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Future Proofing IoT Embedded Platforms for Cryptographic Primitives Support
... [6] investigated address space layout randomization (ASLR) and evaluated its performance using the proposed Hierarchical Attack Representation Model (HARM). Several lightweight MTD techniques are also proposed by randomly choosing different types of cryptographic primitives [7] or both cryptosystems and firmwares [8] for wireless sensor networks. [9] proposed a dynamically changing IPv6 address assignment approach over the IoT devices using Low-Powered Wireless Personal Area Networks (LPWPANs) protocol to defend against various network attacks. ...
Resource constrained Internet-of-Things (IoT) devices are highly likely to be compromised by attackers because strong security protections may not be suitable to be deployed. This requires an alternative approach to protect vulnerable components in IoT networks. In this paper, we propose an integrated defense technique to achieve intrusion prevention by leveraging cyberdeception (i.e., a decoy system) and moving target defense (i.e., network topology shuffling). We verify the effectiveness and efficiency of our proposed technique analytically based on a graphical security model in a software defined networking (SDN)-based IoT network. We develop four strategies (i.e., fixed/random and adaptive/hybrid) to address "when" to perform network topology shuffling and three strategies (i.e., genetic algorithm/decoy attack path-based optimization/random) to address "how" to perform network topology shuffling on a decoy-populated IoT network, and analyze which strategy can best achieve a system goal such as prolonging the system lifetime, maximizing deception effectiveness, maximizing service availability, or minimizing defense cost. Our results demonstrate that a software defined IoT network running our intrusion prevention technique at the optimal parameter setting prolongs system lifetime, increases attack complexity of compromising critical nodes, and maintains superior service availability compared with a counterpart IoT network without running our intrusion prevention technique. Further, when given a single goal or a multi-objective goal (e.g., maximizing the system lifetime and service availability while minimizing the defense cost) as input, the best combination of "how" and "how" strategies is identified for executing our proposed technique under which the specified goal can be best achieved.
... Approaches to detect attacks in Modbus data with the help of signatures is presented by Gao and Morris [12]. The security of future industrial applications with the integration of the IIoT is addressed by Plaga et al. [28,29]. Figure 1: Relation of Sub-Processes ...
Due to the fourth industrial revolution, industrial applications make use of the progress in communication and embedded devices. This allows industrial users to increase efficiency and manageability while reducing cost and effort. Furthermore, the fourth industrial revolution, creating the so-called Industry 4.0, opens a variety of novel use and business cases in the industrial environment. However, this progress comes at the cost of an enlarged attack surface of industrial companies. Operational networks that have previously been phyiscally separated from public networks are now connected in order to make use of new communication capabilites. This motivates the need for industrial intrusion detection solutions that are compatible to the long-term operation machines in industry as well as the heterogeneous and fast-changing networks. In this work, process data is analysed. The data is created and monitored on real-world hardware. After a set up phase, attacks are introduced into the systems that influence the process behaviour. A time series-based anomaly detection approach, the Matrix Profiles, are adapted to the specific needs and applied to the intrusion detection. The results indicate an applicability of these methods to detect attacks in the process behaviour. Furthermore, they are easily integrated into existing process environments. Additionally, one-class classifiers One-Class Support Vector Machines and Isolation Forest are applied to the data without a notion of timing. While Matrix Profiles perform well in terms of creating and visualising results, the one-class classifiers perform poorly.
... Approaches to detect attacks in Modbus data with the help of signatures is presented by Gao and Morris [12]. The security of future industrial applications with the integration of the IIoT is addressed by Plaga et al. [28,29]. Figure 1: Relation of Sub-Processes ...
Due to the fourth industrial revolution, industrial applications make use of the progress in communication and embedded devices. This allows industrial users to increase efficiency and manageability while reducing cost and effort. Furthermore, the fourth industrial revolution, creating the so-called Industry 4.0, opens a variety of novel use and business cases in the industrial environment. However, this progress comes at the cost of an enlarged attack surface of industrial companies. Operational networks that have previously been phyiscally separated from public networks are now connected in order to make use of new communication capabilites. This motivates the need for industrial intrusion detection solutions that are compatible to the long-term operation machines in industry as well as the heterogeneous and fast-changing networks. In this work, process data is analysed. The data is created and monitored on real-world hardware. After a set up phase, attacks are introduced into the systems that influence the process behaviour. A time series-based anomaly detection approach, the Matrix Profiles, are adapted to the specific needs and applied to the intrusion detection. The results indicate an applicability of these methods to detect attacks in the process behaviour. Furthermore, they are easily integrated into existing process environments. Additionally, one-class classifiers One-Class Support Vector Machines and Isolation Forest are applied to the data without a notion of timing. While Matrix Profiles perform well in terms of creating and visualising results, the one-class classifiers perform poorly.
SHA-1 is a widely used 1995 NIST cryptographic hash function standard that was officially deprecated by NIST in 2011 due to fundamental security weaknesses demonstrated in various analyses and theoretical attacks.
20 Years of SSL/TLS Research
- C Meyer
C. Meyer, "20 Years of SSL/TLS Research", PhD thesis,
2014. [Online]. Available: http:// www-brs.ub.ruhr-unibochum.de/netahtml/HSS/Diss/MeyerChristopher/diss.pdf
(visited on 06/04/2018).
Another New AES Attack
- Bruce Schneier
Bruce Schneier, Another New AES Attack. [Online]. Available:
https://www.schneier.com/blog/archives/2009/07/another_
new_aes.html (visited on 06/04/2018).
The mbed TLS Project Website
- Arm Limited
ARM Limited, The mbed TLS Project Website. [Online].