The ISC International Journal of Information Security

Abstract Internet of things (IoT) is an innovation in the world of technology. Continuous technological advancements based on the IoT cloud and booming wireless technology have revolutionized the living of human and remote health monitoring of patients is no exclusion. The Telecare Medicine Information Systems (TMIS) is a system between Home Health Care (HHC) Organizations and patients at home that collects, saves, manage and transmits the Electronic Medical Record (EMR) of patients. Therefore, security in remote medicine has always been a very big and serious challenge. Therefore, biometrics-based schemes play a crucial role in IoT, Wireless Sensor Networks (WSN), etc. Recently, Xiong et al. and Mehmood \textit{et al.} presented key exchange methods for healthcare applications that they claimed these schemes provide greater privacy. But unfortunately, we show that these schemes suffer from privacy issues and key compromise impersonation attack. To remove such restrictions, in this paper, a novel scheme (ECKCI) using Elliptic Curve Cryptography (ECC) with KCI resistance property was proposed. Furthermore, we demonstrate that the ECKCI not only overcomes problems such as key compromise impersonation attack in previous protocols, but also resists all specific attacks. Finally, a suitable equilibrium between the performance and security of ECKCI in comparisons with these recently proposed protocols was obtained. Also, the simulation results with the Scyther and ProVerif tools show that the ECKCI is safe in terms of security.

Abstract To enhance the accuracy of learning models‎, ‎it becomes imperative to train them on more extensive datasets‎. ‎Unfortunately‎, ‎access to such data is often restricted because data providers are hesitant to share their data due to privacy concerns‎. ‎Hence‎, ‎it is critical to develop obfuscation techniques that empower data providers to transform their datasets into new ones that ensure the desired level of privacy‎. ‎In this paper‎, ‎we present an approach where data providers utilize a neural network based on the autoencoder architecture to safeguard the sensitive components of their data while preserving the utility of the remaining parts‎. ‎More specifically‎, ‎within the autoencoder framework and after the encoding process‎, ‎a classifier is used to extract the private feature from the dataset‎. ‎This feature is then decorrelated from the other remaining features and subsequently subjected to noise‎. ‎The proposed method is flexible‎, ‎allowing data providers to adjust their desired level of privacy by changing the noise level‎. ‎Additionally‎, ‎our approach demonstrates superior performance in achieving the desired trade-off between utility and privacy compared to similar methods‎, ‎all while maintaining a simpler structure‎.‎‎

Abstract Designing authentication techniques suitable for wireless sensor networks (WSNs) with their dedicated consideration is critical due to the nature of public channel. In 2022, Liu et al. presented an authentication protocol which employs dynamic authentication credentials (DACs) and Intel software guard extensions (SGX) to guarantee security in WSNs, and it was shown that it is secure by formal and informal security analysis. In this paper, we show that it is not secure against desynchronization attack and offline guessing attack for long-term random numbers of users. In addition, it suffers from the known session-specific temporary information attack. Then, to address these vulnerabilities an improved authentication scheme using DAC and Intel SGX will be presented. It is shown that not only it is secure against aforementioned attacks with employing formal and informal analysis, but also it has a reasonable communication and computation overhead. It should be highlighted that the communication and computation overheads of our proposal are increased negligibly, but it provides more security features compared to the baseline protocol.

Abstract This article presents new methods and tools used in the field of text analysis to identify fake news in the media. The problem with the research is that, as a rule, to identify fakes, a training dataset is required, on which thematic fakes were tested. This is not always feasible and requires additional resources. To solve this problem, a comprehensive research methodology has been developed that covers most detection tools, even in the absence of an established database containing reliable and fake news. The study includes a combination of various algorithms combined into a single analytical structure, presented in the work in the form of pseudocode. The authors introduce the concept of an "emotional fake model" similar to individual emotions included in a broader emotional spectrum. The essence of the model is to evaluate fakes based on the structure of definitions of emotions formed in fakes, which differ from the original signals due to different weight coefficients. The innovation involves a two—stage identification of fakes - initially clusters of messages from the text corpus are identified, and then, based on text analysis tools, their linguistic features and emotional differences are revealed (based on a set of emotions POMS). In the context of creating fake news using neural networks, emotional coloring plays a crucial role, providing a permanent foundation that can serve as a cornerstone for identification.

Abstract Midori64 is a lightweight SPN block cipher introduced by Banik et al. at ASIACRYPT 2015 which operates on 64-bit states through 16 rounds using a 128-bit key. In the last decade, Midori64 has been exposed to several attacks intensely. In this paper, we provide the first boomerang attack on Midori64 in the literature, to the best of our knowledge. For this purpose, firstly we present a practical single key 7-round boomerang attack on Midori64 improving the mixture idea of Biryukov by a new technique which we call ``mixture pool", and then extend our attack up to 9 rounds with time complexity $2^{122.3}$, and memory and data complexity $2^{36}$. (The authors of Midori stated that they expect much smaller rounds than 8 rounds of Midori64 are secure against boomerang-type attacks.) We also emphasize that the mixture pool idea provides a kind of data-memory tradeoff and hence presents more usefulness for boomerang-type attacks.

Abstract Distributed Denial of Service (DDoS) attacks are of two kinds viz. high-rate DDoS (HRDDoS) attacks and low-rate DDoS (LRDDoS) attacks. A shrew attack is a LRDDoS attack that can prove to be more harmful than a HRDDoS attack since they are not easily noticeable and are stealthy. They cause TCP flows to attain near-zero throughput by sending attack pulses of very short bursts synchronized with the TCP retransmission timeout (RTO) value. Consequently, it compels the TCP packets to be dropped whenever it tries to retransmit again after the timeout. Thus, it may endanger the victim systems if not detected for a long time and reduce the overall quality of services without being noticed. In this paper, we perform the analysis of the network traffic based on a statistical approach where the deviation in the behavior of the flows is analyzed based on the packets sent during the normal and attack conditions. To do this, we determine the participation of a flow in congestion and its adherence to the legitimate TCP-compliant nature during attack conditions based on a priority determiner D. The shrew attack flows exhibit higher values of $D$ as they do not adhere to the TCP compliance and tend to contribute to more congestion to disrupt a server. This nature of attack flows enables us to filter them based on the values of $D$ and mitigate them by blocking these flows. The experimental results on various scenarios demonstrated high accuracy to substantiate the efficacy of the proposed method.