Abstract: A recent trend for many malicious actors, such as: (1) terrorists in Iraq and Syria, (2) lone wolf domestic terrorists, (3) drug cartels, or (4) espionage-minded corporations, has been to use commercialoff- the-shelf (COTS) small unmanned aerial systems (sUAS) (i.e., drones) that can circumvent ground-based defenses to attack or spy on targets, to transport contraband, or to steal information. Because of the low cost of COTS sUAS and the prior success of these uses, this trend is increasing at an alarming rate, leading to the need to counter the malicious usage of sUAS (i.e., rogue sUAS). Researchers, the armed forces, and technologists have all proposed disparate solutions to this problem. There are no comprehensive and compact solutions capable of effectively tracking, identifying, and actively neutralizing the threats associated with rogue sUAS. Thus, we have developed a mobile cyber solution, using rigorous penetration testing across the top sUAS COTS vendors. Based on the market share of these top vendors, our approach is applicable to approximately 90% of all COTS sUAS.We demonstrate that hard-topatch vulnerabilities (i.e., vulnerabilities that exist across all the top vendors of sUAS) can be used as back-doors to counter the threat of rouge sUAS. Our solution can be launched from a standard laptop or Android mobile device with an external antenna, and is capable of tracking, identifying, and disrupting all Parrot and 3DR sUAS, as well as almost all DJI sUAS (i.e., renders them incapable of video flight) within a 300-meter radius.

Abstract: With the advent of the Internet of Things (IoT), wireless sensor and actuator networks, subsequently referred to as IoT networks (IoTNs), are proliferating at an unprecedented rate in several newfound areas such as smart cities, health care, and transportation, and consequently, securing them is of paramount importance. In this paper, we present several useful insights from an exploratory study of the impacts of network layer attacks on IoTNs. We envision that these insights will guide the design of future frameworks to defend against network layer attacks. We also present a preliminary such framework and demonstrate its effectiveness in detecting network layer attacks through experiments on a real IoTN test-bed.

Abstract: The Internet of Things (IoT) and Cyber-Physical Systems (CPS) technologies have increased the complexity of systems and also exposed them to additional vulnerabilities. Attack-graphs are graphical representations that provide a complete view of how interdependencies among atomic vulnerabilities may be exploited by an adversary to stitch together an attack that can compromise the system. Their manual construction is tedious, error-prone, and time consuming. This paper presents a model-based Automated Attack- Graph Generator and Visualizer (A2G2V). Given the networked system description (its components, connectivity, services it supports, their vulnerabilities and protections), the attack graph enlists set of all possible sequences in which atomic-level vulnerabilities can be exploited to compromise a certain system-level security. The proposed A2G2V tool extends an existing formal methods tool (a model-checker) by integrating with it an architecture description tool, our own code (for parsing counterexamples, encoding those for specification relaxation, iterating till all attack sequences are revealed), and also a graph visualization tool.