Distributed Denial Of Service Attack Scenario Research Articles

Toward Generating a New Cloud-Based Distributed Denial of Service (DDoS) Dataset and Cloud Intrusion Traffic Characterization

The distributed denial of service attack poses a significant threat to network security. Despite the availability of various methods for detecting DDoS attacks, the challenge remains in creating real-time detectors with minimal computational overhead. Additionally, the effectiveness of new detection methods depends heavily on well-constructed datasets. This paper addresses the critical DDoS dataset creation and evaluation domain, focusing on the cloud network. After conducting an in-depth analysis of 16 publicly available datasets, this research identifies 15 shortcomings across various dimensions, emphasizing the need for a new approach to dataset creation. Building upon this understanding, this paper introduces a new public DDoS dataset named BCCC-cPacket-Cloud-DDoS-2024. This dataset is meticulously crafted, addressing challenges identified in previous datasets through a cloud infrastructure featuring over eight benign user activities and 17 DDoS attack scenarios. Also, a Benign User Profiler (BUP) tool has been designed and developed to generate benign user network traffic based on a normal user behavior profile. We manually label the dataset and extract over 300 features from the network and transport layers of the traffic flows using NTLFlowLyzer. The experimental phase involves identifying an optimal feature set using three distinct algorithms: ANOVA, information gain, and extra tree. Finally, this paper proposes a multi-layered DDoS detection model and evaluates its performance using the generated dataset to cover the main issues of the traditional approaches.

Anomaly Detection in Smart Agriculture Systems on Network Edge Using Deep Learning Technique

With the widespread adoption of Internet of Things (IoT) technologies across various domains, including smart agriculture, urban environments, and homes, the threat of zero-day attacks has surged. This research delves into the application of deep learning techniques to detect anomalies in smart agricultural systems at the network edge, with a specific focus on safeguarding them against Distributed Denial of Service (DDoS) attacks. In this study, we propose an anomaly detection model based on CNN-LSTM to analyze sensor data collected from IoT devices. We rigorously train and test our model using two distinct datasets of sensor readings, simulating potential DDoS attack scenarios. The model's performance is assessed using key metrics such as detection accuracy, recall, and F1-score. Our results demonstrate the effectiveness of our approach, achieving an impressive anomaly detection accuracy of 99.7%. This research contributes significantly to the development of robust and efficient attack and anomaly detection techniques for smart agriculture systems at the network edge, ultimately enhancing the reliability and sustainability of agricultural practices.

Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

In the design and planning of next-generation Internet of Things (IoT), telecommunication, and satellite communication systems, controller placement is crucial in software-defined networking (SDN). The programmability of the SDN controller is sophisticated for the centralized control system of the entire network. Nevertheless, it creates a significant loophole for the manifestation of a distributed denial of service (DDoS) attack straightforwardly. Furthermore, recently a Distributed Reflected Denial of Service (DRDoS) attack, an unusual DDoS attack, has been detected. However, minimal deliberation has given to this forthcoming single point of SDN infrastructure failure problem. Moreover, recently the high frequencies of DDoS attacks have increased dramatically. In this paper, a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has proposed. Our proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS occurrence. The obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99% accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

An Enhanced Way of Distributed Denial of Service Attack Detection by Applying Machine Learning Algorithms in Cloud Computing

Cloud Computing has revolutionized the Information Technology by allowing the users to use variety number of resources in different applications in a less expensive manner. The resources are allocated to access by providing scalability flexible on-demand access in a virtual manner, reduced maintenance with less infrastructure cost. The majority of resources are handled and managed by the organizations over the internet by using different standards and formats of the networking protocols. Various research and statistics have proved that the available and existing technologies are prone to threats and vulnerabilities in the protocols legacy in the form of bugs that pave way for intrusion in different ways by the attackers. The most common among attacks is the Distributed Denial of Service (DDoS) attack. This attack targets the cloud’s performance and cause serious damage to the entire cloud computing environment. In the DDoS attack scenario, the compromised computers are targeted. The attacks are done by transmitting a large number of packets injected with known and unknown bugs to a server. A huge portion of the network bandwidth of the users’ cloud infrastructure is affected by consuming enormous time of their servers. In this paper, we have proposed a DDoS Attack detection scheme based on Random Forest algorithm to mitigate the DDoS threat. This algorithm is used along with the signature detection techniques and generates a decision tree. This helps in the detection of signature attacks for the DDoS flooding attacks. We have also used other machine learning algorithms and analyzed based on the yielded results.

Securing the cloud-assisted smart grid

Rapid elasticity, ubiquitous network access, and highly-reliable services are some of the desirable features of cloud computing that are attractive for building cloud-assisted data-intensive Smart Grid (SG) applications. However, the Distributed Denial-of-Service (DDoS) attacks represent a serious threat to the cloud-assisted SG applications. To mitigate the risk related to the DDoS threat, we propose an SG-relevant Hierarchical Hybrid Cloud-Extension Concept (HHCEC) along with a DDoS attack defense mechanism, termed as Port Hopping Spread Spectrum (PHSS). HHCEC is a cloud-assisted architecture designed to meet scalability and security requirements of the SG applications in the cloud. To prevent transport or application-layer DDoS attacks on HHCEC, PHSS switches the open port of server as a function of time and a secret shared between authorized clients and server, and thus efficiently dropping packets with closed port number. In addition, PHSS spreads the data packets over all the servers versus a single server to provide a robust protection against volume-based DDoS attacks that would affect some of the servers. This packet spreading approach enables PHSS to instantiate replica servers to take over the attacked servers without blocking the whole traffic by utilizing the rapid-elasticity characteristic of the cloud. Moreover, PHSS leverages a shuffling-based containment mechanism in order to quarantine malicious clients in a notably short time. Accordingly, the effect of a DDoS attack based on the compromised secret of the malicious clients is minimized. We evaluate our approach by building a proof-of-concept prototype using Amazon’s EC2 and the PlanetLab test-bed. In a DDoS attack scenario, the proposed approach obtains a significant availability enhancement of > 38% that highlight its efficiency in comparison to existing approaches. The results also indicate negligible overhead for the proposed approach compared to the plain system i.e., no additional latency and less than 0.01% throughput degradation.

OverCourt: DDoS mitigation through credit-based traffic segregation and path migration

Distributed Denial of Service (DDoS) attacks have become one of the most serious threats to the Internet. To mitigate DDoS attacks, much progress has been made in developing currency-based solutions, where a sender is required to spend resources such as computational cost, bandwidth, prior knowledge, and human actions to purchase her legitimacy before sending packets. In this paper, we propose an innovative overlay-based DDoS mitigation architecture by introducing a credit-based accounting mechanism, where a sender can send packets based on her credit points earned by her legitimate communication behaviors instead of expending resources in advance. Since the credit points given to a sender is designed to be measured based on her history of communication patterns, a well-behaving sender can gain her credit points while an ill-behaving one will lose her credit points. We propose an architecture of such a credit-based system, named OverCourt, where a well-behaving client may dynamically migrate to a protected channel when her credit points exceed a threshold while an ill-behaving client will be blocked after her credit points have been exhausted. The analysis and simulation results show that OverCourt can mitigate DDoS attacks under various DDoS attack scenarios.