Post by account_disabled on Feb 12, 2024 6:13:51 GMT
With millions and billions of parameters, deep learning models can do many things: detect objects in images, recognize speech, generate text, and hide malware. One of the studies including that of the University of California found that Neural Network can embed malicious payloads without anti-malware enabled. Their malware concealment technique raises security concerns that have become a hot topic of discussion at machine learning and cybersecurity conferences. As deep learning becomes ingrained in the applications we use every day, the security community needs to think about ways to protect network users against future threats. Neural Network can hide malware (Malware) Related articles: What is Endpoint Security? Endpoint Security solution works remotely What is Malware? Can malware avoid protection software? What is zero trust? How to build a zero trust security model .
Concept of Neural Network Neural Network is a series of algorithms that attempt to recognize underlying relationships in a set of data through a process that mimics the way the human brain works. In this sense, neural networks refer to systems of neurons, which can be organic or artificial in nature. Neural networks can adapt to changing inputs so that the network produces the best possible results without redesigning Costa Rica Telemarketing Data the output criteria. The concept of Neural Network originating from artificial intelligence (AI) is quickly gaining popularity in the development of trading systems. Neural Network evaluates price data and finds opportunities to make trade decisions based on data analysis. Networks can distinguish subtle nonlinear interdependencies and patterns that other technical analysis methods cannot. 2. Hide malware in deep learning model Every deep learning model is composed of multiple layers of AI cells. Based on each type, each neuron has connections to all or some neurons on both the anterior and posterior layers.
The strength of these connections is determined by numerical parameters during training, as the Deep Learning model learns the task for which it has been designed. Large Neural Networks can include hundreds of millions or even billions of parameters. The main idea behind the Evil Model is to embed malware in the parameters of a neural network in a way that makes it invisible to malware scanners. This is a form of practice of hiding one part of information in another part. At the same time, Deep Learning when infected must perform its main task (e.g. image classification) as well as or almost as a secure model to avoid raising suspicion or making it useless. applied to its victims. Finally, the attacker must have a mechanism to deliver the infected model to target devices and extract malware from the parameters of the Neural Network.
Concept of Neural Network Neural Network is a series of algorithms that attempt to recognize underlying relationships in a set of data through a process that mimics the way the human brain works. In this sense, neural networks refer to systems of neurons, which can be organic or artificial in nature. Neural networks can adapt to changing inputs so that the network produces the best possible results without redesigning Costa Rica Telemarketing Data the output criteria. The concept of Neural Network originating from artificial intelligence (AI) is quickly gaining popularity in the development of trading systems. Neural Network evaluates price data and finds opportunities to make trade decisions based on data analysis. Networks can distinguish subtle nonlinear interdependencies and patterns that other technical analysis methods cannot. 2. Hide malware in deep learning model Every deep learning model is composed of multiple layers of AI cells. Based on each type, each neuron has connections to all or some neurons on both the anterior and posterior layers.
The strength of these connections is determined by numerical parameters during training, as the Deep Learning model learns the task for which it has been designed. Large Neural Networks can include hundreds of millions or even billions of parameters. The main idea behind the Evil Model is to embed malware in the parameters of a neural network in a way that makes it invisible to malware scanners. This is a form of practice of hiding one part of information in another part. At the same time, Deep Learning when infected must perform its main task (e.g. image classification) as well as or almost as a secure model to avoid raising suspicion or making it useless. applied to its victims. Finally, the attacker must have a mechanism to deliver the infected model to target devices and extract malware from the parameters of the Neural Network.