Multifamily malware models
نویسندگان
چکیده
منابع مشابه
Forecasting Metropolitan Multifamily Residential Construction
Despite their importance as investment vehicles and as housing, very little analysis of multifamily markets has been undertaken to date. The literature that has been produced has, with few exceptions discussed below, focused on the aggregate national market, or occasionally on the regional market. But housing markets are well known to be local and diverse. The purpose of this paper is to examin...
متن کاملMalware Models for Network and Service Management
Different kinds of malware like the botnets and the worms are a main threat on Internet for the current and future. Their efficiency to control systems is proved and we are investigating the malware mechanism that can be adapted to get an efficient and scalable management plane. Our work consists in modelling malware based network management and assessing its performance.
متن کاملAchieving High Energy Savings in Multifamily Properties
The key to achieving high energy savings (over 20% consistently) is offering a turn-key (“one-stop-shop”) approach to implementing deep retrofits. This has traditionally been the model utilized by Energy Service Companies (ESCOs) on large projects in the MUSH (Municipal, University, School and Hospital) market. Emerging, small firms have enhanced the ESCOs approach and now deploy it for multifa...
متن کاملAutomatic Detection of Malware-Generated Domains with Recurrent Neural Models
Modern malware families often rely on domain-generation algorithms (DGAs) to determine rendezvous points to their command-and-control server. Traditional defence strategies (such as blacklisting domains or IP addresses) are inadequate against such techniques due to the large and continuously changing list of domains produced by these algorithms. This paper demonstrates that a machine learning a...
متن کاملTranscend: Detecting Concept Drift in Malware Classification Models
Building machine learning models of malware behavior is widely accepted as a panacea towards effective malware classification. A crucial requirement for building sustainable learning models, though, is to train on a wide variety of malware samples. Unfortunately, malware evolves rapidly and it thus becomes hard—if not impossible—to generalize learning models to reflect future, previously-unseen...
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: Journal of Computer Virology and Hacking Techniques
سال: 2020
ISSN: 2263-8733
DOI: 10.1007/s11416-019-00345-8