The notion of Information-Centric Networking (ICN) specifies a new communication model that emphasis on the content exchanged rather than the devices connected. ICN architectures like Content Centric Network (CCN) and Named Data Networking (NDN) have been proposed to shift from host-centric based to content-centric based communication along and provide benefits to users in addressing the challenges of traditional IP networks. It differs from host-centric standard Internet Protocol (IP) networking in of naming, routing, forwarding, and caching characteristics. Naming features used in NDN use global unique names provided by content-based security and encryption. It ensures content integrity and authenticity as part of its design. In this paper, we survey on security aspects of NDN/CCN, discussing three integrity attacks such as replay attacks, Man-in-The-Middle (MiTM) attacks, and content poisoning attacks with countermeasure against them. In addition, we highlight an open challenge and offer future research directions in the context of security.

Speech Emotion Recognition (SER) is a common aspect of human-computer interaction and has significant applications in fields such as healthcare, education, and elder care. Although researchers have made progress in speech emotion feature extraction and model identification, they have struggled to create an SER system with satisfactory recognition accuracy. To address this issue, we proposed a novel algorithm called F-Emotion to select speech emotion features and established a parallel deep learning model to recognize different types of emotions. We first extracted emotion features from speech and calculated the F-Emotion value for each feature. These values were then used to determine the combination of speech emotion features that was optimal in speech emotion recognition. Next, a parallel deep learning model was established with the speech emotion feature combination as input to train and test for each type of emotion. Finally, decision fusion was applied to the parallel output results to obtain an overall recognition result. These analyses were conducted on two datasets, RAVDESS and EMO-DB, with the accuracy of speech emotion recognition reaching 82.3% and 88.8%, respectively. The results demonstrate that the F-Emotion algorithm can effectively analyze the correspondence between speech emotion features and emotion types.The MFCC feature best describes emotions of Neutral, Happiness, Fear and Surprise, and Mel best describes emotions of Angry and Sadness.The parallel deep learning model mechanism can improve the accuracy of speech emotion recognition.

The proliferation of connected devices in the Internet of Things (IoT) presents a connectivity challenge. The future internet will require a paradigm shift in which content is evaluated on the basis of “What” it is rather than “Where” it originated. ICN’s goal is to provide the benefits of name-based content addressing in order to facilitate scalable content distribution, security, mobility, and trust. NDN is a new internet architecture that evolved from Content-Centric Networking (CCN). NDN is viewed as a solution to the IoT’s challenges, as well as a way to transcend the IP paradigm. With IoT systems that had a number of challenging characteristics to satisfy, including heterogeneous devices, resource constraints, and energy efficiency. Due to the fact that NDN native features deliver data via hierarchically structured names, it offer promising solutions for current research integrating NDN into IoT. The review discusses the significance of naming, its influence, and security factor. Additionally, research challenges in the areas of naming and security will be discussed. The primary objective of this review is to give a new facelift to a new integrating naming convention for NDN.

Speech reflects people’s mental state and using a microphone sensor is a potential method for human–computer interaction. Speech recognition using this sensor is conducive to the diagnosis of mental illnesses. The gender difference of speakers affects the process of speech emotion recognition based on specific acoustic features, resulting in the decline of emotion recognition accuracy. Therefore, we believe that the accuracy of speech emotion recognition can be effectively improved by selecting different features of speech for emotion recognition based on the speech representations of different genders. In this paper, we propose a speech emotion recognition method based on gender classification. First, we use MLP to classify the original speech by gender. Second, based on the different acoustic features of male and female speech, we analyze the influence weights of multiple speech emotion features in male and female speech, and establish the optimal feature sets for male and female emotion recognition, respectively. Finally, we train and test CNN and BiLSTM, respectively, by using the male and the female speech emotion feature sets. The results show that the proposed emotion recognition models have an advantage in terms of average recognition accuracy compared with gender-mixed recognition models.

Internet of Things (IoT) allows all entities such as computing devices, machines, objects, people, etc., to interact with each other through their Internet Protocol (IP) addresses without human intervention. Consequently, IP addresses are being exhausted rapidly. Named-Data Networking (NDN) tends to transit the conventional host-centric network into the data-centric network. However, routing attacks such as Interest Flooding Attack (IFA) is a primary security concern. To date, far too little attention has been paid to classify the IFA detection mechanisms and further identify the key points to design an efficient detection technique. This study aimed to conduct a comprehensive survey of state-of-the-art IFA detection mechanisms and analysed the algorithms used. In this paper, we summarize the different types of possible attack models with a specific name with a description in NDN, specifically the PIT-oriented routing attack. Besides, we classified these mechanisms into nine categories with its topology used, analogy metrics and attack focus. Finally, we have highlighted and provide recommendations in some critical pieces of architecture in detection mechanism design as future challenges to secure the IoT data from IFA in NDN.