  In recent times, I've encountered a principle known as cloud computing, a
model that simplifies user access to data and computing power on a demand
basis. The main objective of cloud computing is to accommodate users' growing
needs by decreasing dependence on human resources, minimizing expenses, and
enhancing the speed of data access. Nevertheless, preserving security and
privacy in cloud computing systems pose notable challenges. This issue arises
because these systems have a distributed structure, which is susceptible to
unsanctioned access - a fundamental problem. In the context of cloud computing,
the provision of services on demand makes them targets for common assaults like
Denial of Service (DoS) attacks, which include Economic Denial of
Sustainability (EDoS) and Distributed Denial of Service (DDoS). These
onslaughts can be classified into three categories: bandwidth consumption
attacks, specific application attacks, and connection layer attacks. Most of
the studies conducted in this arena have concentrated on a singular type of
attack, with the concurrent detection of multiple DoS attacks often overlooked.
This article proposes a suitable method to identify four types of assaults:
HTTP, Database, TCP SYN, and DNS Flood. The aim is to present a universal
algorithm that performs effectively in detecting all four attacks instead of
using separate algorithms for each one. In this technique, seventeen server
parameters like memory usage, CPU usage, and input/output counts are extracted
and monitored for changes, identifying the failure point using the CUSUM
algorithm to calculate the likelihood of each attack. Subsequently, a fuzzy
neural network is employed to determine the occurrence of an attack. When
compared to the Snort software, the proposed method's results show a
significant improvement in the average detection rate, jumping from 57% to 95%.
