A decade of research on patterns and architectures for IoT security

Security of the Internet of Things (IoT)-based Smart Systems involving sensors, actuators and distributed control loop is of paramount importance but very difficult to address. Security patterns consist of domain-independent time-proven security knowledge and expertise. How are they useful for developing secure IoT-based smart systems? Are there architectures that support IoT security? We aim to systematically review the research work published on patterns and architectures for IoT security (and privacy). Then, we want to provide an analysis on that research landscape to answer our research questions. We follow the well-known guidelines for conducting systematic literature reviews. From thousands of candidate papers initially found in our search process, we have systematically distinguished and analyzed thirty-six (36) papers that have been peer-reviewed and published around patterns and architectures for IoT security and privacy in the last decade (January 2010–December 2020). Our analysis shows that there is a rise in the number of publications tending to patterns and architectures for IoT security in the last three years. We have not seen any approach of applying systematically architectures and patterns together that can address security (and privacy) concerns not only at the architectural level, but also at the network or IoT devices level. We also explored how the research contributions in the primary studies handle the different issues from the OWASP Internet of Things (IoT) top ten vulnerabilities list. Finally, we discuss the current gaps in this research area and how to fill in the gaps for promoting the utilization of patterns for IoT security and privacy by design.

Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique

To protect the network, resources, and sensitive data, the intrusion detection system (IDS) has become a fundamental component of organizations that prevents cybercriminal activities. Several approaches have been introduced and implemented to thwart malicious activities so far. Due to the effectiveness of machine learning (ML) methods, the proposed approach applied several ML models for the intrusion detection system. In order to evaluate the performance of models, UNSW-NB 15 and Network TON_IoT datasets were used for offline analysis. Both datasets are comparatively newer than the NSL-KDD dataset to represent modern-day attacks. However, the performance analysis was carried out by training and testing the Decision Tree (DT), Gradient Boosting Tree (GBT), Multilayer Perceptron (MLP), AdaBoost, Long-Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) for the binary classification task. As the performance of IDS deteriorates with a high dimensional feature vector, an optimum set of features was selected through a Gini Impurity-based Weighted Random Forest (GIWRF) model as the embedded feature selection technique. This technique employed Gini impurity as the splitting criterion of trees and adjusted the weights for two different classes of the imbalanced data to make the learning algorithm understand the class distribution. Based upon the importance score, 20 features were selected from UNSW-NB 15 and 10 features from the Network TON_IoT dataset. The experimental result revealed that DT performed well with the feature selection technique than other trained models of this experiment. Moreover, the proposed GIWRF-DT outperformed other existing methods surveyed in the literature in terms of the F1 score.

Privacy preserving divisible double auction with a hybridized TEE-blockchain system

Double auction mechanisms have been designed to trade a variety of divisible resources (e.g., electricity, mobile data, and cloud resources) among distributed agents. In such divisible double auction, all the agents (both buyers and sellers) are expected to submit their bid profiles, and dynamically achieve the best responses. In practice, these agents may not trust each other without a market mediator. Fortunately, smart contract is extensively used to ensure digital agreement among mutually distrustful agents. The consensus protocol helps the smart contract execution on the blockchain to ensure strong integrity and availability. However, severe privacy risks would emerge in the divisible double auction since all the agents should disclose their sensitive data such as the bid profiles (i.e., bid amount and prices in different iterations) to other agents for resource allocation and such data are replicated on all the nodes in the network. Furthermore, the consensus requirements will bring a huge burden for the blockchain, which impacts the overall performance. To address these concerns, we propose a hybridized TEE-Blockchain system (system and auction mechanism co-design) to privately execute the divisible double auction. The designed hybridized system ensures privacy, honesty and high efficiency among distributed agents. The bid profiles are sealed for optimally allocating divisible resources while ensuring truthfulness with a Nash Equilibrium. Finally, we conduct experiments and empirical studies to validate the system and auction performance using two real-world applications.

LSGAN-AT: enhancing malware detector robustness against adversarial examples

Adversarial Malware Example (AME)-based adversarial training can effectively enhance the robustness of Machine Learning (ML)-based malware detectors against AME. AME quality is a key factor to the robustness enhancement. Generative Adversarial Network (GAN) is a kind of AME generation method, but the existing GAN-based AME generation methods have the issues of inadequate optimization, mode collapse and training instability. In this paper, we propose a novel approach (denote as LSGAN-AT) to enhance ML-based malware detector robustness against Adversarial Examples, which includes LSGAN module and AT module. LSGAN module can generate more effective and smoother AME by utilizing brand-new network structures and Least Square (LS) loss to optimize boundary samples. AT module makes adversarial training using AME generated by LSGAN to generate ML-based Robust Malware Detector (RMD). Extensive experiment results validate the better transferability of AME in terms of attacking 6 ML detectors and the RMD transferability in terms of resisting the MalGAN black-box attack. The results also verify the performance of the generated RMD in the recognition rate of AME.

Indexing structures for the PLS blockchain

This paper studies known indexing structures from a new point of view: minimisation of data exchange between an IoT device acting as a blockchain client and the blockchain server running a protocol suite that includes two Guy Fawkes protocols, PLS and SLVP. The PLS blockchain is not a cryptocurrency instrument; it is an immutable ledger offering guaranteed non-repudiation to low-power clients without use of public key crypto. The novelty of the situation is in the fact that every PLS client has to obtain a proof of absence in all blocks of the chain to which its counterparty does not contribute, and we show that it is possible without traversing the block’s Merkle tree. We obtain weight statistics of a leaf path on a sparse Merkle tree theoretically, as our ground case. Using the theory we quantify the communication cost of a client interacting with the blockchain. We show that large savings can be achieved by providing a bitmap index of the tree compressed using Tunstall’s method. We further show that even in the case of correlated access, as in two IoT devices posting messages for each other in consecutive blocks, it is possible to prevent compression degradation by re-randomising the IDs using a pseudorandom bijective function. We propose a low-cost function of this kind and evaluate its quality by simulation, using the avalanche criterion.

Transparency order versus confusion coefficient: a case study of NIST lightweight cryptography S-Boxes

Side-channel resistance is nowadays widely accepted as a crucial factor in deciding the security assurance level of cryptographic implementations. In most cases, non-linear components (e.g. S-Boxes) of cryptographic algorithms will be chosen as primary targets of side-channel attacks (SCAs). In order to measure side-channel resistance of S-Boxes, three theoretical metrics are proposed and they are reVisited transparency order (VTO), confusion coefficients variance (CCV), and minimum confusion coefficient (MCC), respectively. However, the practical effectiveness of these metrics remains still unclear. Taking the 4-bit and 8-bit S-Boxes used in NIST Lightweight Cryptography candidates as concrete examples, this paper takes a comprehensive study of the applicability of these metrics. First of all, we empirically investigate the relations among three metrics for targeted S-boxes, and find that CCV is almost linearly correlated with VTO, while MCC is inconsistent with the other two. Furthermore, in order to verify which metric is more effective in which scenarios, we perform simulated and practical experiments on nine 4-bit S-Boxes under the non-profiled attacks and profiled attacks, respectively. The experiments show that for quantifying side-channel resistance of S-Boxes under non-profiled attacks, VTO and CCV are more reliable while MCC fails. We also obtain an interesting observation that none of these three metrics is suitable for measuring the resistance of S-Boxes against profiled SCAs. Finally, we try to verify whether these metrics can be applied to compare the resistance of S-Boxes with different sizes. Unfortunately, all of them are invalid in this scenario.

Verifiable image revision from chameleon hashes

In a digital society, the rapid development of computer science and the Internet has greatly facilitated image applications. However, one of the public network also brings risks to both image tampering and privacy exposure. Image authentication is the most important approaches to verify image integrity and authenticity. However, it has been challenging for image authentication to address both issues of tampering detection and privacy protection. One aspect, image authentication requires image contents not be changed to detect tampering. The other, privacy protection needs to remove sensitive information from images, and as a result, the contents should be changed. In this paper, we propose a practical image authentication scheme constructed from chameleon hashes combined with ordinary digital signatures to make tradeoff between tampering detection and privacy protection. Our scheme allows legitimate users to modify contents of authenticated images with a privacy-aware purpose (for example, cover some sensitive areas with mosaics) according to specific rules and verify the authenticity without interaction with the original authenticator. The security of our scheme is guaranteed by the security of the underlying cryptographic primitives. Experiment results show that our scheme is efficient and practical. We believe that our work will facilitate image applications where both authentication and privacy protection are desirable.

Multidimensional linear cryptanalysis with key difference invariant bias for block ciphers

For block ciphers, Bogdanov et al. found that there are some linear approximations satisfying that their biases are deterministically invariant under key difference. This property is called key difference invariant bias. Based on this property, Bogdanov et al. proposed a related-key statistical distinguisher and turned it into key-recovery attacks on LBlock and TWINE-128. In this paper, we propose a new related-key model by combining multidimensional linear cryptanalysis with key difference invariant bias. The main theoretical advantage is that our new model does not depend on statistical independence of linear approximations. We demonstrate our cryptanalysis technique by performing key recovery attacks on LBlock and TWINE-128. By using the relations of the involved round keys to reduce the number of guessed subkey bits. Moreover, the partial-compression technique is used to reduce the time complexity. We can recover the master key of LBlock up to 25 rounds with about 260.4 distinct known plaintexts, 278.85 time complexity and 261 bytes of memory requirements. Our attack can recover the master key of TWINE-128 up to 28 rounds with about 261.5 distinct known plaintexts, 2126.15 time complexity and 261 bytes of memory requirements. The results are the currently best ones on cryptanalysis of LBlock and TWINE-128.

Position paper: GPT conjecture: understanding the trade-offs between granularity, performance and timeliness in control-flow integrity

Performance/security trade-off is widely noticed in CFI research, however, we observe that not every CFI scheme is subject to the trade-off. Motivated by the key observation, we ask three questions: ➊ does trade-off really exist in different CFI schemes? ➋ if trade-off do exist, how do previous works comply with it? ➌ how can it inspire future research? Although the three questions probably cannot be directly answered, they are inspiring. We find that a deeper understanding of the nature of the trade-off will help answer the three questions. Accordingly, we proposed the GPT conjecture to pinpoint the trade-off in designing CFI schemes, which says that at most two out of three properties (fine granularity, acceptable performance, and preventive protection) could be achieved.

Confidential machine learning on untrusted platforms: a survey

With the ever-growing data and the need for developing powerful machine learning models, data owners increasingly depend on various untrusted platforms (e.g., public clouds, edges, and machine learning service providers) for scalable processing or collaborative learning. Thus, sensitive data and models are in danger of unauthorized access, misuse, and privacy compromises. A relatively new body of research confidentially trains machine learning models on protected data to address these concerns. In this survey, we summarize notable studies in this emerging area of research. With a unified framework, we highlight the critical challenges and innovations in outsourcing machine learning confidentially. We focus on the cryptographic approaches for confidential machine learning (CML), primarily on model training, while also covering other directions such as perturbation-based approaches and CML in the hardware-assisted computing environment. The discussion will take a holistic way to consider a rich context of the related threat models, security assumptions, design principles, and associated trade-offs amongst data utility, cost, and confidentiality.