Impact evaluation of DDoS and Malware attack using IoT devices
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Ronierison Maciel
Abstract
Distributed denial-of-service (DDoS) attacks deny access to infrastructures of service providers. These attacks can arise anytime, anywhere, and with little or no warning at all. Most of the small and medium businesses (SMBs) are not able to handle a significant outage, which may be fatal for the companies. These attacks generate damage to enterprises due to service provisioning interruption, which increases the chances of financial losses, and the system’s unavailability. Therefore, to overcome these issues, the companies must possess a bandwidth higher than the attacker, redundant components in their infrastructure, regular backups, firewalls, other proactive and reactive mechanisms for threat monitoring. This chapter explores DDoS and Malware attacks that employ the Internet of Things (IoT) devices. Hierarchical modeling is commonly used to evaluate the availability of such systems. This chapter also assesses the DDoS attack impacts and Malware in IoT devices. It was proposed models based on attack trees that produce the system and components behavior to determine the DDoS and Malware attack effects on system availability; still, it was verified metrics of interest as the likelihood of an attack, attacker benefit, feasibility, and pain factor. The attack tree indicators show the impact of the concurrent attacks using vulnerable IoT devices on a computer system, which can cause a system’s downtime. Using the attack tree analysis, we allow planning and improving the system’s availability, maintainability, and reliability. The obtained results show that DDoS attacks orchestrated by IoT devices correlate negatively with Malware and affect the system’s availability and services.
Abstract
Distributed denial-of-service (DDoS) attacks deny access to infrastructures of service providers. These attacks can arise anytime, anywhere, and with little or no warning at all. Most of the small and medium businesses (SMBs) are not able to handle a significant outage, which may be fatal for the companies. These attacks generate damage to enterprises due to service provisioning interruption, which increases the chances of financial losses, and the system’s unavailability. Therefore, to overcome these issues, the companies must possess a bandwidth higher than the attacker, redundant components in their infrastructure, regular backups, firewalls, other proactive and reactive mechanisms for threat monitoring. This chapter explores DDoS and Malware attacks that employ the Internet of Things (IoT) devices. Hierarchical modeling is commonly used to evaluate the availability of such systems. This chapter also assesses the DDoS attack impacts and Malware in IoT devices. It was proposed models based on attack trees that produce the system and components behavior to determine the DDoS and Malware attack effects on system availability; still, it was verified metrics of interest as the likelihood of an attack, attacker benefit, feasibility, and pain factor. The attack tree indicators show the impact of the concurrent attacks using vulnerable IoT devices on a computer system, which can cause a system’s downtime. Using the attack tree analysis, we allow planning and improving the system’s availability, maintainability, and reliability. The obtained results show that DDoS attacks orchestrated by IoT devices correlate negatively with Malware and affect the system’s availability and services.
Kapitel in diesem Buch
- Frontmatter I
- Preface V
- Acknowledgments VII
- About the Editors IX
- Contents XI
- List of contributors XIII
- Impact evaluation of DDoS and Malware attack using IoT devices 1
- Understanding and implementation of machine learning using support vector machine for efficient DDoS attack detection 29
- Cryptographic method based on Catalan objects and enumerative chess problem 51
- Distributed denial-of-service attacks and mitigation in wireless sensor networks 67
- New techniques for DDoS attacks mitigation in resource-constrained networks 83
- Detection and behavioral analysis of botnets using honeynets and classification techniques 131
- Selected practical and effective techniques to combat distributed denial-of-service (DDoS) attacks 159
- Probability, queuing, and statistical perspective in the distributed denial-of-service attacks domain 173
- Frequently used machine learning algorithm for detecting the distributed denial-of-service (DDoS) attacks 189
- Utilization of puzzles for protection against DDoS attacks 203
- Index 217
Kapitel in diesem Buch
- Frontmatter I
- Preface V
- Acknowledgments VII
- About the Editors IX
- Contents XI
- List of contributors XIII
- Impact evaluation of DDoS and Malware attack using IoT devices 1
- Understanding and implementation of machine learning using support vector machine for efficient DDoS attack detection 29
- Cryptographic method based on Catalan objects and enumerative chess problem 51
- Distributed denial-of-service attacks and mitigation in wireless sensor networks 67
- New techniques for DDoS attacks mitigation in resource-constrained networks 83
- Detection and behavioral analysis of botnets using honeynets and classification techniques 131
- Selected practical and effective techniques to combat distributed denial-of-service (DDoS) attacks 159
- Probability, queuing, and statistical perspective in the distributed denial-of-service attacks domain 173
- Frequently used machine learning algorithm for detecting the distributed denial-of-service (DDoS) attacks 189
- Utilization of puzzles for protection against DDoS attacks 203
- Index 217
