Machine Learning based intrusion detection systems (Research Proposal Sample)

How to use Machine Learning based intrusion detection systems (IDS) to solve problem of sheer volume of data generated by connected devices in IoT
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Introduction
One problem that can arise with the intrusion detection systems (IDS) with respect to Internet of Things (IoT) is the sheer volume of data that can be generated by the connected devices. Fog computing is used to create a decentralized intrusion detection system (IDS) that can identify DDoS assaults on the IoT network's memory pool (Jamalipour & Murali, 2021). Solving the problem of sheer volume of data requires use of data reduction techniques for purposes of filtering out unnecessary data, while focusing on the essential information. To detect and warn of potential threats, DDoS protection systems first establish a baseline for normal network traffic by examining historical data known as "traffic patterns." For example, an IDS could use a sampling as well as aggregation techniques to reduce the volume of data that requires analysis (Liu & Lai & Zhang, 2017). Besides, an IDS can use data classification algorithms to prioritize the most important data to be analyzed. This study will propose the use of anomaly detection algorithms to identify patterns of behavior that are indicative of malicious activity. In this proposal, the anomaly detection algorithms will be trained to recognize patterns of network traffic that are characteristic of a distributed denial of service (DDoS) attack to identify patterns of sensor data that are indicative of tampering or unauthorized access.
Aims and Objectives
The study aims to investigate how machine learning algorithms can be used to identify patterns of behavior that are indicative of malicious activity in IoT to enhance the security of IoT systems. The main objective of the study is to develop and trained anomaly detection algorithms to recognize patterns of network traffic that are characteristic of a distributed denial of service (DDoS) attack to identify patterns of sensor data that are indicative of tampering or unauthorized access in IoT systems.
Literature Review
Machine learning algorithms have gained a significant attention in the field of cyber security for their ability to identify patterns of behavior that are indicative of malicious activity. As noted by Jamalipour and Murali (2021), these algorithms have the potential to significantly enhance the security of IoT systems, as they can be used to analyze sheer volumes of data generated by connected devices in real-time and detect security threats that may not be visible to traditional security measures. As noted by Anwar et al. (2022), one of the vital advantages of machine learning algorithms for IoT security is their ability to learn and adapt over time. These algorithms can be trained on large datasets of normal as well as malicious behavior, and can then be used to identify patterns of behavior that are indicative of malicious activity in real-time. This allows the algorithm to continuously improve its performance and become more effective at detecting security threats.
Some examples of machine learning algorithms include; anomaly detection algorithms, classification algorithm and clustering algorithms. The anomaly detection algorithms tend to use statistical models for purposes of identifying patterns of behavior that are considered normal for the IoT systems (Punidha, Pavithra & Swathika, 2018). Any deviations from this normal behavior are often flagged as potentially suspicious. On the other hand, classification algorithms use pre-defined rules as well as patterns to classify data into different categories based on their relevance to the IoT systems. These algorithms can be used to identify known security threats, for example, malware and phishing attacks. The last category of algorithms is clustering algorithm, which group data points into clusters based on their similarity, and can be used to identify patterns of behavior that are indicative of malicious activity.
Methodology
Choosing anomaly detection algorithms because they are capable of using statistical models for purposes of identifying patterns of behavior that are considered normal for the IoT systems. Similarly, anomaly detection algorithms can automatically analyze large datasets and alert users of potential anomalies in real-time.
To enhance the security of IoT devices, anomaly detection algorithms will be used. The following approach will be followed;
* To establish a dataset of vulnerabilities that can be collected using penetration testing tools.
* Anomaly detection algorithms will be developed that allow the IoT system to quickly detect new vulnerabilities before they are exploited. The performances of these algorithms will be improved by training anomaly detection on prime data to build predicted models.
* Anomaly detection algorithms will be evaluated to develop solutions that can automatically detect vulnerabilities and protect against attacks in the IoT environment.
Scope and Limitations
As mentioned in the study, there are three machine learning algorithms, namely; anomaly detection algorithms, classification algorithm and clustering algorithms. However, the scope will only be limited to anomaly detection algorithms because of its capability of applying statistical models for identifying patterns of behav