The DLAP framework leverages static analysis tools and deep learning models to create prompts that enhance LLMs. Evaluated on a dataset of over 40,000 examples from four major software projects, DLAP ...

Security breaches due to attacks by malicious software (malware) continue to escalate posing a major security concern in this digital age. With many computer users, corporations, and governments ...

Intrusion detection systems (IDSs) are deployed to detect anomalies in real time. They classify a network's incoming traffic as benign or anomalous (attack). An efficient and robust IDS in ...

Approach with unsupervised learning: without giving any label for normal or abnormal examples, the anomaly detection problem is formulated in another way: either by re-constructing the given input or ...

Cyber Security: Development of Network Intrusion Detection System (NIDS), with Machine Learning and Deep Learning (RNN) models, MERN web I/O System. The deployed project link is as follows. - MohdS ...

Discover the best deep learning software for training and deploying neural networks with powerful features and customizable options. Written by eWEEK content and product recommendations are ...

Deep learning has also changed how embedded vision is implemented. CNN graphs are not “programmed”—they are “trained” using a software framework and then mapped into the embedded vision hardware.

Within the realm of data science, deep learning frameworks are predominantly delivered via software found in the Python ecosystem. When looking at the options in the space, it may appear to some as a ...