Enhancing DDoS Detection in Cloud Computing Environment Through Effective Feature Selection With SMOTE

In today's landscape, the widespread adoption of cloud computing has been accompanied by a corresponding increase in security vulnerabilities, with Distributed Denial-of-Service (DDoS) attacks posing one of the most serious challenges by overwhelming resources such as CPU power, memory, and network bandwidth, thereby disrupting services for legitimate users. Detecting DDoS attacks in cloud environments is particularly difficult due to the similarity between malicious and legitimate traffic, often originating from numerous geographically dispersed sources. This study evaluates the effectiveness of five supervised machine learning algorithms Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), k-Nearest Neighbours (KNN), and Naïve Bayes (NB) for detecting DDoS attacks in cloud computing environments using the publicly available Software Defined Networking (SDN) DDoS Attack Dataset. Comprehensive preprocessing including normalization, feature selection, and Synthetic Minority Oversampling Technique (SMOTE) was applied, along with rigorous regularization strategies to mitigate overfitting. Experimental results demonstrate that Random Forest achieved the highest balanced performance (95% accuracy, 96% precision, 95% recall), followed by KNN (94%), SVM (93%), DT (92%), and Naïve Bayes (91%). These findings confirm the potential of machine learning for reliable DDoS detection while emphasizing the importance of proper model regularization to ensure generalizability. Future work should explore larger datasets, real-time traffic analysis, and hybrid models to further enhance robustness.

DDoS (Distributed Denial of Service) attacks have now become a serious risk to the integrity and confidentiality of computer networks and systems, which are essential assets in today’s world. Detecting DDoS attacks is a difficult task that must be accomplished before any mitigation strategies can be used. The identification of DDoS attacks has already been successfully implemented using machine learning/deep learning (ML/DL). However, due to an inherent limitation of ML/DL frameworks—so-called optimal feature selection—complete accomplishment is likewise out of reach. This is a case in which a machine learning/deep learning-based system does not produce promising results for identifying DDoS attacks. At the moment, existing research on forecasting DDoS attacks has yielded a variety of unexpected predictions utilising machine learning (ML) classifiers and conventional approaches for feature encoding. These previous efforts also made use of deep neural networks to extract features without having to maintain the track of the sequence information. The current work suggests predicting DDoS attacks using a hybrid deep learning (DL) model, namely a CNN with BiLSTM (bidirectional long/short-term memory), in order to effectively anticipate DDoS attacks using benchmark data. By ranking and choosing features that scored the highest in the provided data set, only the most pertinent features were picked. Experiment findings demonstrate that the proposed CNN-BI-LSTM attained an accuracy of up to 94.52 percent using the data set CIC-DDoS2019 during training, testing, and validation.

Distributed denial of service (DDoS) attacks have emerged as a critical challenge for cloud computing, impacting service availability and raising concerns among providers. Despite cloud computing's scalable and flexible architecture, its vulnerabilities make it an attractive target for attackers. This paper presents a comprehensive survey of DDoS attacks in cloud environments, focusing on detection mechanisms leveraging information theory. Key contributions include an analysis of cloud computing characteristics exploited by attackers, a taxonomy of DDoS attacks, and a discussion of effective anomaly detection approaches. Solutions based on information theory, encompassing detection parameters, metrics, and validation techniques, are reviewed for their ability to enhance security with high accuracy and low computational costs. This survey aims to guide researchers and practitioners in developing advanced defenses for cloud applications. Open issues and future research directions are identified to inspire further innovation in mitigating DDoS attacks.

Due to the growing dependence of digital services on the Internet, Distributed Denial of Service (DDoS) attacks are a common threat that can cause significant disruptions to online operations and financial losses. Machine learning (ML) offers a promising way for early DDoS attack detection due to its ability to analyze large datasets and identify patterns. However, adding too many features to the ML might reduce its effectiveness in identifying the attacks provided by central network paradigms such as the Software-Defined Network (SDN). In this research, we investigate the effectiveness of the ML methods such as (Random Forest (RF), Naive Base (NB), and K-Nearest Neighbor’s (KNN)) combining SDN to enhance the classification of DDoS attacks. We leverage three diverse datasets: DDoS attack SDN, CICDDoS2019, and SDN-DDOS-TCP-SYN dataset. By leveraging cross-feature selection and feature ranking techniques, such as information gain, gain ratio, and Gini importance, we could identify the most relevant network features for DDoS attacks. We reduced the feature up to 5 effective features without compromising the classification accuracy. The experimental results show that the proposed models achieved an accuracy of 100% for both Random Forest (RF) and K-Nearest Neighbor (KNN), and 99.8% for Naive Bayes (NB). Due to their high accuracy and lower complexity, KNN and NB outperform ML algorithms in this study

Cloud computing has become the backbone of modern information technology structures, providing on-demand scalability, flexibility and cost-effectiveness. However, the growing dependency on cloud services creates new security challenges, particularly in terms of Distributed Denial-of-Service (DDoS) assaults. These assaults attempt to infect a target system with fake traffic, making it unavailable for actual users. In this paper, Enhancing Network Traffic Flow and Mitigating DDoS Threats in Cloud Computing Environments through Explicit Feature Interaction-Aware Graph Neural Network (ENTF-DDoS-EFIAGNN) is proposed. The methodology begins by performing data collection from the CICDDoS 2019 and UNSW-NB15 dataset, followed by preprocessing with the Multi-Observation Fusion Kalman Filter (MOFKF) used to impute missing values and Synthetic Minority Oversampling Technique (SMOTE) is used for data balancing. The Explicit Feature Interaction-Aware Graph Neural Network (EFIAGNN) is used to detect several DDoS attacks, including DrDoS_MSSQL, DrDoS_DNS, DrDoS_SNMP, DrDoS_LDAP, NetBIOS, Lag, Syn, MSSQL, Portmap, UDP, and LDAP. The EFIAGNN’s detection abilities are further improved by the Adaptive Elite Ant Lion Optimizer(AEALO). To mitigate the effects of a DDoS attack, it involves Boundary Integrated Neural Networks (BINN). The efficiency of the proposed ENTF-DDoS-EFIAGNN is validated using measures like accuracy, precision, average connection delay, computational time, Latency, Scalability, Resource Utilization and dropout rate. The results obtained show that ENTF-DDoS-EFIAGNN surpasses existing methods in regard to accuracy (24.20%, 25.11%, and 26.74%) as well as ROC (23.44%, 24.75%, and 25.64%) compared to techniques like an SDN-assisted defense mechanism for the shrew DDoS attack in cloud computing environment (DDoS-HBA-Bi-LSTM), a multi-point collaborative DDoS defense mechanism for IIoT environment(FQBDDA-CC), and Machine learning-driven DDoS attack detection in VANET cloud environments (ED-DDoS-CC), respectively.

DDoS attack prediction using a honey badger optimization algorithm based feature selection and Bi-LSTM in cloud environment

A Subset Scaling Recursive Feature Collection Based DDoS Detection Using Behavioural Based Ideal Neural Network For Security In A Cloud Environment

Cloud computing security remains the goal of both cloud service providers and customers. With many of the security threats to the security of cloud computing, Distributed Denial of Service (DDoS) attacks is one of the most worrisome. The danger posed by the DDoS attacks are already known and continue to be the predominant security challenge in reaching an impervious and guaranteed safe cloud computing resources and service delivery. Many researchers have proposed many detection and defense techniques to protect cloud computing against DDoS attacks. In this paper, we present a review of many detection techniques that are useful in spotting DDoS attacks that are cloud-based and make a comparative analysis between them to find a suitable technique for spotting these cloud computing based DDoS attacks.

Cloud computing has revolutionized the digital landscape by enabling organizations to manage data and applications in a scalable, cost-effective, and flexible manner. However, this shift also introduces new security challenges, with Distributed Denial of Service (DDoS) attacks among the most severe. As DDoS attacks become increasingly complex, traditional security measures often struggle to provide real-time detection and mitigation, highlighting the urgent need for more advanced approaches. This paper presents a robust, machine learning-based framework designed to detect and mitigate DDoS attacks within cloud environments, utilizing a combination of optimized machine learning (ML) and deep learning (DL) techniques to meet the demands of modern cloud security. The proposed solution integrates a variety of ML models, including Random Forest, Naïve Bayes, KNN, XG Boost each contributing distinct strengths to the detection process, we enhance model efficiency, improve feature selection, and fine-tune model parameters. These optimizations allow the framework to achieve high detection accuracy, with results exceeding 99.8% on benchmark datasets, including CIC-DDoS2019, which contain diverse and realistic DDoS attack patterns. Moreover, the proposed framework demonstrates low false-positive rates, making it reliable for deployment in dynamic cloud environments with timely responses are critical. Beyond high accuracy, our model is designed for scalability and adaptability, addressing the need for cloud infrastructures to handle fluctuating traffic volumes and evolving attack vectors. By reducing computational overhead through efficient feature selection and model optimization, the framework ensures minimal resource consumption, aligning with the operational efficiency goals of cloud systems. The research contributes to a deeper understanding of ML models in cloud security, providing a foundation for further advancements in real-time, autonomous DDoS mitigation solutions.

Distributed denial of service (DDoS) attacks in cloud computing environments are growing due to the essential characteristics of cloud computing. With recent advances in software-defined networking (SDN), SDN-based cloud brings us new chances to defeat DDoS attacks in cloud computing environments. Nevertheless, there is a contradictory relationship between SDN and DDoS attacks. On one hand, the capabilities of SDN, including software-based traffic analysis, centralized control, global view of the network, dynamic updating of forwarding rules, make it easier to detect and react to DDoS attacks. On the other hand, the security of SDN itself remains to be addressed, and potential DDoS vulnerabilities exist across SDN platforms. In this paper, we discuss the new trends and characteristics of DDoS attacks in cloud computing, and provide a comprehensive survey of defense mechanisms against DDoS attacks using SDN. In addition, we review the studies about launching DDoS attacks on SDN, as well as the methods against DDoS attacks in SDN. To the best of our knowledge, the contradictory relationship between SDN and DDoS attacks has not been well addressed in previous works. This work can help to understand how to make full use of SDN's advantages to defeat DDoS attacks in cloud computing environments and how to prevent SDN itself from becoming a victim of DDoS attacks, which are important for the smooth evolution of SDN-based cloud without the distraction of DDoS attacks.

The salient features of cloud computing (such as on-demand self-service, resource pooling, broad network access, rapid elasticity, and measured service) are being exploited by attackers to launch the severe Distributed Denial of Service (DDoS) attack. Generally, the DDoS attacks in such an environment have been implemented by flooding a huge volume (high-rate) of malicious traffic to exhaust the victim servers’ resources. Due to this huge volume of malicious traffic, such attacks can be easily detected. Thus, attackers are getting attracted towards the low-rate DDoS attacks, slowly. Low-rate DDoS attacks are difficult to detect due to their stealthy and low-rate traffic. In the recent years, many efforts have been devoted to defend against the low-rate DDoS attacks. By utilizing the salient features of cloud computing, it becomes easy for an attacker to launch sophisticated low-rate DDoS attacks. Thus, the study of various DDoS attacks and their corresponding defense approaches becomes essential to protect the cloud infrastructure from fatal effects of DDoS attacks. This paper presents a comprehensive taxonomy of all the possible variants of cloud DDoS attacks solutions with detailed insight into the characterization, prevention, detection, and mitigation mechanisms. The paper provides a detailed discussion on essential performance metrics to evaluate various defense solutions and their behavior in a cloud environment. The purpose of this survey paper is to excite the cloud security researchers to develop effective defense solutions against the various DDoS attacks. The research gaps and challenges are found, and described in the paper while future research directions are outlined.

Supervisory Control and Data Acquisition (SCADA) systems play a crucial role in overseeing and controlling renewable energy sources like solar, wind, hydro, and geothermal resources. Nevertheless, with the expansion of conventional SCADA network infrastructures, there arise significant challenges in managing and scaling due to increased size, complexity, and device diversity. Using Software Defined Networking (SDN) technology in traditional SCADA network infrastructure offers management, scaling and flexibility benefits. However, as the integration of SDN-based SCADA systems with modern technologies such as the Internet of Things, cloud computing, and big data analytics increases, cybersecurity becomes a major concern for these systems. Therefore, cyber-physical energy systems (CPES) should be considered together with all energy systems. One of the most dangerous types of cyber-attacks against SDN-based SCADA systems is Distributed Denial of Service (DDoS) attacks. DDoS attacks disrupt the management of energy resources, causing service interruptions and increasing operational costs. Therefore, the first step to protect against DDoS attacks in SDN-based SCADA systems is to develop an effective intrusion detection system. This paper proposes a Decision Tree-based Ensemble Learning technique to detect DDoS attacks in SDN-based SCADA systems by accurately distinguishing between normal and DDoS attack traffic. For training and testing the ensemble learning models, normal and DDoS attack traffic data are obtained over a specific simulated experimental network topology. Techniques based on feature selection and hyperparameter tuning are used to optimize the performance of the decision tree ensemble models. Experimental results show that feature selection, combination of different decision tree ensemble models, and hyperparameter tuning can lead to a more accurate machine learning model with better performance detecting DDoS attacks against SDN-based SCADA systems.

Security in this world of digital computing plays a typical role, since all the operations are automated and large volumes of data are being maintained in the servers. Cloud computing is one of the evolving technologies where a huge volume of storage is made on-line, data and services are also distributed. Because of its distributed nature, they have become easy targets for the intruders to exploit the information. The well known Distributed Denial of Service (DDoS) attack is the most prominent attacks in this area of computing. DDoS is the single largest threat to internet and internet of things. This paper provides a wide survey on various DDoS attacks, their vulnerabilities and countermeasures proposed against them. Also this paper provides an in-depth analysis on effects of DDoS attacks in the Cloud environment. Through the analysis done it will be useful for designing a secured cloud infrastructure which will abide the DDoS attacks.

Strong defence mechanisms are necessary to defend the availability and integrity of network infrastructure in the ever-changing cybersecurity landscape, where Distributed Denial of Service (DDoS) attacks are becoming more common. The capacity of Deep Learning (DL) models to automatically learn feature representations and discern complicated patterns within network traffic data has made them a potential strategy for DDoS attack finding and mitigation. The design of a critical cybersecurity threat that disrupts network operations and causes considerable economic losses internationally also affects how well DL models fight against developing attacks. The Shepard Interpolation Neural Network (SINN) classifier, fine-tuned with the Artificial Rabbit Optimiser (ARO), is proposed as a new method for DDoS assault detection in this paper. The technique takes advantage of the Kaggle DDoS dataset, which is naturally skewed and poses problems for conventional ML models. The solution is to use the Synthetic Minority Oversampling Technique (SMOTE) to level the playing field in the dataset, which will result in better model presentation in every class. Thanks to ARO’s hyperparameter optimisation, the SINN classifier—which is already well-known for its interpolation capabilities and resilience when dealing with nonlinear patterns—achieves even better detection accuracy. Experiments show that the suggested model outdoes state-of-the-art machine learning tactics for DDoS attack finding in terms of accuracy, recall, and F1-score. Reliable classification results are contributed to by the incorporation of SMOTE, which successfully mitigates bias induced by class imbalance. In addition to establishing a scalable framework for practical applications, this work showcases SINN-ARO’s capabilities in protecting networks from ever-changing cyber threats. Investigating how well this approach works in real-time, dynamic network settings and expanding its applicability to identify different forms of cyberattacks are the primary goals of future studies.

The recent development of cloud computing offers various services on demand for organization and individual users, such as storage, shared computing space, networking, etc. Although Cloud Computing provides various advantages for users, it remains vulnerable to many types of attacks that attract cyber criminals. Distributed Denial of Service (DDoS) is the most common type of attack on cloud computing. Consequently, Cloud computing professionals and security experts have focused on the growth of preventive processes towards DDoS attacks. Since DDoS attacks have become increasingly widespread, it becomes difficult for some DDoS attack methods based on individual network flow features to distinguish various types of DDoS attacks. Further, the monitoring pattern of traffic changes and accurate detection of DDoS attacks are most important and urgent. In this research work, DDoS attack detection methods based on deep belief network feature extraction and Hybrid Long Short-Term Memory (LSTM) model have been proposed with NSL-KDD dataset. In Hybrid LSTM method, the Particle Swarm Optimization (PSO) technique, which is combined to optimize the weights of the LSTM neural network, reduces the prediction error. This deep belief network method is used to extract the features of IP packets, and it identifies DDoS attacks based on PSO-LSTM model. Moreover, it accurately predicts normal network traffic and detects anomalies resulting from DDoS attacks. The proposed PSO-LSTM architecture outperforms the classification techniques including standard Support Vector Machine (SVM) and LSTM in terms of attack detection performance along with the results of the measurement of accuracy, recall, f-measure, precision.