Cryptographic network protocols play a crucial role in enabling secure data exchange over insecure media in modern network environments. However, even minor vulnerabilities can make protocols an easy target for cyber attackers. Therefore, it is essential to investigate the threats and vulnerabilities stemming from the cryptographic network protocols. Furthermore, it is necessary to comprehensively investigate the weaknesses of network protocols that use cryptographic primitives to inform users and developers about potential attack points. This comprehensive survey examines the relationship between encryption schemes and network protocols and presents an in-depth review of associated threats and vulnerabilities. Given that most cryptographic protocols operate in the Transport and Application layers of the Transmission Control Protocol/Internet Protocol (TCP/IP) protocol stack, our investigation primarily centers around encryption algorithms used by representative and notable cryptographic network protocols such as Transport Layer Security (TLS) and Secure Shell (SSH). Furthermore, we delve into the attackers’ methods to exploit the already identified and existing vulnerabilities, seeking to understand the mechanisms employed to compromise these protocols. Through this survey, we aim to provide the readership with an in-depth understanding of the existing and new vulnerabilities associated with modern cryptographic protocols and provide valuable insights into securing them effectively. We also discuss the existing challenges and future research directions in this domain.

Background: The automatic interpretation of electrocardiography (ECG) data can provide continuous analysis of heart activity, allowing the effective use of wireless devices such as the Holter monitor. Materials and Methods: We propose an intelligent heartbeat monitoring system to detect the possibility of arrhythmia in real time. We detected heartbeats and extracted features such as the QRS complex and P wave from ECG signals using the Pan–Tompkins algorithm, and the heartbeats were then classified into 16 types using a decision tree. Results: We tested the sensitivity, specificity, and accuracy of our system against data from the MIT-BIH Arrhythmia Database. Our system achieved an average accuracy of 97% in heartbeat detection and an average heartbeat classification accuracy of above 96%, which is comparable with the best competing schemes. Conclusions: This work provides a guide to the systematic design of an intelligent classification system for decision support in Holter ECG monitoring.

Respiratory sounds are expressed as nonlinear and nonstationary signals, whose unpredictability makes it difficult to extract significant features for classification. Static cepstral coefficients such asMel-frequency cepstral coefficients (MFCCs), have been used for classification of lung sound signals. However, they are modeled in high-dimensional hyperspectral space, and also lose temporal dependency information. Therefore, we propose shifted $$\delta $$δ -cepstral coefficients in lower-subspace (SDC-L) as a novel feature for lung sound classification. It preserves temporal dependency information of multiple frames nearby same to original SDC, and improves feature extraction by reducing the hyperspectral dimension. We modified EMD algorithm by adding a stopping rule to objectively select a finite number of intrinsic mode functions (IMFs). The performances of SDC-L were evaluated with three machine learning techniques (support vector machine (SVM), k-nearest neighbor (k-NN) and random forest (RF)) and two deep learning algorithms (multilayer perceptron (MLP) and convolutional neural network (cNN)) and one hybrid deep learning algorithm combining cNN with long short term memory (LSTM) in terms of accuracy, precision, recall and F1-score. We found that the first 2 IMFs were enough to construct our feature. SVM, MLP and a hybrid deep learning algorithm (cNN plus LSTM) outperformed with SDC-L, and the other classifiers achieved equivalent results with all features. Our findings show that SDC-L is a promising feature for the classification of lung sound signals.

Long-term electrocardiogram (ECG) monitoring, as a representative application of cyber-physical systems, facilitates the early detection of arrhythmia. A considerable number of previous studies has explored monitoring techniques and the automated analysis of sensing data. However, ensuring patient privacy or confidentiality has not been a primary concern in ECG monitoring. First, we propose an intelligent heart monitoring system, which involves a patient-worn ECG sensor (e.g., a smartphone) and a remote monitoring station, as well as a decision support server that interconnects these components. The decision support server analyzes the heart activity, using the Pan–Tompkins algorithm to detect heartbeats and a decision tree to classify them. Our system protects sensing data and user privacy, which is an essential attribute of dependability, by adopting signal scrambling and anonymous identity schemes. We also employ a public key cryptosystem to enable secure communication between the entities. Simulations using data from the MIT-BIH arrhythmia database demonstrate that our system achieves a 95.74% success rate in heartbeat detection and almost a 96.63% accuracy in heartbeat classification, while successfully preserving privacy and securing communications among the involved entities.