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The Internet of Things (IoT) has seen expeditious growth in recent times with 7 billion connected devices in 2020, thus leading to the vital importance of real-time monitoring of IoT devices. Through this paper, we demonstrate the idea of building a cloud-native application to monitor smart home devices. The application intends to provide valuable performance metrics from the perspective of end-users and react to anomalies in real-time. In this demo paper, we conduct the demonstration using Autoencoder (an unsupervised technique) based Deep Neural Networks (DNNs) to learn the normal operating conditions of power consumption of smart devices. When an anomaly is detected, the DNNs take proactive action and send appropriate commands back to the device. In addition, the users are provided with a real-time graphical representation of power consumption. This will help to save electricity on a domestic as well as industrial level. Finally, we discuss the future prospects of monitoring IoT devices.

Cyber-security is concerned with protecting information, a vital asset in today’s world. The volume of data that is generated can be usefully analyzed when cyber-security systems are effectively implemented with the aid of software support. Our approach is to determine normal behavior of a system based on sequences of system call traces made by the kernel processes in the system. This paper describes a robust and computationally efficient anomaly based host based intrusion detection system using an Encoder-Decoder mechanism. Using CuDNNLSTM networks, it is possible to obtain a set of comparable results with reduced training times. The Bidirectional Encoder and a unidirectional Decoder is trained on normal call sequences in the ADFA-LD dataset. Intrusion Detection is evaluated based on determining the probability of a sequence being reconstructed by the model yes

Cyber security has become one of the most challenging aspects of modern world digital technology and it has become imperative to minimize and possibly avoid the impact of cybercrimes. Host based intrusion detection systems help to protect systems from various kinds of malicious cyber attacks. One approach is to determine normal behaviour of a system based on sequences of system calls made by processes in the system [1]. This paper describes a computational efficient anomaly based intrusion detection system based on Recurrent Neural Networks. Using Gated Recurrent Units rather than the normal LSTM networks it is possible to obtain a set of comparable results with reduced training times. The incorporation of stacked CNNs with GRUs leads to improved anomaly IDS. Intrusion Detection is based on determining the probability of a particular call sequence occurring from a language model trained on normal call sequences from the ADFA Data set of system call traces [2]. Sequences with a low probability of occurring are classified as an anomaly. yes

The main purpose of the study is to identify the enablers which highly influence the knowledge management practices in Indian higher educational institutions. The aim of this study is realised by identifying the respondents' perception regarding each and every item of knowledge management enablers' dimensions, i.e., mutual trust, technology, organisation structure and collaboration. The study involved 225 research scholars and 225 faculty members from nine higher educational institutions. The respondents were required to respond to a five-point Likert-scale from strongly agree to strongly disagree. The collected data was then analysed using the SPSS 18.0 and AMOS 18.0 software packages. An exploratory factor analysis was conducted followed by confirmatory factor analysis. Descriptive and statistical data analyses were also performed on various dimensions of knowledge management enablers. The researcher extracted that more than half percent of the respondents were strongly agreed and agreed towards each and every items of knowledge management enabler's dimensions. Technology, collaboration and organisation structure were identify as the enablers which influence the knowledge management practices among Indian higher educational institutions.

Fecundity is an area where individual experiences, community belief and anecdotes cloud scientific evidence. It has been a common belief that working women experience various types of subfecundity like infertility, abortion and prolonged waiting time for pregnancy. Work environment may be contributing to this end, besides the delay in starting a reproductive career. Since working women in military environment are exposed to factors like mobility, shift duty, anaesthetic gases etc, it was decided to study their fecundity vis-a-vis housewives of same sodo economic status. In this study 204 subjects returned the questionnaire with a response rate of 68%. It was found that higher shades of subfecundity exist among women engaged in military service but does not reach the level of statistical significance (p=0.05) to attribute it to the work environment.