A survey and analysis of intrusion detection models based on information security and object technology-cloud intrusion dataset

Abstract

Nowadays society, economy, and critical infrastructures have become principally dependent on computers, networks, and information technology solutions, on the other side, cyber-attacks are becoming more sophisticated and thus presenting increasing challenges in accurately detecting intrusions. Failure to prevent intrusions could compromise data integrity, confidentiality, and availability. Different detection methods are proposed to tackle computer security threats, which can be broadly classified into anomaly-based intrusion detection systems (AIDS) and signature-based intrusion detection systems (SIDS). One of the most preferred AIDS mechanisms is the machine learning-based approach which provides the most relevant results ever, but it still suffers from disadvantages like unrepresentative dataset, indeed, most of them were collected during a limited period of time, in some specific networks and mostly don't contain up-to-date data. Additionally, they are imbalanced and do not hold sufficient data for all types of attacks, especially new attack types. For this reason, upto-date datasets such as information security and object technology-cloud intrusion dataset (ISOT-CID) are very convenient to train predictive models on a cloud-based intrusion detection approach. The dataset has been collected over a sufficiently long period and involves several hours of attack data, culminating into a few terabytes. It is large and diverse enough to accommodate machine-learning studies.

Machine learning approach for intrusion detection system using dimensionality reduction

“…Achieving a minimum number of input variables in a dataset is referred to as dimensionality reduction. The drawback of dimensionality [25], is the idea of including extra features in a predictive modeling task which makes it harder to model. Still, these methods can be used in applied ML to simplify classification methods can be used in applied ML to simplify a classification or regression dataset so that a predictive model fits it better.…”

Hyperparameters optimization XGBoost for network intrusion detection using CSE-CIC-IDS 2018 dataset

“…They detect odd patterns or suspicious actions by utilizing machine learning and statistical methodologies, potentially identifying unforeseen risks. However, they may generate false positives and require ongoing calibration to reduce such warnings [8]- [12]. A network intrusion detection system (NIDS) is a critical component in identifying ongoing attacks by differentiating normal network traffic from malicious traffic [13].…”

Mitigating ransomware attacks through cyber threat intelligence and machine learning

“…This insidious form of malicious software effectively takes control of a computer system, encrypts files on the hard drive, or forces the computer to shut down, demanding a ransom in return for restoring normal functionality and obstructing user access to the system. This form of cybercrime has grown exponentially in recent years, targeting businesses, healthcare institutions, government agencies, and individuals [3]. The motivation behind ransomware attacks is often financial gain, and the consequences can be catastrophic, ranging from financial losses to reputational damage.…”