A survey on botnets, issues, threats, methods, detection and prevention

Botnets have become increasingly common and progressively dangerous to both business and domestic networks alike. Due to the Covid-19 pandemic, a large quantity of the population has been performing corporate activities from their homes. This leads to speculation that most computer users and employees working remotely do not have proper defences against botnets, resulting in botnet infection propagating to other devices connected to the target network. Consequently, not only did botnet infection occur within the target user’s machine but also neighbouring devices. The focus of this paper is to review and investigate current state of the art and research works for both methods of infection, such as how a botnet could penetrate a system or network directly or indirectly, and standard detection strategies that had been used in the past. Furthermore, we investigate the capabilities of Artificial Intelligence (AI) to create innovative approaches for botnet detection to enable making predictions as to whether there are botnets present within a network. The paper also discusses methods that threat-actors may be used to infect target devices with botnet code. Machine learning algorithms are examined to determine how they may be used to assist AI-based detection and what advantages and disadvantages they would have to compare the most suitable algorithm businesses could use. Finally, current botnet prevention and countermeasures are discussed to determine how botnets can be prevented from corporate and domestic networks and ensure that future attacks can be prevented

Road Roughness Estimation Using Machine Learning

Road roughness is a very important road condition for the infrastructure, as the roughness affects both the safety and ride comfort of passengers. The roads deteriorate over time which means the road roughness must be continuously monitored in order to have an accurate understand of the condition of the road infrastructure. In this paper, we propose a machine learning pipeline for road roughness prediction using the vertical acceleration of the car and the car speed. We compared well-known supervised machine learning models such as linear regression, naive Bayes, k-nearest neighbor, random forest, support vector machine, and the multi-layer perceptron neural network. The models are trained on an optimally selected set of features computed in the temporal and statistical domain. The results demonstrate that machine learning methods can accurately predict road roughness, using the recordings of the cost approachable in-vehicle sensors installed in conventional passenger cars. Our findings demonstrate that the technology is well suited to meet future pavement condition monitoring, by enabling continuous monitoring of a wide road network

A Constructive Framework for the Preventive Signalling Maintenance Crew Scheduling Problem in the Danish Railway system

In this paper we consider the problem of planning preventive maintenance of railway signals in Denmark. This case is particularly interesting, as the entire railway signalling system is currently being upgraded to the new European Railway Traffic Management System (ERTMS) standard. This upgrade has significant implications for signal maintenance scheduling in the system. We formulate the problem as a multidepot vehicle routing and scheduling problem with time windows and synchronisation constraints, in a multiday time schedule. The requirement that some tasks require the simultaneous presence of more than one engineer means that task synchronisation must be considered. A multi-stage constructive framework is proposed, which first distributes maintenance tasks using a clustering formulation. Following this, a Constraint Programming (CP) based approach is used to generate feasible monthly plans for large instances of practical interest. Experimental results indicate that the proposed framework can generate feasible solutions and schedule a monthly plan of up to 1000 tasks for eight crew members, in a reasonable amount of computational tim

Motorcyclists' reactions to safety helmet law: a qualitative study

<p>Abstract</p> <p>Background</p> <p>Extensive body of the literature reveals that proper use of helmets is an effective way to reduce the severity of injuries and fatalities among motorcyclists. However, many motorcyclists do not use safety helmet properly. This study aimed to empirically explore reactions of motorcyclists to the safety helmet laws, in Iran.</p> <p>Methods</p> <p>Qualitative data were collected via four focus groups and 11 in-depth interviews. Participants were 28 male motorcyclists who never used a safety helmet during rides, and 4 male police officers. All transcripts, codes and categories were read for several times to exhaust identifiable major themes. During this process data were reduced from text to codes and themes.</p> <p>Results</p> <p>Five major themes emerged from the data analyses, including themes related to the following: (1) circumventing or dodging police officers; (2) simulating a helmet wearing behavior; (3) accepting the probability of receiving a ticket; (4) taking advantage of the police neglect and carelessness; and (5) using a cheap or convenient helmet.</p> <p>Conclusion</p> <p>Our findings suggest certain levels of reckless driving among the participating motorcyclists in this study. They also point to a system of law enforcement that operates haphazardly and fails to consistently penalize those who deviate from it. Further studies are needed to investigate how "risks" are perceived and relate to "reactions", and how a 'culture of masculinity' may encourage risk tolerance and a disposition toward lawlessness and carelessness among male motorcyclists. Also, there is a need for the development and implementation of multidimensional interventions that would offer socio-culturally sensitive educational and motivational messages to the motorcyclists and the in-service traffic-enforcement officers in Iran.</p