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The proliferation of IoT comes with many challenges, among which security is one of the most serious issues. Since most IoT devices are designed based on bare-metal devices with low performance, low power, and small form factor, an attacker can easily perform firmware extraction and analysis. In addition, code reuse attacks, such as Return Oriented Programming (ROP) attacks, are possible based on the analyzed firmware information. Since most code reuse attacks are performed through a combination of gadgets that include return instructions, preventing an attacker from analyzing return instructions can be a fundamental solution for these attacks. To prevent code reuse attacks on bare-metal based IoT devices, this paper proposes Return Instruction Obfuscation (RIO). The proposed scheme encrypts all return instructions in the firmware and instruments the modules necessary to decrypt and execute the encrypted return instructions using a Low Level Virtual Machine (LLVM). Since all return instructions in the firmware are encrypted, the proposed scheme can prevent attackers from performing firmware analysis and gadget collection. The proposed scheme was implemented and evaluated on Nuvoton’s NuMaker-PFM-M2351 development board with an ARM Cortex-M23 based SoC.

A cognitive radio (CR) is a promising technology to solve the emerging spectrum crisis, especially for applications where thousands of wireless sensor nodes are deployed. Since continuous spectrum sensing will greatly reduce the lifetime of a network composed of energy-restricted CR nodes, an accurate method for predicting spectrum occupancy is necessary to improve energy efficiency. This paper proposes a hidden Markov model (HMM)-based cooperative spectrum sensing (CSS) that predicts the status of a network environment. The traditional prediction algorithms for cooperative spectrum sensing assume that all CR nodes have the same network environment. However, the channel availability of various CR nodes can be quite different, and thus the traditional algorithms will lead to low prediction accuracy in a complex radio environment. The proposed methods learn the historical spectrum sensing results and help the network to make an energy-efficient spectrum sensing decision. More specifically, the hidden state of HMM is set to different areas, where primary users (PUs) perform different activities. A Baum-Welch (BW) algorithm is employed to estimate the parameters of the HMM based on the past spectrum sensing results, and then the parameters are fed to a forward algorithm for the predicting of PUs' activity. Based on the prediction, secondary users (SUs) are classified into either ”interfered by PU” or ”not interfered by PU. ” The nodes selected as ”interfered by PU” will not perform spectrum sensing to reduce unnecessary energy consumption. The performance of the proposed method is evaluated using the simulations under different traffic conditions. The simulation results show that, compared with the conventional HMM-based methods, the effectiveness of the proposed algorithm in energy efficiency and spectrum utilization improved by about 13% and 15%, respectively.