Biblio

Networked embedded systems (which include IoT, CPS, etc.) are vulnerable. Even though we know how to secure these systems, their heterogeneity and the heterogeneity of security policies remains a major problem. Designers face ever more sophisticated attacks while they are not always security experts and have to get a trade-off on design criteria. We propose in this paper the CLASA architecture (Cross-Layer Agent Security Architecture), a generic, integrated, inter-operable, decentralized and modular architecture which relies on cross-layering.

Implementation of Internet-of-Things (IoT) can take place in many applications, for instance, automobiles, and industrial automation. We generally view the role of an Electronic Control Unit (ECU) or industrial network node that is occupied and interconnected in many different configurations in a vehicle or a factory. This condition may raise the occurrence of problems related to security issues, such as unauthorized access to data or components in ECUs or industrial network nodes. In this paper, we propose a hardware (HW)/software (SW) framework having integrated security extensions complemented with various security-related features that later can be implemented directly from the framework to All Programmable Multiprocessor System-on-Chip (AP MPSoC)-based ECUs. The framework is a software-defined one that can be configured or reconfigured in a higher level of abstraction language, including High-Level Synthesis (HLS), and the output of the framework is hardware configuration in multiprocessor or reconfigurable components in the FPGA. The system comprises high-level requirements, covert and side-channel estimation, cryptography, optimization, artificial intelligence, and partial reconfiguration. With this framework, we may reduce the design & development time, and provide significant flexibility to configure/reconfigure our framework and its target platform equipped with security extensions.

Due to their proven efficiency, machine-learning systems are deployed in a wide range of complex real-life problems. More specifically, Spiking Neural Networks (SNNs) emerged as a promising solution to the accuracy, resource-utilization, and energy-efficiency challenges in machine-learning systems. While these systems are going mainstream, they have inherent security and reliability issues. In this paper, we propose NeuroAttack, a cross-layer attack that threatens the SNNs integrity by exploiting low-level reliability issues through a high-level attack. Particularly, we trigger a fault-injection based sneaky hardware backdoor through a carefully crafted adversarial input noise. Our results on Deep Neural Networks (DNNs) and SNNs show a serious integrity threat to state-of-the art machine-learning techniques.

The existing research on the Internet of Things(IoT) security mainly focuses on attack and defense on a single protocol layer. Increasing and ubiquitous use of loT also makes it vulnerable to many attacks. An attacker try to performs the intelligent, brutal and stealthy attack that can reduce the risk of being detected. In these kinds of attacks, the attackers not only restrict themselves to a single layer of protocol stack but they also try to decrease the network performance and throughput by a simultaneous and coordinated attack on different layers. A new class of attacks, termed as cross-layer attack became prominent due to lack of interaction between MAC, routing and upper layers. These attacks achieve the better effect with reduced cost. Research has been done on cross-layer attacks in other domains like Cognitive Radio Network(CRN), Wireless Sensor Networks(WSN) and ad-hoc networks. However, our proposed scheme of cross-layer attack in IoT is the first paper to the best of our knowledge. In this paper, we have proposed Rank Manipulation and Drop Delay(RMDD) cross-layer attack in loT, we have investigated how small intensity attack on Routing protocol for low power lossy networks (RPL) degrades the overall application throughput. We have exploited the Rank system of the RPL protocol to implement the attacks. Rank is given to each node in the graph, and it shows its position in the network. If the rank could be manipulated in some manner, then the network topology can be modified. Simulation results demonstrate that the proposed attacks degrade network performance very much in terms of the throughput, latency, and connectivity.

Wireless communications in Cyber-Physical Systems (CPS) are vulnerable to many adversarial attacks such as eavesdropping. To secure the communications, secret session keys need to be established between wireless devices. In existing symmetric key establishment protocols, it is assumed that devices are pre-loaded with secrets. In the CPS, however, wireless devices are produced by different companies. It is not practical to assume that the devices are pre-loaded with certain secrets when they leave companies. As a consequence, existing symmetric key establishment protocols cannot be directly implemented in the CPS. Motivated by these observations, this paper presents a cross-layer key establishment model for heterogeneous wireless devices in the CPS. Specifically, by implementing our model, wireless devices extract master keys (shared with the system authority) at the physical layer using ambient wireless signals. Then, the system authority distributes secrets for devices (according to an existing symmetric key establishment protocol) by making use of the extracted master keys. Completing these operations, wireless devices can establish secret session keys at higher layers by calling the employed key establishment protocol. Additionally, we prove the security of the proposed model. We analyse the performance of the new model by implementing it and converting existing symmetric key establishment protocols into cross-layer key establishment protocols.

Discrete fractional Fourier transform (DFRFT) is a generalization of discrete Fourier transform. There are a number of DFRFT proposals, which are useful for various signal processing applications. This paper investigates practical solutions toward the construction of unconditionally secure communication systems based on DFRFT via cross-layer approach. By introducing a distort signal parameter, the sender randomly flip-flops between the distort signal parameter and the general signal parameter to confuse the attacker. The advantages of the legitimate partners are guaranteed. We extend the advantages between legitimate partners via developing novel security codes on top of the proposed cross-layer DFRFT security communication model, aiming to achieve an error-free legitimate channel while preventing the eavesdropper from any useful information. Thus, a cross-layer strong mobile communication secure model is built.