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Software-Defined Networking (SDN) is an emerging architecture that enables flexible and easy management and communication of large-scale networks. It offers programmable and centralized interfaces for making complex network decisions dynamically and seamlessly. However, SDN provides opportunities for businesses and individuals to build network applications based on their demands and improve their services. In contrast, it started to face a new array of security and privacy challenges and simultaneously introduced the threats of a single point of failure. Usually, attackers launch malicious attacks such as botnets and Distributed Denial of Service (DDoS) to the controller through OpenFlow switches. Deep learning (DL)-based security applications are trending, effectively detecting and mitigating potential threats with fast response. In this article, we analyze and show the performance of the DL methods to detect botnet-based DDoS attacks in an SDN-supported environment. A newly self-generated dataset is used for the evaluation. We also used feature weighting and tuning methods to select the best subset of features. We verify the measurements and simulation outcomes over a self-generated dataset and real testbed settings. The main aim of this study is to find a lightweight DL method with baseline hyper-parameters to detect botnet-based DDoS attacks with features and data that can be easily acquired. We observed that the best subset of features influences the performance of the DL method, and the prediction accuracy of the same method could be variated with a different set of features. Finally, based on empirical results, we found that the CNN method outperforms the dataset and real testbed settings. The detection rate of CNN reaches 99% for normal flows and 97% for attack flows.

Internet of things architecture is the integration of real-world objects and places with the internet. This booming in technology is bringing ease in our lifestyle and making formerly impossible things possible. Internet of things playing a vital role in bridging this gap easily and rapidly. IoT is changing our lifestyle and the way of working the technologies, by bringing them together at the one page in several application areas of daily life. However, IoT has to face several challenges in the form of cyber scams, one of the major challenges IoT has to face is the likelihood of Ransomware attack. Ransomware is a malicious kind of software that restricts access to vital information in some way and demand payment for getting access to this information. The ransomware attack is becoming widespread daily, and it is bringing disastrous consequences, including loss of sensitive data, loss of productivity, data destruction, and loss of reputation and business downtime. Which further leads to millions of dollar daily losses due to the downtime. This is inevitable for organizations to revise their annual cybersecurity goals and need to implement proper resilience and recovery plan to keep business running. However, before proceeding towards providing a practical solution, there is a need to synthesize the existing data and statistics about this crucial attack to make aware to the researchers and practitioners. To fill this gap, this paper provides a comprehensive survey on evolution, prevention and mitigation of Ransomware in IoT context. This paper differs from existing in various dimensions: firstly, it provides deeper insights about Ransomware evolution in IoT. Secondly; it discusses diverse aspects of Ransomware attacks on IoT which include, various types of Ransomware, Current research in Ransomware, Existing techniques to prevent and mitigate Ransomware attacks in IoT along with the ways to deal with an affected machine, the decision about paying the ransom or not, and future emerging trends of Ransomware propagation in IoT. Thirdly, a summary of current research is also provided to show various directions of research. In sum, this detailed survey is expected to be useful for researchers and practitioners who are involved in developing solutions for IoT security.

Industry Revolution 4.0 connects the Internet of Things (IoT) resource-constrained devices to Smart Factory solutions and delivers insights. As a result, a complex and dynamic network with a vulnerability inherited from the Internet becomes an attractive target for hackers to attack critical infrastructures. Therefore, this paper selects three potential attacks with the evaluation of the protections, namely (1) distributed denial of service (DDoS), (2) address resolution protocol (ARP) spoofing, and (3) Internet protocol (IP) fragmentation attacks. In the DDoS protection, the F1-score, accuracy, precision, and recall of the four-feature random forest with principal component analysis (RFPCA) model are 95.65%, 97%, 97.06%, and 94.29%, respectively. In the ARP spoofing, a batch processing method adopts the entropy calculated in the 20 s window with sensitivity to network abnormalities detection of various ARP spoofing scenarios involving victims’ traffic. The detected attacker’s MAC address is inserted in the block list to filter malicious traffic. The proposed protection in the IP fragmentation attack is implementing one-time code (OTC) and timestamp fields in the packet header. The simulation shows that the method detected 160 fake fragments from attackers among 2040 fragments.

The advent of new technologies and the rapid growth of internet users have given birth to the menace of cyber-crime. Unfortunately, it is increasing at an alarming pace. This situation calls for good cyber hygiene behavior to secure digital lives. Cyber hygiene behaviour holds a significant role in terms of cybersecurity across the globe. There is a dire need to understand better the user variations associated with good or bad cyber hygiene behaviour and an improved view of what users do to encourage good cyber hygiene. Cybersecurity attacks are rising due to recent advancements in ICT and the Industrial Revolution 4.0 (IR 4.0). Software development organizations are among the crucial sectors suffering from cybersecurity issues. These organizations are more vulnerable to cyber-attacks because they lack proper cybersecurity culture. Although many initiatives have been taken by academia and industry to address this rising issue, the problem still exists for Software development organizations because good cyber hygiene behaviour is not observed, which is a prerequisite to reduce cyber threats. This study performed a Systematic Literature Review (SLR) of research papers published during 2010 – 2020. The key factors influencing software engineers’ cyber hygiene behaviour intention are extracted from the published literature. The study examined 35 research papers out of 5,270 found from IEEE Xplore, Emerald Insight, SpringerLink, and ScienceDirect databases. The study reviewed number of factors such as the role of personal, social, socio-cognitive, environmental, & technological factors that may individually or collectively influence software engineers’ cyber hygiene behaviour. The positive and negative factors associated with the cyber hygiene behaviour of software engineers are also categorized. This study enriches the understanding of the potential factors related to software engineers’ cyber hygiene behaviours. It provides valuable insights to researchers, software development organizations, governments, and individuals associated with the field of Software Engineering. This research will assist in changing the software engineers’ behaviour towards cyber hygiene, which will ultimately lead to mitigate the issues of Cybersecurity.

The paralyzed population is increasing worldwide due to stroke, spinal code injury, post-polio, and other related diseases. Different assistive technologies are used to improve the physical and mental health of the affected patients. Exoskeletons have emerged as one of the most promising technology to provide movement and rehabilitation for the paralyzed. But exoskeletons are limited by the constraints of weight, flexibility, and adaptability. To resolve these issues, we propose an adaptive and flexible Brain Energized Full Body Exoskeleton (BFBE) for assisting the paralyzed people. This paper describes the design, control, and testing of BFBE with 15 degrees of freedom (DoF) for assisting the users in their daily activities. The flexibility is incorporated into the system by a modular design approach. The brain signals captured by the Electroencephalogram (EEG) sensors are used for controlling the movements of BFBE. The processing happens at the edge, reducing delay in decision making and the system is further integrated with an IoT module that helps to send an alert message to multiple caregivers in case of an emergency. The potential energy harvesting is used in the system to solve the power issues related to the exoskeleton. The stability in the gait cycle is ensured by using adaptive sensory feedback. The system validation is done by using six natural movements on ten different paralyzed persons. The system recognizes human intensions with an accuracy of 85%. The result shows that BFBE can be an efficient method for providing assistance and rehabilitation for paralyzed patients.

A growing portfolio of research has been reported on the use of machine learning-based architectures and models in the domain of healthcare. The development of data-driven applications and services for the diagnosis and classification of key illness conditions is challenging owing to issues of low volume, low-quality contextual data for the training, and validation of algorithms, which, in turn, compromises the accuracy of the resultant models. Here, a fusion machine learning approach is presented reporting an improvement in the accuracy of the identification of diabetes and the prediction of the onset of critical events for patients with diabetes (PwD). Globally, the cost of treating diabetes, a prevalent chronic illness condition characterized by high levels of sugar in the bloodstream over long periods, is placing severe demands on health providers and the proposed solution has the potential to support an increase in the rates of survival of PwD through informing on the optimum treatment on an individual patient basis. At the core of the proposed architecture is a fusion of machine learning classifiers (Support Vector Machine and Artificial Neural Network). Results indicate a classification accuracy of 94.67%, exceeding the performance of reported machine learning models for diabetes by ~1.8% over the best reported to date.