Search results
Results from the WOW.Com Content Network
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
The use of the EICAR test string can be more versatile than straightforward detection: a file containing the EICAR test string can be compressed or archived, and then the antivirus software can be run to see whether it can detect the test string in the compressed file. Many of the AMTSO Feature Settings Checks [5] are based on the EICAR test ...
Depending on the type and variation in training data, machine learning can be roughly categorized into three frameworks: supervised learning, unsupervised learning, and reinforcement learning. Multiple instance learning (MIL) falls under the supervised learning framework, where every training instance has a label, either discrete or real valued ...
In machine learning (ML), a learning curve (or training curve) is a graphical representation that shows how a model's performance on a training set (and usually a validation set) changes with the number of training iterations (epochs) or the amount of training data. [1]
An intrusion detection system (IDS) is a device or software application that monitors a network or systems for malicious activity or policy violations. [1] Any intrusion activity or violation is typically either reported to an administrator or collected centrally using a security information and event management (SIEM) system.
HACS Penetration Testing Services typically strategically test the effectiveness of the organization's preventive and detective security measures employed to protect assets and data. As part of this service, certified ethical hackers typically conduct a simulated attack on a system, systems, applications or another target in the environment ...
In a classification task, the precision for a class is the number of true positives (i.e. the number of items correctly labelled as belonging to the positive class) divided by the total number of elements labelled as belonging to the positive class (i.e. the sum of true positives and false positives, which are items incorrectly labelled as belonging to the class).
According to the company, SONAR 3 is fine-tuned to better detect fake antivirus software and is better integrated with the network component. They advise: "In SONAR 3 we have further enhanced our integration with the network component in order to classify, convict, and remediate malware on the basis of its malicious network activity.