چكيده به لاتين
The Internet of Things (IoT) is increasingly receiving the attention from industry and academia these days. The number of connected devices, will reach 30 billion by 2030 by a reputable prediction. Due to the limitation in the processing resources, heterogeneity, and the energy limitation of the Things, as well as the lack of a single standard for implementing the security mechanisms, the IoT has become one of the major cybersecurity fields. Security Resource Allocation (SRA) problem in the IoT networks refers to the placement of the security resources in the IoT infrastructure (e.g, nodes, cluster heads, or gateways). To solve this problem, it is mandatory to take into account the dynamic nature of the communication environments and the uncertainty for the actions of the attackers. In the traditional approaches for solving the SRA, the attacker works over based on his assumptions about the system conditions. Meanwhile the defender collects the information of the system with prior knowledge of the attacker's behavior and the targeted nodes. In this dissertation, unlike the mentioned traditional approaches, a realistic approach has been adopted for the Dynamic Security Resources Allocation in the IoT to battle with attackers with unknown behavior. In the stated problem, due to the fact that there is a need to make a decision regarding the deployment of several security resources during the learning periods, the state space of the strategies is expressed in the combinatorial form. Also, the SRAIoT problem is defined in the context of a combinatorial-adversarial multi-armed bandit problem. Since switching in the security resources has a high cost, in real scenarios, this cost is included in the utility function of the problem. Thus the proposed framework considers both the cost of switching and the earned reward. The simulation results show the proper performance of the proposed algorithms in various settings of the attacker, such as fixed, uniform, normal, and adaptive (intelligent) compared to the based combinatorial algorithm. For example, the proposed algorithms have obtained 1.3 to 2.5 times more cumulative utility than the based algorithm. Also, the simulation results show the faster convergence of the weak regret criterion of the proposed algorithms compared to the base algorithm. In addition, in this dissertation, in order to simulate the Internet of Things network in a realistic context, the attack scenario has been simulated using the Cooja simulator. The obtained results indicate that the proposed solution has improved by 15.8% in terms of packet delivery rate (PDR) compared to the basic method, on average in all tests.
