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Computer vision and deep learning provide new ways to detect cyber threats

#artificialintelligence

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. The last decade's growing interest in deep learning was triggered by the proven capacity of neural networks in computer vision tasks. If you train a neural network with enough labeled photos of cats and dogs, it will be able to find recurring patterns in each category and classify unseen images with decent accuracy. What else can you do with an image classifier? In 2019, a group of cybersecurity researchers wondered if they could treat security threat detection as an image classification problem.


Computer vision and deep learning provide new ways to detect cyber threats

#artificialintelligence

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. The last decade's growing interest in deep learning was triggered by the proven capacity of neural networks in computer vision tasks. If you train a neural network with enough labeled photos of cats and dogs, it will be able to find recurring patterns in each category and classify unseen images with decent accuracy. What else can you do with an image classifier? In 2019, a group of cybersecurity researchers wondered if they could treat security threat detection as an image classification problem.


Machine learning technique detects phishing sites based on markup visualization

#artificialintelligence

Machine learning models trained on the visual representation of website code can help improve the accuracy and speed of detecting phishing websites. This is according to a paper (PDF) by security researchers at the University of Plymouth and the University of Portsmouth, UK. The researchers aim to address the shortcomings of existing detection methods, which are either too slow or not accurate enough. The technique developed by the researchers uses "binary visualization" libraries to transform the markup and code of web pages into images. Using this method, they created a dataset of legitimate and phishing images of websites.


Deep Reinforcement Learning for Detecting Malicious Websites

arXiv.org Machine Learning

Phishing is the simplest form of cybercrime with the objective of baiting people into giving away delicate information such as individually recognizable data, banking and credit card details, or even credentials and passwords. This type of simple yet most effective cyber-attack is usually launched through emails, phone calls, or instant messages. The credential or private data stolen are then used to get access to critical records of the victims and can result in extensive fraud and monetary loss. Hence, sending malicious messages to victims is a stepping stone of the phishing procedure. A \textit{phisher} usually setups a deceptive website, where the victims are conned into entering credentials and sensitive information. It is therefore important to detect these types of malicious websites before causing any harmful damages to victims. Inspired by the evolving nature of the phishing websites, this paper introduces a novel approach based on deep reinforcement learning to model and detect malicious URLs. The proposed model is capable of adapting to the dynamic behavior of the phishing websites and thus learn the features associated with phishing website detection.


Malware Squid: A Novel IoT Malware Traffic Analysis Framework using Convolutional Neural Network and Binary Visualisation

arXiv.org Artificial Intelligence

Internet of Things devices have seen a rapid growth and popularity in recent years with many more ordinary devices gaining network capability and becoming part of the ever-growing IoT network. With this exponential growth and the limitation of resources, it is becoming increasingly harder to protect against security threats such as malware due to its evolving faster than the defence mechanisms can handle with. The traditional security systems are not able to detect unknown malware as they use signature-based methods. In this paper, we aim to address this issue by introducing a novel IoT malware traffic analysis approach using neural network and binary visualisation. The prime motivation of the proposed approach is to faster detect and classify new malware (zero-day malware). The experiment results show that our method can satisfy the accuracy requirement of practical application.