Technologies, especially Fourth Industrial
Revolution Technologies (4thIRTs) like Big Data
Analytics (BDA), Artificial Intelligence (AI), and Cloud
Computing (CC), among others, have led to exponential
growth in intrusions and assaults across Internet-based
technologies. One of the fatal dangers rising is the
distributed denial of service (DDoS) assault that may
shut down Internet-based systems and applications in no
time. The attackers are changing their skills frequently
and consequently avoiding the existing detection
mechanisms. Since the number of files created and
stored has expanded manifolds, the standard detection
systems are not suited for identifying modern DDoS
attacks. With the emergence of network-based
computing technologies like cloud computing, fog
computing, and IoT (Internet of Things), the context of
digitizing confidential data over the network is being
adopted by various organizations where the security of
that sensitive data is considered a major concern. Over
the past decade, there has been massive growth in the
usage of the internet, along with technological
advancements that demand the development of efficient
security algorithms that can withstand various patterns
of security breaches. The work systematically evaluates
the prominent literature, specifically in deep learning, to
identify DDoS using machine learning techniques.
Keywords :
DDoS Attack, Machine Learning, Deep Learning,VolumetricAttacks, Network Intrusion Detection System, PICO, PRISMA, SLRA.