01382nas a2200193   4500000000100000000000100001008004100002260003300043100001700076700002000093700001800113700002500131700002600156700002100182700002300203245004800226856008600274520082800360       2020                            d  cJune 2020aBudva, Montenegro1 aErol Gelenbe1 aPiotr Fröhlich1 aMateusz Nowak1 aStavros Papadopoulos1 aAikaterini Protogerou1 aAnastasis Drosou1 aDimitrios Tzovaras00aIoT Network Attack Detection and Mitigation  uhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9134241&isnumber=91340633 aCyberattacks on the Internet of Things (IoT) can cause major economic and physical damage, and disrupt production lines, manufacturing processes, supply chains, impact the physical safety of vehicles, and damage the health of human beings. Thus we describe and evaluate a distributed and robust attack detection and mitigation system for network environments where communicating decision agents use Graph Neural Networks to provide attack alerts. We also present an attack mitigation system that uses a Reinforcement Learning driven Software Defined Network to process the alerts generated by the attack detection system, together with Quality of Service measurements, so as to re-route sensitive traffic away from compromised network paths using. Experimental results illustrate both the detection and re-routing scheme.