# Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks
## 3 Dataset  
Its prominence and extensive utilisation underpins the selection of the KDDCUP'99 dataset for our research in the domain of intrusion detection. This dataset's vast repository of instances and encompassing feature set, coupled with its coverage of diverse attack types and scenarios, renders it a pivotal resource for this field. It is a fundamental benchmark, facilitating the rigorous evaluation and comparative analysis of various intrusion detection methodologies and systems. It is imperative to acknowledge that the KDDCUP'99 dataset does not lack limitations and challenges, as this research will discuss later. These encompass the antiquity of the underlying network environment, an inherent skew in class distribution, the presence of superfluous and non-informative features, and the prevalence of mislabelled instances. It is also important to note that contemporary datasets, such as MAWILab, Malware Training Sets, ADFA Intrusion Detection Datasets, CTU-13, Aposemat IoT-23, and EMBER, are available for intrusion detection research. These datasets encapsulate real-time network traffic and contemporary attack patterns, which may introduce novel complexities and research opportunities. Nevertheless, these datasets, distinguished by unique formats, structures, and characteristics, may necessitate distinct methodologies and techniques for processing and analysis. Moreover, certain datasets are not readily accessible to the public, potentially limiting their utility and reproducibility. Given these considerations, the KDDCUP'99 dataset is a classic and universally recognised resource for intrusion detection research. It is our belief that this research offers substantive insights and contributions to the realm of intrusion detection and the application of machine learning in fortifying network security. This dataset contains information about different types of connections to a computer network, such as normal connections and malicious attacks. The dataset has the following characteristics:  
* It has 41 features, such as duration, protocol type, service, source bytes, destination bytes, etc., that describe each connection. * It has 4.9 million records, of which 10% are available as a subset for training and testing purposes. * It has 23 types of attacks, such as denial of service, probing, user to root, remote to local, etc., representing different network intrusions. * It is based on a simulated military network environment that mimics real-world scenarios. The KDDCUP'99 dataset was chosen for this research because it offers a large and diverse set of data that covers various aspects of network activity and security. It also has a well-defined task and evaluation criteria that can be used to compare different methods and models. However, this dataset also has some limitations and challenges, such as:  
* It is outdated and does not reflect the current state of network traffic and attacks that are more complex and sophisticated. * It has some redundant and irrelevant records that may affect the quality and accuracy of the models. * It has imbalanced classes that may cause some models to be biased or overfitting. The main objective of this research is to study the effects of data poisoning attacks on several machine learning models commonly used for NIDSs. Data poisoning attacks are adversarial attacks that corrupt the training data of machine learning models, resulting in inaccurate or malicious predictions. The KDDCUP'99 dataset contains a large and diverse set of network connections with different types of intrusions. The dataset was created by processing the tcpdump portions of the 1998 DARPA Intrusion Detection System (IDS) Evaluation dataset, which was prepared and managed by MIT Lincoln Lab. The original raw data consisted of about four gigabytes of compressed binary tcpdump data from seven weeks of network traffic. These data were processed into about five million connection records, each corresponding to a single connection.