Attack Detection in Internet of Things: A Systematic Literature Review

Abstract

AbstractAs the frequency of security breaches continues to rise, cybersecurity remains a critical concern for every industry in the online. Thousands of zeroday attacks are known to emerge on a regular basis as a consequence of the integration of multiple protocols, primarily from the Internet of Things (IoT). The majority of such attacks are minor variants on previous research findings intrusions. This suggests that even sophisticated techniques like typical machine learning (ML) algorithms have difficulties spotting these tiny kinds of attacks over time. These attacks are called as DDoS attack; they are used to prevent clients from accessing a server or website. DDoS attacks have been employed by cybercriminals to bring down targeted servers and breach venture networks with the ability to overwhelm results. Because of the growing volume and complexity of DDoS attacks, many organizations are having difficulty handling them. Smart gadgets and IoT are particularly vulnerable to a wide range of DDoS hits due to resource constraints such as limited memory and processing capacity, thus cybercriminals are aware of these current technologies and their flaws. Because of an attack on their internet service providers in 2016, many firms, including Netflix, CNN, and Twitter, were forced to go down for nine hours. This technological failure resulted in several issues, including financial losses, productivity losses, brand damage, insurance rating drops, unstable client-provider relationships, and IT budget overruns. We need to construct an IDS system to expose and prevent DDoS attacks to secure data processing, information technology, and commercial components. The cost of cybersecurity will be greatly lowered if security teams use current and new technology like ML, automation, and artificial intelligence (AI). This study will examine the detection performance of DDoS attacks using several ML, DL techniques and also categorize it into cloud and fog ecosystems.KeywordsCyber securityDDOS attackMachine learningIoTFog networks