Machine Learning Techniques for Passive Network Inventory
نویسندگان
چکیده
منابع مشابه
Machine Learning and Knowledge Acquisition Techniques for Natural Resources Inventory
The knowledge on relationships between natural resources and their environmental conditions is needed for predictive mapping of natural resources (such as soils) and for mapping the susceptibility of natural hazards (such as landslides). This knowledge often exists in the form of human expertise or in raw data (point observations and map) forms. This workshop introduces the techniques for extra...
متن کاملMachine Learning Techniques for Network Intrusion Detection
Most of the currently available network security techniques are not able to cope with the dynamic and increasingly complex nature of cyber attacks on distributed computer systems. Therefore, an automated and adaptive defensive tool is imperative for computer networks. Alongside the existing prevention techniques such as encryption and firewalls, Intrusion Detection System (IDS) has established ...
متن کاملUsing Machine Learning Techniques for Advanced Passive Operating System Fingerprinting
TCP/IP fingerprinting is the active or passive collection of information usually extracted from a remote computer’s network stack. The combination of such information can be then used to infer the remote operating system (OS fingerprinting). OS fingerprinting is traditionally based on a database of “signatures”. A signature comprises several features (i.e., pairs attribute/value) extracted from...
متن کاملMachine Learning for NLP: Supervised learning techniques
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ....
متن کاملMachine Learning for NLP: Unsupervised learning techniques
• So far we have seen supervised learning (of classification): – learning based on a training set where labelling of instances represents the target (categorisation) function – classifier implements an approximation of the target funtion – outcome: a classification decision • Unsupervised learning: – learning based on unannotated instances; – outcome: a grouping of objects (instances and groups...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Network and Service Management
سال: 2010
ISSN: 1932-4537
DOI: 10.1109/tnsm.2010.1012.0352