Understanding and Mitigating Cloud Computing Security Attacks: A Case of DDoS

1Joshua John, 2Okonkwo Obikwelu,3Godspower Akawuku and 4Chika Lilian Onyagu

 1Institute of Computing & ICT, Ahmadu Bello University, Zaria, Nigeria

2,3Department of Computer Science, Faculty of Physical Sciences, Nnamdi Azikiwe University, Awka, Nigeria.

4Margaret Lawrence University, Umunede Delta state, Nigeria.


Although cloud computing is considered the most widespread technology. Despite of what it offers, the end users still suffers many challenges, especially related to its security issues such as vulnerability to malicious attacks such as Distributed Denial of Service attack (DDoS), that prevents accessing the internet. This paper is design to bring understanding and ways of mitigating DDoS attacks in cloud computing environment by leveraging from the pool of diverse solutions proffered that detect and prevent such attacks from harming network communication.

Keywords: Cloudflare, Firewall, Gateway, Webserver and Zombies


Nowadays, everyone seems to be discussing about Cloud Computing. It is simply the shifting of technology to the cloud which have happened as a result of the move of traditional storage software to the Internet that took place progressively over the past ten years.  Cloud computing could be conceived as a way of accessing compute and storage systems without actually owning and doing active management of the resources. It can be described basically as services hosted over the internet rather than onsite servers. These services can be accessed from remote locations and have advantages such as saving time, cost, space and electricity [1]. These services are: Infrastructure as a Service (IaaS): this is a third-party hosts elements of infrastructure, such as hardware, software, servers, and storage, also providing backup, security, and maintenance.  Software as a Service (SaaS): Using the cloud, software such as an internet browser or application is able to become a usable tool and Platform as a Service (PaaS): The branch of cloud computing that allows users to develop, run, and manage applications, without having to get caught up in code, storage, and infrastructure and so on. In the recent years, adoption of cloud computing is increasing at an unprecedented pace [2].

We use cloud computing in our everyday life without even realizing it. Sending an email through an online service provider, listening to music, playing games or even just storing pictures and documents in our mobile devices constitute use of cloud computing. Unfortunately, as technology continues to grow, along with these advantages is a key concern about security, which on daily basis is on the increase. Other concern of enforcing security is as a result of the proliferation of cloud devices arousing attractive target for attackers. Cybersecurity ventures expect global cybercrime costs to grow by 15% per year over the next five years, reaching US$10.5tn annually by 2025 [3, 4, 5].

There are several potential security attacks on cloud computing environment, such as, Denial of Service (DoS) attacks, authentication attacks, man-in-the-middle, wrapping attacks, malware-injection attacks, flooding attacks, and browser attacks, etc. The most major threat to cloud security is Distributed Denial of Service Attack (DDoS) [6]. A distributed denial-of-service (DDoS) attack is a malicious attempt to disrupt the normal traffic of a targeted server, service or network by overwhelming the target or its surrounding infrastructure with a flood of Internet traffic. A simple principle governs a DDoS attack: it takes down websites offline by consuming more resources or occupying all available bandwidth. Attackers with more hijacked cloud devices can consume more resources and launch a more damaging attack.  The three main goals of attackers include: To cause consumption of limited resources; to cause destructive changes to network devices and to change or destroy configuration information. This is accomplished by exhausting the computing resources of the server by flooding the network bandwidth, which eventually leads to the non-availability of cloud services or resources, thereby, resulting to massive financial loss [7]. This kind of attack would lead to business lose or even discontinuance to various groups of users including government services, manufacturing, retailers, health care data support and it is launched for various reasons ranging from activism to state-sponsored disruption, with many attacks being carried out simply for profit. DDoS threats are not only becoming more dangerous, but attacks are also increasing in number. Experts predict that there was a 314% increase in overall attacks from the first half of 2022 to the first half of 2023 [8]. That number indicates that nearly every business will face a DDoS at some point, so preparing for this attack type should be at the top of your security to-do list [9, 10, 11]. Attackers seized the opportunity to create large botnets, to large complex DDoS attacks to disable or knock offline a target website. A botnet is a group of infected computers under the control of attackers used to perform various scams and cyber-attacks. Here, the attackers use malware to take control of vulnerable IoT devices to block legitimate users from accessing internet services by executing DDoS attacks.

Review of related work

The adoption of cloud computing has revolutionized data storage and processing. However, it has also introduced unique security concerns that should be understood to enable us improvise solutions to the challenges that could arise from misconfigurations, inadequate access controls, and vulnerabilities in cloud infrastructure which are also responsible for data breaches and unauthorized access to sensitive information. Many studies have been made of how to handle DDoS attacks and many elegant algorithms have been suggested. Some research deals with attack prevention and/or detection, some focus about how to filter DDoS attack and some research considers attack trace back. Here we discuss different research papers. For each of the following research papers we point out the proposed or deployed method and the scope of the method.

[12] proposed an IDS that made use of feature selection and a deep learning model for the categorization of DDoS attacks. They used the jumping gene-adapted NSGA-II algorithm to perform the feature selection. For the deep learning model, they used a convolutional neural network (CNN) integrated with long short-term memory (LSTM). The evaluation results showed the proposed approach to be effective against DDoS attacks.

For [13], real-time recognition of DDoS attacks using an ML classifier which relied on a distributed processing framework was the way to go. The DDoS detection rate was computed using the OpenStack based cloud testbed, through the Apache Spark architecture. A DL based IDS for DDoS attacks was proposed on the basis of 3 methods, namely Convolutional Neural Network (CNN), Deep Neural Network (DNN), and Recurrent Neural Network (RNN). The performance of each method was analyzed on the basis of 2 classification types (multiclass and binary), using 2 real traffic datasets- TON_IoT and CIC-DDoS2019. Based on this analysis, a DL based detection method for DoS attacks was proposed, which used the CNN method to carry out multiclass classification and binary classification, and used RNN method to improve efficiency.

[34], proposed a Mixed Kernel Extreme Learning Machine (MKELM) method integrating the ReliefF algorithm with nature inspired algorithms, for IDS. The MKELMs were developed to predict attacks, with the ReliefF algorithm providing inputs to the MKELM for selecting a suitable feature. The nature inspired algorithm determined the fitness function on the basis of kernel alignment, which was then used to build an optimum composite kernel in the MKELM. A novel approach was presented for evaluating resource consumption through ‘scaling down’ the resource i.e., through an improvement of the ‘scale inside out’ approaches. The presented approach utilized two modules- authentication model and elastic load balancing- to detect and mitigate DDoS attacks.

[15], proposed an ML-based model for the detection of DDoS attacks. The authors applied three different machine learning models: K-Nearest Neighbors (KNN), Random Forest (RF) and Naive Bayes (NB) classifier. The proposed approach can detect any type of DDoS attack in the network. The results of the proposed approach showed that the model can detect attacks with an average accuracy of 98.5%. Using the similar hybrid techniques, [16], proposed a hybrid detection method to DDoS attack, using entropy-based methods and custom-tailored methods. Custom tailored methods can only be used for the ones they are specially designed whereas anomaly-based methods can detect a wide range of anomalies and attack. The proposed hybrid method is combination of both feature-based and volume-based detection along with high and low rate attacks which gets a comparison between proposed and previously known methods. Some of the most common DDoS attacks are ICMP flood, SYN flood, DNS amplification attacks and the earlier Smurf Attack and Fraggle Attack. A script file was developed to test the following attacks: ICMP flood attack with a high packet rate attack on the specified target. Using this approach, a real-life botnet is simulated. As in a real-life situation, the attack does not start with all attackers at the same time, but instead with attackers initiated after a random delay period. [17] designed a technique for identifying cloud computing DDoS attacks. This technique employs machine learning algorithms such as support vector machine (SVM), naive Bayes (NB), and random forest (RF) for classification. The study was carried out using Tor Hammer as an attacking tool on a cloud environment, and a new dataset for the intrusion detection technique was developed.

[18], proposed a DDoS detection model that uses two algorithms, namely the power spectral density (PSD) and SVM algorithms, for low-rate DDoS attack classifications. The PSD algorithm calculates the entropy and then compares it with two predefined thresholds. To distinguish traffic patterns, the SVM algorithm is applied to investigate suspicious traffic and recognize similar patterns for the classifications. The experimental results showed that the proposed approach detected 99.19% of all low-rate DDoS attack traffic within a low complexity timeframe. The proposed work must be validated with recent datasets. [19], proposed a hybrid model of intrusion detection system in a cloud computing environment. The model detects violations, using the IP (Internet protocol)/MAC (Media access control) address at the point of entering the network of a cloud-based system. The methodology adopted is Object-Oriented Hypermedia Method (OOHDM) and the programming languages used is PHP, JavaScript, CSS and MySQL. The new system brings about a new method of detecting intruders by the combined use of IP/MAC address. [20] used Neural Networks and Data mining technique to detect DDOS attacks. This model needs less memory and claims that they have faster detection. The result shows that most of TCP attacks are detected. This system helps in detecting layer 7 (Application Layer) attacks and carry a lot of overhead. TTL value low and slow attacks. Packet monitoring in the cloud for TTL value said to have greater advantages in detecting DDOS attacks in the cloud, but it slows down the system performance which creates a limitation for cloud service providers.

Understanding How DDoS attack work?

The DDoS attacks are carried out with networks of Internet-connected machines. These networks consist of computers and other devices (such as cloud devices) which have been infected with malware, allowing them to be controlled remotely by an attacker. These individual devices are referred to as bots (or zombies), and a group of bots is called a botnet. Once a botnet has been established, the attacker is able to direct an attack by sending remote instructions to each bot. When a victim’s server or network is targeted by the botnet, each bot sends requests to the target’s IP address, potentially causing the server or network to become overwhelmed, resulting in a denial-of-service to normal traffic.


Though cloud computing has its pros and cons, on the whole, it is more beneficial than the harm it causes. Investment in cloud computing should be for the long term and the cloud computing domain is expected to evolve further in the future. Cloud computing has brought about a huge change in the way organizations operate. They have improved the entire process in a number of ways. We have seen a few of the major benefits that cloud computing has to offer.


Both outside attackers and insider threats (malicious or accidental) are substantial cloud security threats. It is essential to develop a comprehensive cloud security model to tackle the threat. With the appropriate tools and practices, you can significantly reduce your security risks.


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