Collaborating Authors

End-To-End Anomaly Detection for Identifying Malicious Cyber Behavior through NLP-Based Log Embeddings Artificial Intelligence

Rule-based IDS (intrusion detection systems) are being replaced by more robust neural IDS, which demonstrate great potential in the field of Cybersecurity. However, these ML approaches continue to rely on ad-hoc feature engineering techniques, which lack the capacity to vectorize inputs in ways that are fully relevant to the discovery of anomalous cyber activity. We propose a deep end-to-end framework with NLP-inspired components for identifying potentially malicious behaviors on enterprise computer networks. We also demonstrate the efficacy of this technique on the recently released DARPA OpTC data set.

Machine Learning based Anomaly Detection for 5G Networks Machine Learning

Protecting the networks of tomorrow is set to be a challenging domain due to increasing cyber security threats and widening attack surfaces created by the Internet of Things (IoT), increased network heterogeneity, increased use of virtualisation technologies and distributed architectures. This paper proposes SDS (Software Defined Security) as a means to provide an automated, flexible and scalable network defence system. SDS will harness current advances in machine learning to design a CNN (Convolutional Neural Network) using NAS (Neural Architecture Search) to detect anomalous network traffic. SDS can be applied to an intrusion detection system to create a more proactive and end-to-end defence for a 5G network. To test this assumption, normal and anomalous network flows from a simulated environment have been collected and analyzed with a CNN. The results from this method are promising as the model has identified benign traffic with a 100% accuracy rate and anomalous traffic with a 96.4% detection rate. This demonstrates the effectiveness of network flow analysis for a variety of common malicious attacks and also provides a viable option for detection of encrypted malicious network traffic.

Cyber Criminals vs Robots


What happens when cyber criminals face robots? What happens when they use robots? How will offensive and defensive strategies of cybersecurity evolve as artificial intelligence continues to grow? Both artificial intelligence and cybersecurity have consistently landed in the top charts of fastest growing industries year after year¹². The 2 fields overlap in many areas and will undoubtedly continue to do so for years to come. For this article, I have narrowed my scope to a specific use case, intrusion detection. An Intrusion Detection System (IDS) is software that monitors a company's network for malicious activity. I dive into AI's role in Intrusion Detection Systems, code my own IDS using machine learning, and further demonstrate how it can be used to assist threat hunters.

A Deep Learning Approach to Fast, Format-Agnostic Detection of Malicious Web Content Machine Learning

Malicious web content is a serious problem on the Internet today. In this paper we propose a deep learning approach to detecting malevolent web pages. While past work on web content detection has relied on syntactic parsing or on emulation of HTML and Javascript to extract features, our approach operates directly on a language-agnostic stream of tokens extracted directly from static HTML files with a simple regular expression. This makes it fast enough to operate in high-frequency data contexts like firewalls and web proxies, and allows it to avoid the attack surface exposure of complex parsing and emulation code. Unlike well-known approaches such as bag-of-words models, which ignore spatial information, our neural network examines content at hierarchical spatial scales, allowing our model to capture locality and yielding superior accuracy compared to bag-of-words baselines. Our proposed architecture achieves a 97.5% detection rate at a 0.1% false positive rate, and classifies small-batched web pages at a rate of over 100 per second on commodity hardware. The speed and accuracy of our approach makes it appropriate for deployment to endpoints, firewalls, and web proxies.