Kleptography trapdoor free cryptographic protocols

Context. Methods of known kleptography implementations are being investigated. The article focuses mostly on SETUP design of subliminal data leakage channels. Aim. Suggest approaches to develop SETUP resistant cryptosystems. Methods. The necessary conditions for SETUP implementation are building in entropy source (otherwise generated secret will be predictable). In this article, it\u27s considered subscriber whose protocol implementation is suspected to be modified by Developer (the malicious actor who is able to influence on cryptosystem implementation) to create subliminal leakage channel. The possible countermeasure is to prohibit usage own random sources for subscribers, enforce generate random values from public counters. %them to use external Trusted Random Number Generation service. Results. The formal model for basic SETUP scheme has been suggested. Approach to develop SETUP resistant protocols has been described. Two basic SETUP-resistance protocols (nonce generation protocol and Diffie-Hellman key agreement protocol) have been proposed

Non-Pattern-Based Anomaly Detection in Time-Series

Anomaly detection across critical infrastructures is not only a key step towards detecting threats but also gives early warnings of the likelihood of potential cyber-attacks, faults, or infrastructure failures. Owing to the heterogeneity and complexity of the cybersecurity field, several anomaly detection algorithms have been suggested in the recent past based on the literature; however, there still exists little or no research that points or focuses on Non-Pattern Anomaly Detection (NP-AD) in Time-Series at the time of writing this paper. Most of the existing anomaly detection approaches refer to the initial profiling, i.e., defining which behavior represented by time series is “normal”, whereas everything that does not meet the criteria of “normality” is set as “abnormal” or anomalous. Such a definition does not reflect the complexity and sophistication of anomaly nature. Under different conditions, the same behavior may or may not be anomalous. Therefore, the authors of this paper posit the need for NP-AD in Time-Series as a step toward showing the relevance of deviating or not conforming to expected behaviors. Non-Pattern (NP), in the context of this paper, illustrates non-conforming patterns or a technique of deviating with respect to some characteristics while dynamically adapting to changes. Based on the experiments that have been conducted in this paper, it has been observed that the likelihood of NP-AD in Time-Series is a significant approach based on the margins of data streams that have been used from the perspective of non-seasonal time series with outliers, the Numenta Anomaly Benchmark (NAB) dataset and the SIEM SPLUNK machine learning toolkit. It is the authors’ opinion that this approach provides a significant step toward predicting futuristic anomalies across diverse cyber, critical infrastructures, and other complex settings. © 2023 by the authors