Cyber-threat protection is one of the most challenging research branches of Internet-of-Things (iot). With the exponential increase of tiny connected devices, the battle between friend and foe intensifies. Unfortunately,iotdevices offer very limited security features, laying themselves wide open to new attacks, inhibiting the expected global adoption ofiottechnologies. Moreover, existing prevention and mitigation techniques and intrusion detection systems handle attack anomalies rather than the attack itself while using a significant amount of the network resources.rpl, the de-facto routing protocol foriot, proposes minimal security features that cannot handle internal attacks. Hence, in this paper, we proposesprinkler, which identifies the specificthingthat is under attack by an adversarial Man-in-The-Middle.sprinkleruses the multi-instance feature ofrplto identify the adversary. The proposed solution adheres to two basic principles: it only uses pre-existing standard routing protocols and does not rely on a centralized or trusted third-party node such as a certificate authority. All information must be gleaned by each node using only primitives that already exist in the underlying communication protocol, which excludes any training dataset. Simulations show thatsprinkleradds minimal maintenance and energy expenditure while pinpointing deterministically the attacker in the network. In particular,sprinklerhas a message delivery rate and detection rate of 100%.

With the emergence of new and exciting wireless technologies and capabilities, Unmanned Aerial Vehicles (UAVs) and the services they allow, stand to be a major influencer in our daily lives. Unfortunately, they are also prone to a plethora of security issues. Existing studies propose both prevention and identification schemes for various routing attacks. They do not, however, preclude future malicious attempts. Hence, in this work we identify the specific UAV that is compromising the network, with the specific purpose of flushing it out. The proposed solution combines secret sharing and cheating identification schemes with multi-path routing protocols, to deterministically pinpoint the compromised node that is cheating the UAV flock. It assures a quiet identification of the adversary creating new opportunities for its attack, even when facing a sophisticated adversary that selectively modifies data messages or re-routes them in within the network. We took special care to allow for applicability in existing networks by adhering to two basic principles: only using pre-existing standard routing protocols and not relying on a centralized or trusted third party node such as a base station. All information must be gleaned by each node using only primitives which already exist in the underlying communication protocols. We provide a rigorous mathematical proof of the cost bounds, and run simulations to prove feasibility. Moreover, the simulations show a 100% detection rate and message delivery rate. The communication overhead varies, on average, between$0.4\cdot 10^{6}-0.8\cdot 10^{6}$bytes, depending on various parameters such as the network size and the reception rate of network nodes. The time required varies between 0.2–0.4 seconds, depending mainly on the network size.

Software Defined Networking decouples the control and data planes. The response time and quality of service of the controllers is a key aspect in implementing the Software Defined Networking paradigm. A wireless Software Defined Networking control plane is even more challenging. Many radio communication problems arise in modeling the wireless east west bound and southbound interfaces. Wireless networks feature many unique components and metrics that often do not exist in wired networks: separate control transport may intensify on the latency within the wireless data plain with additional interference. Obviously, Wi-Fi based control plane has its implications, e.g., higher packet loss and hidden or exposed terminals. Moreover, wireless links can be operated in a number of different wireless characteristics, e.g., transmission rates and power settings. In this paper we define and solve the known Controllers Placement Problem, but for a Wi-Fi based control plane: the Wireless Control Placement Problem. We define the metrics for an effective wireless controllers placement and propose a multi-objective optimization for the Wireless Controller Placement Problem. Then we evaluate the influence of the variant metrics on the number of controllers and their locations.