The prevalence of internet usage leads to diverse internet traffic, which may contain information about various types of internet attacks. In recent years, many researchers have applied deep learning technology to intrusion detection systems and obtained fairly strong recognition results. However, most experiments have used old datasets, so they could not reflect the latest attack information. In this paper, a current state of the CSE-CIC-IDS2018 dataset and standard evaluation metrics has been employed to evaluate the proposed mechanism. After preprocessing the dataset, six models—deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), CNN + RNN and CNN + LSTM—were constructed to judge whether network traffic comprised a malicious attack. In addition, multi-classification experiments were conducted to sort traffic into benign traffic and six categories of malicious attacks: BruteForce, Denial-of-service (DoS), Web Attacks, Infiltration, Botnet, and Distributed denial-of-service (DDoS). Each model showed a high accuracy in various experiments, and their multi-class classification accuracy were above 98%. Compared with the intrusion detection system (IDS) of other papers, the proposed model effectively improves the detection performance. Moreover, the inference time for the combinations of CNN + RNN and CNN + LSTM is longer than that of the individual DNN, RNN and CNN. Therefore, the DNN, RNN and CNN are better than CNN + RNN and CNN + LSTM for considering the implementation of the algorithm in the IDS device.

Deployment of wireless sensor networks (WSNs) has drawn much attention in recent years. Given the limited energy for sensor nodes, it is critical to implement WSNs with energy efficiency designs. Sensing coverage in networks, on the other hand, may degrade gradually over time after WSNs are activated. For mission-critical applications, therefore, energy-efficient coverage control should be taken into consideration to support the quality of service (QoS) of WSNs. Usually, coverage-controlling strategies present some challenging problems: (1) resolving the conflicts while determining which nodes should be turned off to conserve energy; (2) designing an optimal wake-up scheme that avoids awakening more nodes than necessary. In this paper, we implement an energy-efficient coverage control in cluster-based WSNs using a Memetic Algorithm (MA)-based approach, entitled CoCMA, to resolve the challenging problems. The CoCMA contains two optimization strategies: a MA-based schedule for sensor nodes and a wake-up scheme, which are responsible to prolong the network lifetime while maintaining coverage preservation. The MA-based schedule is applied to a given WSN to avoid unnecessary energy consumption caused by the redundant nodes. During the network operation, the wake-up scheme awakens sleeping sensor nodes to recover coverage hole caused by dead nodes. The performance evaluation of the proposed CoCMA was conducted on a cluster-based WSN (CWSN) under either a random or a uniform deployment of sensor nodes. Simulation results show that the performance yielded by the combination of MA and wake-up scheme is better than that in some existing approaches. Furthermore, CoCMA is able to activate fewer sensor nodes to monitor the required sensing area. Source Code

In today’s society, many families do not have children due to various reasons. The reasons include the pressure brought by childcare, and that double-income families do not have time to raise children, especially novice parents who will retreat. For this reason, we made a chatbot to solve problems. Through the questionnaires collected by our study, we found that most novice parents use INFANBOT when their children cry, thus, we used a baby’s cry as an example here. When parents face a baby’s cry, they can tap the button “burst into tears!”. The chatbot will immediately tell parents how to solve the problem. The authors designed a chatbot system named INFANBOT that combined the concepts of infancy health education to alleviate parents’ troubles at work and parenting. It also reduced the anxiety of caregivers during the parenting process and gave them correct parenting knowledge. The INFANBOT is a real-time system that can provide real-time services to novice parents. Additionally, when the user is using INFANBOT, the system will record the problems encountered and invite the user to fill in a questionnaire at an appropriate time to improve the system. After a preliminary study, we found that INFANBOT can solve most of the problems encountered by users. Statistically, all respondents gave above 4.5 points in the Likert-type five-point scale. Therefore, most respondents felt that INFANBOT could solve their problems effectively and quickly. The INFANBOT system developed by this study is designed to meet the needs of users. The system design of INFANBOT established in this study met the needs of its users and can help users improve their parenting troubles. This study also has positive effects and contributions to society: 1. After using INFANBOT, users can effectively improve their knowledge of children’s health education. 2. After using INFANBOT, users feel recognition of the professionalism of the health care knowledge provided by the robot, which can effectively improve the user’s parenting problems. 3. Most of the users are satisfied with the positive results after using INFANBOT, so novice parents and parents who feel anxious about parenting can quickly search for common parenting problems on the LINE community software.

This paper reports on the development and subsequent use of a packet switching communication model for monitoring of power transmission line systems. Modern fault detection and fault location techniques for EHV/UHV transmission networks usually work based on the data measured by a phasor measurement unit (PMU). Digital cameras have also been widely utilized to monitor the physical status of power transmission lines on electricity pylons. PMU measures voltage and current phasors with synchronized time stamps, and then transmits the measured data to a monitoring center for analysis. The transmission of these data is required to be very stable. For the sake of operation speed and system security, the development of an enhanced communication infrastructure that guarantees the quality of service (QoS) for the essential measured data is a crucial issue. We have developed an adaptive routing algorithm for packet switching networks to guarantee the QoS of important power system communications and have conducted computer simulations to demonstrate the effectiveness of the proposed algorithm. The proposed algorithm can minimize transmission delay and reduce the number of redundant transmissions caused by loss of packets. Hence, the simulation results show the feasibility of packet switching networks on monitoring power systems.