Covert Timing Channel Analysis Either as Cyber Attacks or Confidential Applications

Covert timing channels are an important alternative for transmitting information in the world of the Internet of Things (IoT). In covert timing channels data are encoded in inter-arrival times between consecutive packets based on modifying the transmission time of legitimate traffic. Typically, the modification of time takes place by delaying the transmitted packets on the sender side. A key aspect in covert timing channels is to find the threshold of packet delay that can accurately distinguish covert traffic from legitimate traffic. Based on that we can assess the level of dangerous of security threats or the quality of transferred sensitive information secretly. In this paper, we study the inter-arrival time behavior of covert timing channels in two different network configurations based on statistical metrics, in addition we investigate the packet delaying threshold value. Our experiments show that the threshold is approximately equal to or greater than double the mean of legitimate inter-arrival times. In this case covert timing channels become detectable as strong anomalies

Learning from expert advice framework: Algorithms and applications

Online recommendation systems have been widely used by retailers, digital marketing, and especially in e-commerce applications. Popular sites such as Netflix and Amazon suggest movies or general merchandise to their clients based on recommendations from peers. At core of recommendation systems resides a prediction algorithm, which based on recommendations received from a set of experts (users), recommends objects to other users. After a user ``consumes" an object, his feedback provided to the system is used to assess the performance of experts at that round and adjust the predictions of the recommendation system for the future rounds. This so-called ``learning from expert advice'' framework has been extensively studied in the literature. In this dissertation, we investigate various settings and applications ranging from partial information, adversarial scenarios, to limited resources. We propose provable algorithms for such systems, along with theoretical and experimental results. In the first part of the thesis, we focus our attention to a generalized model of learning from expert advice in which experts could abstain from participating at some rounds. Our proposed online algorithm falls into the class of weighted average predictors and uses a time varying multiplicative weight update rule. This update rule changes the weight of an expert based on his relative performance compared to the average performance of available experts at the current round. We prove the convergence of our algorithm to the best expert, defined in terms of both availability and accuracy, in the stochastic setting. Next, we study the optimal adversarial strategies against the weighted average prediction algorithm. All but one expert are honest and the malicious expert's goal is to sabotage the performance of the algorithm by strategically providing dishonest recommendations. We formulate the problem as a Markov decision process (MDP) and apply policy iteration to solve it. For the logarithmic loss, we prove that the optimal strategy for the adversary is the greedy policy, whereas for the absolute loss, in the $2$-experts, discounted cost setting, we prove that the optimal strategy is a threshold policy. We extend the results to the infinite horizon problem and find the exact thresholds for the stationary optimal policy. As an effort to investigate the extended problem, we use a mean field approach in the $N$-experts setting to find the optimal strategy when the predictions of the honest experts are i.i.d. In addition to designing an effective weight update rule and investigating optimal strategies of malicious experts, we also consider active learning applications for learning with expert advice framework. In this application, the target is to reduce the number of labeling while still keeping the regret bound as small as possible. We proposed two algorithms, EPSL and EPAL, which are able to efficiently request label for each object. In essence, the idea of two algorithms is to examine the opinion ranges of experts, and decide to acquire labels based on the maximum difference of those opinion using a randomized policy. Both algorithms obtain nearly optimal regret bound up to some constant depending on the characteristics of experts' predictions. Last but not least, we turn our attention to the generalized ``best arm identification" problem in which, at each time, there is a subset of products whose rewards or profits are unknown (but follow some fixed distributions), and the goal is to select the best product to recommend to users after trying on a number of sampling. We propose UCB based (Upper Confidence Bound) algorithms that provide flexible parameter tuning based on the availability of each arm in the collection. We also propose a simple, yet efficient, uniform sampling algorithm for this problem. We proved that, for these algorithms, the error of selecting the incorrect arm decays exponentially over time

Energy and performance-optimized scheduling of tasks in distributed cloud and edge computing systems

Infrastructure resources in distributed cloud data centers (CDCs) are shared by heterogeneous applications in a high-performance and cost-effective way. Edge computing has emerged as a new paradigm to provide access to computing capacities in end devices. Yet it suffers from such problems as load imbalance, long scheduling time, and limited power of its edge nodes. Therefore, intelligent task scheduling in CDCs and edge nodes is critically important to construct energy-efficient cloud and edge computing systems. Current approaches cannot smartly minimize the total cost of CDCs, maximize their profit and improve quality of service (QoS) of tasks because of aperiodic arrival and heterogeneity of tasks. This dissertation proposes a class of energy and performance-optimized scheduling algorithms built on top of several intelligent optimization algorithms. This dissertation includes two parts, including background work, i.e., Chapters 3–6, and new contributions, i.e., Chapters 7–11. 1) Background work of this dissertation. Chapter 3 proposes a spatial task scheduling and resource optimization method to minimize the total cost of CDCs where bandwidth prices of Internet service providers, power grid prices, and renewable energy all vary with locations. Chapter 4 presents a geography-aware task scheduling approach by considering spatial variations in CDCs to maximize the profit of their providers by intelligently scheduling tasks. Chapter 5 presents a spatio-temporal task scheduling algorithm to minimize energy cost by scheduling heterogeneous tasks among CDCs while meeting their delay constraints. Chapter 6 gives a temporal scheduling algorithm considering temporal variations of revenue, electricity prices, green energy and prices of public clouds. 2) Contributions of this dissertation. Chapter 7 proposes a multi-objective optimization method for CDCs to maximize their profit, and minimize the average loss possibility of tasks by determining task allocation among Internet service providers, and task service rates of each CDC. A simulated annealing-based bi-objective differential evolution algorithm is proposed to obtain an approximate Pareto optimal set. A knee solution is selected to schedule tasks in a high-profit and high-quality-of-service way. Chapter 8 formulates a bi-objective constrained optimization problem, and designs a novel optimization method to cope with energy cost reduction and QoS improvement. It jointly minimizes both energy cost of CDCs, and average response time of all tasks by intelligently allocating tasks among CDCs and changing task service rate of each CDC. Chapter 9 formulates a constrained bi-objective optimization problem for joint optimization of revenue and energy cost of CDCs. It is solved with an improved multi-objective evolutionary algorithm based on decomposition. It determines a high-quality trade-off between revenue maximization and energy cost minimization by considering CDCs’ spatial differences in energy cost while meeting tasks’ delay constraints. Chapter 10 proposes a simulated annealing-based bees algorithm to find a close-to-optimal solution. Then, a fine-grained spatial task scheduling algorithm is designed to minimize energy cost of CDCs by allocating tasks among multiple green clouds, and specifies running speeds of their servers. Chapter 11 proposes a profit-maximized collaborative computation offloading and resource allocation algorithm to maximize the profit of systems and guarantee that response time limits of tasks are met in cloud-edge computing systems. A single-objective constrained optimization problem is solved by a proposed simulated annealing-based migrating birds optimization. This dissertation evaluates these algorithms, models and software with real-life data and proves that they improve scheduling precision and cost-effectiveness of distributed cloud and edge computing systems