Biblio

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.

In January 2017 encrypted Internet traffic surpassed non-encrypted traffic. Although encryption increases security, it also masks intrusions and attacks by blocking the access to packet contents and traffic features, therefore making data analysis unfeasible. In spite of the strong effect of encryption, its impact has been scarcely investigated in the field. In this paper we study how encryption affects flow feature spaces and machine learning-based attack detection. We propose a new cross-layer feature vector that simultaneously represents traffic at three different levels: application, conversation, and endpoint behavior. We analyze its behavior under TLS and IPSec encryption and evaluate the efficacy with recent network traffic datasets and by using Random Forests classifiers. The cross-layer multi-key approach shows excellent attack detection in spite of TLS encryption. When IPsec is applied, the reduced variant obtains satisfactory detection for botnets, yet considerable performance drops for other types of attacks. The high complexity of network traffic is unfeasible for monolithic data analysis solutions, therefore requiring cross-layer analysis for which the multi-key vector becomes a powerful profiling core.