Assessing and augmenting SCADA cyber security: a survey of techniques

SCADA systems monitor and control critical infrastructures of national importance such as power generation and distribution, water supply, transportation networks, and manufacturing facilities. The pervasiveness, miniaturisations and declining costs of internet connectivity have transformed these systems from strictly isolated to highly interconnected networks. The connectivity provides immense benefits such as reliability, scalability and remote connectivity, but at the same time exposes an otherwise isolated and secure system, to global cyber security threats. This inevitable transformation to highly connected systems thus necessitates effective security safeguards to be in place as any compromise or downtime of SCADA systems can have severe economic, safety and security ramifications. One way to ensure vital asset protection is to adopt a viewpoint similar to an attacker to determine weaknesses and loopholes in defences. Such mind sets help to identify and fix potential breaches before their exploitation. This paper surveys tools and techniques to uncover SCADA system vulnerabilities. A comprehensive review of the selected approaches is provided along with their applicability

Machine Learning in IoT Security:Current Solutions and Future Challenges

The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resource-constrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy issues in IoT networks primarily through traditional cryptographic approaches. However, the unique characteristics of IoT nodes render the existing solutions insufficient to encompass the entire security spectrum of the IoT networks. This is, at least in part, because of the resource constraints, heterogeneity, massive real-time data generated by the IoT devices, and the extensively dynamic behavior of the networks. Therefore, Machine Learning (ML) and Deep Learning (DL) techniques, which are able to provide embedded intelligence in the IoT devices and networks, are leveraged to cope with different security problems. In this paper, we systematically review the security requirements, attack vectors, and the current security solutions for the IoT networks. We then shed light on the gaps in these security solutions that call for ML and DL approaches. We also discuss in detail the existing ML and DL solutions for addressing different security problems in IoT networks. At last, based on the detailed investigation of the existing solutions in the literature, we discuss the future research directions for ML- and DL-based IoT security

Detailed Review on The Denial of Service (DoS) and Distributed Denial of Service (DDoS) Attacks in Software Defined Networks (SDNs) and Defense Strategies

The development of Software Defined Networking (SDN) has altered the landscape of computer networking in recent years. Its scalable architecture has become a blueprint for the design of several advanced future networks. To achieve improve and efficient monitoring, control and management capabilities of the network, software defined networks differentiate or decouple the control logic from the data forwarding plane. As a result, logical control is centralized solely in the controller. Due to the centralized nature, SDNs are exposed to several vulnerabilities such as Spoofing, Flooding, and primarily Denial of Service (DoS) and Distributed Denial of Service (DDoS) among other attacks. In effect, the performance of SDN degrades based on these attacks. This paper presents a comprehensive review of several DoS and DDoS defense/mitigation strategies and classifies them into distinct classes with regards to the methodologies employed. Furthermore, suggestions were made to enhance current mitigation strategies accordingly

Mobile Firewall System For Distributed Denial Of Service Defense In Internet Of Things Networks

Internet of Things (IoT) has seen unprecedented growth in the consumer space over the past ten years. The majority of IoT device manufacturers do not, however, build their products with cybersecurity in mind. The goal of the mobile firewall system is to move mitigation of network-diffused attacks closer to their source. Attack detection and mitigation is enforced using a machine that physically traverses the area. This machine uses a suite of security tools to protect the network. Our system provides advantages over current network attack mitigation techniques. Mobile firewalls can be deployed when there is no access to the network gateway or when no gateway exists, such as in IoT mesh networks. The focus of this thesis is to refine an explicit implementation for the mobile firewall system and evaluate its effectiveness. Evaluation of the mobile firewall system is analyzed using three simulated distributed denial of service case studies. Mobility is shown to be a great benefit when defending against physically distant attackers – the system takes no more than 131 seconds to fully nullify a worst-case attack