Biblio

In this short paper, we survey some work at the intersection of Artificial Intelligence (AI) and security that are based on game theoretic considerations, and particularly focus on the author's (our) contribution in these areas. One half of this paper focuses on applications of game theoretic and learning reasoning for addressing security applications such as in public safety and wildlife conservation. In the second half, we present recent work that attacks the learning components of these works, leading to sub-optimal defense allocation. We finally end by pointing to issues and potential research problems that can arise due to data quality in the real world.

In this paper, we present the architecture of a Smart Industry inspired platform designed for Agriculture 4.0 applications and, specifically, to optimize an ecosystem of SW and HW components for animal repelling. The platform implementation aims to obtain reliability and energy efficiency in a system aimed to detect, recognize, identify, and repel wildlife by generating specific ultrasound signals. The wireless sensor network is composed of OpenMote hardware devices coordinated on a mesh network based on the 6LoWPAN protocol, and connected to an FPGA-based board. The system, activated when an animal is detected, elaborates the data received from a video camera connected to FPGA-based hardware devices and then activates different ultrasonic jammers belonging to the OpenMotes network devices. This way, in real-time wildlife will be progressively moved away from the field to be preserved by the activation of specific ultrasonic generators. To monitor the daily behavior of the wildlife, the ecosystem is expanded using a time series database running on a Cloud platform.

The Internet of Things (IoT) provides connectivity between heterogeneous devices in different applications, such as smart wildlife, supply chain and traffic management. Trust management system (TMS) assesses the trustworthiness of service with respect to its quality. Under different context information, a service provider may be trusted in one context but not in another. The existing context-aware trust models usually store trust values under different contexts and search the closest (to a given context) record to evaluate the trustworthiness of a service. However, it is not suitable for distributed resource-constrained IoT devices which have small memory and low power. Reputation systems are applied in many trust models where trustor obtains recommendations from others. In context-based trust evaluation, it requires interactive queries to find relevant information from remote devices. The communication overhead and energy consumption are issues in low power networks like 6LoWPAN. In this paper, we propose a new context-aware trust model for lightweight IoT devices. The proposed model provides a trustworthiness overview of a service provider without storing past behavior records, that is, constant size storage. The proposed model allows a trustor to decide the significance of context items. This could result in distinctive decisions under the same trustworthiness record. We also show the performance of the proposed model under different attacks.