Modern malware uses advanced techniques to hide from static and dynamic analysis tools. To achieve stealthiness when attacking a mobile device, an effective approach is the use of a covert channel built by two colluding applications to exchange data locally. Since this process is tightly coupled with the used hiding method, its detection is a challenging task, also worsened by the very low transmission rates. As a consequence, it is important to investigate how to reveal the presence of malicious software using general indicators, such as the energy consumed by the device. In this perspective, this paper aims to spot malware covertly exchanging data using two detection methods based on artificial intelligence tools, such as neural networks and decision trees. To verify their effectiveness, seven covert channels have been implemented and tested over a measurement framework using Android devices. Experimental results show the feasibility and effectiveness of the proposed approach to detect the hidden data exchange between colluding applications.

Descent algorithms based on the gradient, conjugate gradient, and Newton methods are investigated to perform optimization in moving horizon state estimation for discrete-time linear and nonlinear systems. Conditions that ensure the stability of the estimation error are established for single and multi iteration schemes with a least-squares cost function that takes into account only a batch of most recent information. Simulation results show the effectiveness of the proposed approaches also in comparison with techniques based on the Kalman filter.

The ubiquitous diffusion of cloud computing requires suitable management policies to face the workload while guaranteeing quality constraints and mitigating costs. The typical trade-off is between the used power and the adherence to a service-level metric subscribed by customers. To this aim, a possible idea is to use an optimization-based placement mechanism to select the servers where to deploy virtual machines. Unfortunately, high packing factors could lead to performance and security issues, e.g., virtual machines can compete for hardware resources or collude to leak data. Therefore, we introduce a multi-objective approach to compute optimal placement strategies considering different goals, such as the impact of hardware outages, the power required by the datacenter, and the performance perceived by users. Placement strategies are found by using a deep reinforcement learning framework to select the best placement heuristic for each virtual machine composing the workload. Results indicate that our method outperforms bin packing heuristics widely used in the literature when considering either synthetic or real workloads.

Moving-horizon (MH) state estimation is addressed for nonlinear discrete-time systems affected by bounded noises acting on system and measurement equations by minimizing a sliding-window least-squares cost function. Such a problem is solved by searching for suboptimal solutions for which a certain error is allowed in the minimization of the cost function. Nonlinear parameterized approximating functions such as feedforward neural networks are employed for the purpose of design. Thanks to the offline optimization of the parameters, the resulting MH estimation scheme requires a reduced online computational effort. Simulation results are presented to show the effectiveness of the proposed approach in comparison with other estimation techniques.

Inventory control for the management of multi-item multi-echelon distribution chains is addressed in a two-level hierarchical framework motivated by strategic and tactical points of view. Toward this end, a discrete-time dynamic model is presented together with various types of constraints to describe a generic distribution chain in detail. As to the strategic level, a worst-case approach is proposed to set up a stock replenishment policy by using the uncertain information available on long-term predictions of customers' demands. The solution of the resulting min-max problem is obtained by using a branch-and-bound algorithm to select policy parameters such as safety stocks and delivery cycle times of goods. The online decisions on the transportation of goods are made at the tactical level instead. In order to accomplish such a task, an approach based on model predictive control is proposed to exploit recent, more reliable, short-term predictions of the demands. Simulation results are presented to show the effectiveness and potential of the proposed methodology.

A dynamic discrete-time model of container flows in maritime terminals is proposed as a system of queues. Such queues are controlled via input variables that account for the use of the available resources given by the capacities of the handling machines used to move containers inside a terminal. Two feedback control strategies for the allocation of such resources are described. The first consists of a resource assignment that is proportional to the corresponding queue lengths; in the second, the assignment is obtained by the one-step-ahead optimization of a performance cost function according to a myopic approach. Simulation results are reported to compare such methodologies for the purpose of sensitivity and scenario analyses in the management of a maritime terminal.