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Purpose: Small and medium enterprises (SMEs) are the most significant contributors to maximum employment generation, the gross domestic product (GDP) of many countries, and the overall global economy. It is also evident that cyber threats are becoming a big challenge for SMEs, which is directly impacting global economy.

Methodology: Existing research inputs were accessed to understand current cyber threats for SMEs and their cybersecurity posture. Additionally, this research has collected the latest insights by taking direct inputs from SMEs and conducting a well-designed research survey. It has provided a few direct inputs to designing solutions for the SME segment. For analysis and recommendations, cybersecurity best practices and core cybersecurity concepts are considered at the centre of the solution.

Findings: Implementing existing cybersecurity standards or frameworks is not easy for SMEs, as they generally have limited resources and different priorities for their business when it comes to the implementation of any cybersecurity controls. Currently, many cybersecurity standards are not able to support the implementation of business domain-specific controls.

Practical implications: Along with the research findings shared in this chapter, as a resolution to the problems faced by SMEs, the authors will propose a new framework as a solution. This framework is designed using core concepts of cybersecurity such as confidentiality, integrity, and availability (CIA triad) as well as defence in depth (DiD) mechanisms in each layer of organisation. The authors will also share a high-level idea about how reliable artificial intelligence-based software can help identify recommended controls for particular SMEs.

In this chapter, an attempt has been made to develop a security-based hardware system using an 8-bit single-chip microcontroller in conjunction with some sensor technology and lighting and alarming actuators. The proposed system aims to ensure the security and privacy of a dedicated area in terms of unauthorized human intrusion, hazardous gas leakage, extreme prolonged temperature changes, atypical smoke or vapor content in space and abrupt drop in illumination. The proposed system is capable of detecting any type of physical intervention and hazardous anomalies in the environment of the reserved space. In order to define the operation of the system, programs written in C++ with the special rule of code structuring have been deployed on the microcontroller using the Arduino Integrated Development Environment. The system is like a small container, enclosing all the respective sensor modules, microcontroller board and open connections for actuators. The proposed system is easy to use hardware and does not demand any human intervention for its functioning and can be installed with almost no changes in the infrastructure.

Purpose – The purpose of this paper is to develop prototype of the information system of the flood monitoring based internet of things (IoT). This prototype serves to assist users in accessing flood levels through water levels and rainy weather conditions.

Design/Methodology/Approach – This paper presents the design of information system of flood monitoring based internet of things (IoT). This prototype study acquires water level and rainfall data using ultrasonic sensors HC-SR04 and rain sensor. Data of flood height and rain levels detected by sensors are processed using Arduino Uno Microcontroller to produce output data in HTML format. Flood altitude information system and rainy weather from the microcontroller are distributed using ethernet module as web server integrated with Wireless N Router TL-MR3020 as a gateway path to the user.

Findings – This research produces a prototype of web-based flood monitoring information system that has been able to distribute data of flood height and rainy weather in real time.

Research Limitations/Implications – In the implementation of measurement, the information system only accesses one flood detector or one flooded location.

Practical Implications – This research produces a prototype of web-based flood monitoring information system that has been able to distribute data of flood height and rainy weather in real time.

Originality/Value – System prototype is the first development that uses internet of things (IoTs) method in real time web-based flood measurement with information in the form of flood hazard information in the form of water level and rainy weather conditions.

A zero-day vulnerability is a complimentary ticket to the attackers for gaining entry into the network. Thus, there is necessity to device appropriate threat detection systems and establish an innovative and safe solution that prevents unauthorised intrusions for defending various components of cybersecurity. We present a survey of recent Intrusion Detection Systems (IDS) in detecting zero-day vulnerabilities based on the following dimensions: types of cyber-attacks, datasets used and kinds of network detection systems.

Purpose: The study focuses on presenting an exhaustive review on the effectiveness of the recent IDS with respect to zero-day vulnerabilities.

Methodology: Systematic exploration was done at the IEEE, Elsevier, Springer, RAID, ESCORICS, Google Scholar, and other relevant platforms of studies published in English between 2015 and 2021 using keywords and combinations of relevant terms.

Findings: It is possible to train IDS for zero-day attacks. The existing IDS have strengths that make them capable of effective detection against zero-day attacks. However, they display certain limitations that reduce their credibility. Novel strategies like deep learning, machine learning, fuzzing technique, runtime verification technique, and Hidden Markov Models can be used to design IDS to detect malicious traffic.

Implication: This paper explored and highlighted the advantages and limitations of existing IDS enabling the selection of best possible IDS to protect the system. Moreover, the comparison between signature-based and anomaly-based IDS exemplifies that one viable approach to accurately detect the zero-day vulnerabilities would be the integration of hybrid mechanism.

One of the most neglected sources of energy loss is streetlights that generate too much light in areas where it is not required. Energy waste has enormous economic and environmental effects. In addition, due to the conventional manual nature of operation, streetlights are frequently seen being turned ‘ON’ during the day and ‘OFF’ in the evening, which is regrettable even in the twenty-first century. These issues require automated streetlight control in order to be resolved. This study aims to develop a novel streetlight controlling method by combining a smart transport monitoring system powered by computer vision technology with a closed circuit television (CCTV) camera that allows the light-emitting diode (LED) streetlight to automatically light up with the appropriate brightness by detecting the presence of pedestrians or vehicles and dimming the streetlight in their absence using semantic image segmentation from the CCTV video streaming. Consequently, our model distinguishes daylight and nighttime, which made it feasible to automate the process of turning the streetlight ‘ON’ and ‘OFF’ to save energy consumption costs. According to the aforementioned approach, geo-location sensor data could be utilised to make more informed streetlight management decisions. To complete the tasks, we consider training the U-net model with ResNet-34 as its backbone. Validity of the models is guaranteed with the use of assessment matrices. The suggested concept is straightforward, economical, energy-efficient, long-lasting and more resilient than conventional alternatives.