Non-cryptographic Solutions Research Articles

Security of federated learning with IoT systems: Issues, limitations, challenges, and solutions

Federated Learning (FL, or Collaborative Learning (CL)) has surely gained a reputation for not only building Machine Learning (ML) models that rely on distributed datasets, but also for starting to play a key role in security and privacy solutions to protect sensitive data and information from a variety of ML-related attacks. This made it an ideal choice for emerging networks such as Internet of Things (IoT) systems, especially with its state-of-the-art algorithms that focus on their practical use over IoT networks, despite the presence of resource-constrained devices. However, the heterogeneous nature of the current devices and models in complex IoT networks has seriously hindered the FL training process's ability to perform well. Thus, rendering it almost unsuitable for direct deployment over IoT networks despite ongoing efforts to tackle this issue and overcome this challenging obstacle. As a result, the main characteristics of FL in the IoT from both security and privacy aspects are presented in this study. We broaden our research to investigate and analyze cutting-edge FL algorithms, models, and protocols, with a focus on their efficacy and practical application across IoT networks and systems alike. This is followed by a comparative analysis of the recently available protection solutions for FL that can be based on cryptographic and non-cryptographic solutions over heterogeneous, dynamic IoT networks. Moreover, the proposed work provides a list of suggestions and recommendations that can be applied to enhance the effectiveness of the adoption of FL and to achieve higher robustness against attacks, especially in heterogeneous dynamic IoT networks and in the presence of resource-constrained devices.

A Survey on Privacy Vulnerabilities in Permissionless Blockchains

Blockchain decentralization not only ensures transparency of transactions to eliminate need of trusting third party, but also makes the transactions of the network to be publicly accessible to all the participating peers in the network. As a result, data anonymity and confidentiality are compromised making several business enterprises and industrialists hesitant to adopt the technology. Although research community has proposed various privacy-preserving solutions for blockchain, however, they still lack in efficiency resulting in distrust of industries in opting for the technology. This study is conducted for contributing to the existing body of knowledge corresponding to privacy in blockchains. The fundamental goal of this study is to delve into privacy vulnerabilities of the blockchain network in a permissionless setting by identifying non-trivial roots of factors causing privacy breach in blockchain and presenting limitation of existing privacy preserving mechanisms. Studies with superficial comparison of privacy preserving techniques are available in literature but a detailed and in-depth analysis of their limitations and causes of privacy breach in blockchain is yet not done. Therefore, in this paper we first present comprehensive analysis of various privacy breaching factors of the blockchain networks. Next, we discuss existing cryptographic and non-cryptographic solutions in literature. We found out that these existing privacy preserving mechanisms have their own set of limitations and hence are inefficient at current point of time. The existing privacy preserving mechanisms need further consideration of the research community before they’re widely adopted and benchmarked. Therefore, in the end, we identified some future directions that need to be addressed to model an efficient privacy preserving mechanism for wider adoption of the blockchain technology.

Embedded Device Forensics and Security

While the increasing digitalization of our society and amalgamation of embedded devices into the ever-increasing facets of our daily life (e.g., in smart and intelligent vehicles, smart cities and smart nations, and critical infrastructure sectors) have resulted in improved productivity and quality of life, the trend has also resulted in a trend of increasing frequency and sophistication of cyber exploitation and cyber threats. Hence, there is a need for coordinated efforts from the research community to address resulting concerns using both cryptographic and non-cryptographic solutions, such as those presented in this special section.

R2NA: Received Signal Strength (RSS) Ratio-Based Node Authentication for Body Area Network

The body area network (BAN) is an emerging branch of wireless sensor networks for personalized applications. The services in BAN usually have a high requirement on security, especially for the medical diagnosis. One of the fundamental directions to ensure security in BAN is how to provide node authentication. Traditional research using cryptography relies on prior secrets shared among nodes, which leads to high resource cost. In addition, most existing non-cryptographic solutions exploit out-of-band (OOB) channels, but they need the help of additional hardware support or significant modifications to the system software. To avoid the above problems, this paper presents a proximity-based node authentication scheme, which only uses wireless modules equipped on sensors. With only one sensor and one control unit (CU) in BAN, we could detect a unique physical layer characteristic, namely, the difference between the received signal strength (RSS) measured on different devices in BAN. Through the above-mentioned particular difference, we can tell whether the sender is close enough to be legitimate. We validate our scheme through both theoretical analysis and experiments, which are conducted on the real Shimmer nodes. The results demonstrate that our proposed scheme has a good security performance.

Secure Video Transmission Against Black Hole Attack in MANETs

Mobile Adhoc Networks (MANETs) are open to a wide range of attacks due to their unique characteristics like dynamic topology, shared medium, absence of infrastructure, and resource constraints. Data packets sent by a source node may reach destination through a number of intermediate nodes. In the absence of security mechanism, it is easy for an intermediate node to intercept or modify the messages, thus attacking the normal operation of MANET. One such attack is Black hole attack, in which, a malicious node called Black hole node attracts all the traffic of the network towards itself, and discards all the packets without forwarding them to the intended recipients. This paper evaluates the performance of Adhoc on-demand Distance Vector (AODV) and its multi-path variant Adhoc On-demand Multi-path Distance Vector (AOMDV) routing protocols under black hole attack. Non-cryptographic solutions Secure Blackhole AODV (SBAODV) and Secure Blackhole AOMDV (SBAOMDV) have been proposed to mitigate the effect of black hole attack. Through NS-2 simulations, the performance of the proposed protocols with video streaming is analyzed. The results show that the proposed solutions provide better performance than the conventional AODV and AOMDV.

Discovering Network Topology in the Presence of Byzantine Faults

We pose and study the problem of Byzantine-robust topology discovery in an arbitrary asynchronous network. The problem is an abstraction of fault-tolerant routing. We formally state the weak and strong versions of the problem. The weak version requires that either each node discovers the topology of the network or at least one node detects the presence of a faulty node. The strong version requires that each node discovers the topology regardless of faults. We focus on non-cryptographic solutions to these problems. We explore their bounds. We prove that the weak topology discovery problem is solvable only if the connectivity of the network exceeds the number of faults in the system. Similarly, we show that the strong version of the problem is solvable only if the network connectivity is more than twice the number of faults. We present solutions to both versions of the problem. The presented algorithms match the established graph connectivity bounds. The algorithms do not require the individual nodes to know either the diameter or the size of the network. The message complexity of both programs is low polynomial with respect to the network size. We describe how our solutions can be extended to add the property of termination, handle topology changes, and perform neighborhood discovery.