Adaptive machine learning‐based alarm reduction via edge computing for distributed intrusion detection systems

2019 ◽  
Vol 31 (19) ◽  
Author(s):  
Yu Wang ◽  
Weizhi Meng ◽  
Wenjuan Li ◽  
Zhe Liu ◽  
Yang Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Edge Computing ◽  
Intrusion Detection Systems ◽  
Detection Systems ◽  
Distributed Intrusion Detection ◽  
Alarm Reduction

scholarly journals Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT

2021 ◽  
Vol 1 (2) ◽  
pp. 252-273
Author(s):  
Pavlos Papadopoulos ◽  
Oliver Thornewill von Essen ◽  
Nikolaos Pitropakis ◽  
Christos Chrysoulas ◽  
Alexios Mylonas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection Systems ◽  
Learning Models ◽  
Detection Systems ◽  
Network Intrusion ◽  
Robust Model ◽  
Significant Probability ◽  
Adversarial Examples ◽  
Attack Surface

As the internet continues to be populated with new devices and emerging technologies, the attack surface grows exponentially. Technology is shifting towards a profit-driven Internet of Things market where security is an afterthought. Traditional defending approaches are no longer sufficient to detect both known and unknown attacks to high accuracy. Machine learning intrusion detection systems have proven their success in identifying unknown attacks with high precision. Nevertheless, machine learning models are also vulnerable to attacks. Adversarial examples can be used to evaluate the robustness of a designed model before it is deployed. Further, using adversarial examples is critical to creating a robust model designed for an adversarial environment. Our work evaluates both traditional machine learning and deep learning models’ robustness using the Bot-IoT dataset. Our methodology included two main approaches. First, label poisoning, used to cause incorrect classification by the model. Second, the fast gradient sign method, used to evade detection measures. The experiments demonstrated that an attacker could manipulate or circumvent detection with significant probability.

scholarly journals Ensemble-Based Online Machine Learning Algorithms for Network Intrusion Detection Systems Using Streaming Data

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 315
Author(s):  
Nathan Martindale ◽  
Muhammad Ismail ◽  
Douglas A. Talbert
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Intrusion Detection ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Intrusion Detection Systems ◽  
Network Intrusion Detection ◽  
Detection Systems ◽  
Network Intrusion ◽  
Network Intrusion Detection Systems

As new cyberattacks are launched against systems and networks on a daily basis, the ability for network intrusion detection systems to operate efficiently in the big data era has become critically important, particularly as more low-power Internet-of-Things (IoT) devices enter the market. This has motivated research in applying machine learning algorithms that can operate on streams of data, trained online or “live” on only a small amount of data kept in memory at a time, as opposed to the more classical approaches that are trained solely offline on all of the data at once. In this context, one important concept from machine learning for improving detection performance is the idea of “ensembles”, where a collection of machine learning algorithms are combined to compensate for their individual limitations and produce an overall superior algorithm. Unfortunately, existing research lacks proper performance comparison between homogeneous and heterogeneous online ensembles. Hence, this paper investigates several homogeneous and heterogeneous ensembles, proposes three novel online heterogeneous ensembles for intrusion detection, and compares their performance accuracy, run-time complexity, and response to concept drifts. Out of the proposed novel online ensembles, the heterogeneous ensemble consisting of an adaptive random forest of Hoeffding Trees combined with a Hoeffding Adaptive Tree performed the best, by dealing with concept drift in the most effective way. While this scheme is less accurate than a larger size adaptive random forest, it offered a marginally better run-time, which is beneficial for online training.

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