Cybersecurity in Large Scale Massive Wireless Networks: Malware Epidemics Characterization and Countermeasures

Large-scale wireless connectivity is pivotal for interfacing the cyber and physical worlds. The foreseen Internet of Things (IoT) and cyber physical systems (CPS) will rely on billions of wireless enabled devices (e.g., sensors, embedded systems, actuators, machines, and vehicles) for big data aggregation and smart automation. Such large-scale wireless connectivity and high density devices make IoT/CPS highly susceptible to malicious software (malware) epidemics. In particular, malware worms can exploit multi-hop wireless connectivity to stealthily diffuse throughout the wireless network and compromise devices without being noticed to security servers at the core network. Compromised devices can be then used by adversaries to remotely launch cyber attacks that cause large-scale critical physical damage and threaten public safety. In this regards, this proposal first aims at overviewing the different types, threats, and propagation models for malware epidemics in large-scale IoT/CPS. Then, different mathematical techniques to characterize malware diffusion in large-scale wireless networks will be surveyed and compared. Such mathematical models will be then utilized to develop novel and cost efficient cybersecurity defense mechanisms against malware epidemics in large-scale wireless networks. In particular, we believe that embracing a strategically selected small portion of the devices with state-of-the-art security mechanisms, denoted as spatial firewalls, should be sufficient to quarantine malware epidemics. Such firewalls are meant to monitor the wireless network, detect malware epidemics, and thwart their diffusion. Upon the detection of malware epidemics, the firewalls will initiate an on-demand software patching to cure infected devices. One of the main objectives of this proposal is investigate and assess the spatial firewalls countermeasure for malware epidemics. The proposal also aims at exploring and comparing different firewall selection strategies and on-demand patching solutions that efficiently quarantine malware epidemics and cure infected devices.