and distinguishing malicious and innocent computer network activities based on activity traces. While information theory offers powerful tools for analyzing dynamical processes, to date the application of information-theoretic methods in security domains has focused on static analyses (e.g., cryptography, natural language processing). This paper leverages information-theoretic concepts and measures to quantify the similarity of pairs of stochastic dynamical systems, and shows that this capability can be used to solve important problems which arise in security applications. We begin by presenting a concise review of the information theory required for our development, and then address two challenging tasks: 1.) characterizing the way influence propagates through social networks, and 2.) distinguishing malware from legitimate software based on the instruction sequences of the disassembled programs. In each application, case studies involving real-world datasets demonstrate that the proposed techniques outperform standard methods.

Human behaviors are often prohibited, or permitted by social norms. Therefore, if autonomous agents interact with humans, they also need to reason about various legal rules, social and ethical social norms, so they would be trusted and accepted by humans. Inverse Reinforcement Learning (IRL) can be used for the autonomous agents to learn social norm-compliant behavior via expert demonstrations. However, norms are context-sensitive, i.e. different norms get activated in different contexts. For example, the privacy norm is activated for a domestic robot entering a bathroom where a person may be present, whereas it is not activated for the robot entering the kitchen. Representing various contexts in the state space of the robot, as well as getting expert demonstrations under all possible tasks and contexts is extremely challenging. Inspired by recent work on Modularized Normative MDP (MNMDP) and early work on context-sensitive RL, we propose a new IRL framework, Context-Sensitive Norm IRL (CNIRL). CNIRL treats states and contexts separately, and assumes that the expert determines the priority of every possible norm in the environment, where each norm is associated with a distinct reward function. The agent chooses the action to maximize its cumulative rewards. We present the CNIRL model and show that its computational complexity is scalable in the number of norms. We also show via two experimental scenarios that CNIRL can handle problems with changing context spaces.

With the increasing interdependence of critical infrastructures, the probability of a specific infrastructure to experience a complex cyber-physical attack is increasing. Thus it is important to analyze the risk of an attack and the dynamics of its propagation in order to design and deploy appropriate countermeasures. The attack trees, commonly adopted to this aim, have inherent shortcomings in representing interdependent, concurrent and sequential attacks. To overcome this, the work presented here proposes a stochastic methodology using Petri Nets and Continuous Time Markov Chain (CTMC) to analyze the attacks, considering the individual attack occurrence probabilities and their stochastic propagation times. A procedure to convert a basic attack tree into an equivalent CTMC is presented. The proposed method is applied in a case study to calculate the different attack propagation characteristics. The characteristics are namely, the probability of reaching the root node & sub attack nodes, the mean time to reach the root node and the mean time spent in the sub attack nodes before reaching the root node. Additionally, the method quantifies the effectiveness of specific defenses in reducing the attack risk considering the efficiency of individual defenses.

Microgrids' dependency on communication links exposes the control systems to cyber attack threats. In this work, instead of designing reactive defense approaches, a proacitve moving target defense mechanism is proposed for securing microgrid secondary frequency control from denial of service (DoS) attack. The sensor data is transmitted by following a Markov process, not in a deterministic way. This uncertainty will increase the difficulty for attacker's decision making and thus significantly reduce the attack space. As the system parameters are constantly changing, a gain scheduling based secondary frequency controller is designed to sustain the system performance. Case studies of a microgrid with four inverter-based DGs show the proposed moving target mechanism can enhance the resiliency of the microgrid control systems against DoS attacks.

In order to overcome the problem of edge information loss in the process of neural network processing, a method of neural network style transfer based on edge detection is presented. The edge information of the content image is extracted, and the edge information image is processed in the neural network together with the content image and the style image to constrain the edge information of the content image. Compared with Gatys algorithm and markov random field neural network algorithm, the content image edge structure after image style transfer is successfully retained.

Advanced Persistent Threats (APTs) are stealthy, sophisticated, long-term, multi-stage attacks that threaten the security of sensitive information. Dynamic Information Flow Tracking (DIFT) has been proposed as a promising mechanism to detect and prevent various cyber attacks in computer systems. DIFT tracks suspicious information flows in the system and generates security analysis when anomalous behavior is detected. The number of information flows in a system is typically large and the amount of resources (such as memory, processing power and storage) required for analyzing different flows at different system locations varies. Hence, efficient use of resources is essential to maintain an acceptable level of system performance when using DIFT. On the other hand, the quickest detection of APTs is crucial as APTs are persistent and the damage caused to the system is more when the attacker spends more time in the system. We address the problem of detecting APTs and model the trade-off between resource efficiency and quickest detection of APTs. We propose a game model that captures the interaction of APT and a DIFT-based defender as a two-player, multi-stage, zero-sum, Stackelberg semi-Markov game. Our game considers the performance parameters such as false-negatives generated by DIFT and the time required for executing various operations in the system. We propose a two-time scale Q-learning algorithm that converges to a Stackelberg equilibrium under infinite horizon, limiting average payoff criteria. We validate our model and algorithm on a real-word attack dataset obtained using Refinable Attack INvestigation (RAIN) framework.

Advanced Persistent Threat (APT) is a stealthy, continuous and sophisticated method of network attacks, which can cause serious privacy leakage and millions of dollars losses. In this paper, we introduce a new game-theoretic framework of the interaction between a defender who uses limited Security Resources(SRs) to harden network and an attacker who adopts a multi-stage plan to attack the network. The game model is derived from Stackelberg games called a Multi-stage Maze Network Game (M2NG) in which the characteristics of APT are fully considered. The possible plans of the attacker are compactly represented using attack graphs(AGs), but the compact representation of the attacker's strategies presents a computational challenge and reaching the Nash Equilibrium(NE) is NP-hard. We present a method that first translates AGs into Markov Decision Process(MDP) and then achieves the optimal SRs allocation using the policy hill-climbing(PHC) algorithm. Finally, we present an empirical evaluation of the model and analyze the scalability and sensitivity of the algorithm. Simulation results exhibit that our proposed reinforcement learning-based SRs allocation is feasible and efficient.

This paper investigates the impact of authentication on effective capacity (EC) of an underwater acoustic (UWA) channel. Specifically, the UWA channel is under impersonation attack by a malicious node (Eve) present in the close vicinity of the legitimate node pair (Alice and Bob); Eve tries to inject its malicious data into the system by making Bob believe that she is indeed Alice. To thwart the impersonation attack by Eve, Bob utilizes the distance of the transmit node as the feature/fingerprint to carry out feature-based authentication at the physical layer. Due to authentication at Bob, due to lack of channel knowledge at the transmit node (Alice or Eve), and due to the threshold-based decoding error model, the relevant dynamics of the considered system could be modelled by a Markov chain (MC). Thus, we compute the state-transition probabilities of the MC, and the moment generating function for the service process corresponding to each state. This enables us to derive a closed-form expression of the EC in terms of authentication parameters. Furthermore, we compute the optimal transmission rate (at Alice) through gradient-descent (GD) technique and artificial neural network (ANN) method. Simulation results show that the EC decreases under severe authentication constraints (i.e., more false alarms and more transmissions by Eve). Simulation results also reveal that the (optimal transmission rate) performance of the ANN technique is quite close to that of the GTJ method.

This paper proposes a method for detecting anomalies in video data. A Variational Autoencoder (VAE) is used for reducing the dimensionality of video frames, generating latent space information that is comparable to low-dimensional sensory data (e.g., positioning, steering angle), making feasible the development of a consistent multi-modal architecture for autonomous vehicles. An Adapted Markov Jump Particle Filter defined by discrete and continuous inference levels is employed to predict the following frames and detecting anomalies in new video sequences. Our method is evaluated on different video scenarios where a semi-autonomous vehicle performs a set of tasks in a closed environment.

A Robot Operating System (ROS) plays a significant role in organizing industrial robots for manufacturing. With an increasing number of the robots, the operators integrate a ROS with networked communication to share the data. This cyber-physical nature exposes the ROS to cyber attacks. To this end, this paper proposes a cross-layer approach to achieve secure and resilient control of a ROS. In the physical layer, due to the delay caused by the security mechanism, we design a time-delay controller for the ROS agent. In the cyber layer, we define cyber states and use Markov Decision Process to evaluate the tradeoffs between physical and security performance. Due to the uncertainty of the cyber state, we extend the MDP to a Partially Observed Markov Decision Process (POMDP). We propose a threshold solution based on our theoretical results. Finally, we present numerical examples to evaluate the performance of the secure and resilient mechanism.

We classify .NET files as either benign or malicious by examining directed graphs derived from the set of functions comprising the given file. Each graph is viewed probabilistically as a Markov chain where each node represents a code block of the corresponding function, and by computing the PageRank vector (Perron vector with transport), a probability measure can be defined over the nodes of the given graph. Each graph is vectorized by computing Lebesgue antiderivatives of hand-engineered functions defined on the vertex set of the given graph against the PageRank measure. Files are subsequently vectorized by aggregating the set of vectors corresponding to the set of graphs resulting from decompiling the given file. The result is a fast, intuitive, and easy-to-compute glass-box vectorization scheme, which can be leveraged for training a standalone classifier or to augment an existing feature space. We refer to this vectorization technique as PageRank Measure Integration Vectorization (PMIV). We demonstrate the efficacy of PMIV by training a vanilla random forest on 2.5 million samples of decompiled. NET, evenly split between benign and malicious, from our in-house corpus and compare this model to a baseline model which leverages a text-only feature space. The median time needed for decompilation and scoring was 24ms. 11Code available at https://github.com/gtownrocks/grafuple.

In this paper, we propose a robust Nash strategy for a class of uncertain Markov jump delay stochastic systems (UMJDSSs) via static output feedback (SOF). After establishing the extended bounded real lemma for UMJDSS, the conditions for the existence of a robust Nash strategy set are determined by means of cross coupled stochastic matrix inequalities (CCSMIs). In order to solve the SOF problem, an heuristic algorithm is developed based on the algebraic equations and the linear matrix inequalities (LMIs). In particular, it is shown that robust convergence is guaranteed under a new convergence condition. Finally, a practical numerical example based on the congestion control for active queue management is provided to demonstrate the reliability and usefulness of the proposed design scheme.

We formulate a tracker which performs incessant decision making in order to track objects where the objects may undergo different challenges such as partial occlusions, moving camera, cluttered background etc. In the process, the agent must make a decision on whether to keep track of the object when it is occluded or has moved out of the frame temporarily based on its prediction from the previous location or to reinitialize the tracker based on the belief that the target has been lost. Instead of the heuristic methods we depend on reward and penalty based training that helps the agent reach an optimal solution via this partially observable Markov decision making (POMDP). Furthermore, we employ deeply learned compositional model to estimate human pose in order to better handle occlusion without needing human inputs. By learning compositionality of human bodies via deep neural network the agent can make better decision on presence of human in a frame or lack thereof under occlusion. We adapt skeleton based part representation and do away with the large spatial state requirement. This especially helps in cases where orientation of the target in focus is unorthodox. Finally we demonstrate that the deep reinforcement learning based training coupled with pose estimation capabilities allows us to train and tag multiple large video datasets much quicker than previous works.