Andere Kunden interessierten sich auch für
![Classification of Radar Targets Using Invariant Features]( "Classification of Radar Targets Using Invariant Features")
Classification of Radar Targets Using Invariant Features62,99 €![Theater Land Attack Cruise Missile Defense: Guarding the Back Door]( "Theater Land Attack Cruise Missile Defense: Guarding the Back Door")
Theater Land Attack Cruise Missile Defense: Guarding the Back Door62,99 €![Active Computer Network Defense: An Assessment]( "Active Computer Network Defense: An Assessment")
Active Computer Network Defense: An Assessment60,99 €![Battlespace Awareness for Network Defense: Legal Framework and Operational Requirements]( "Battlespace Awareness for Network Defense: Legal Framework and Operational Requirements")
Battlespace Awareness for Network Defense: Legal Framework and Operational Requirements61,99 €![Deceiving Adversary Network Scanning Efforts Using Host-Based Deception]( "Deceiving Adversary Network Scanning Efforts Using Host-Based Deception")
Deceiving Adversary Network Scanning Efforts Using Host-Based Deception61,99 €![Decision Factors for Cooperative Multiple Warhead Uav Target Classification and Attack with Control Applications - War College Series]( "Decision Factors for Cooperative Multiple Warhead Uav Target Classification and Attack with Control Applications - War College Series")
Decision Factors for Cooperative Multiple Warhead Uav Target Classification and Attack with Control Applications - War College Series27,99 €![Host-Based Systemic Network Obfuscation System for Windows]( "Host-Based Systemic Network Obfuscation System for Windows")
Host-Based Systemic Network Obfuscation System for Windows61,99 €
This work presents a hybrid network-host monitoring strategy, which fuses data from both the network and the host to recognize malware infections. This work focuses on three categories: Normal, Scanning, and Infected. The network-host sensor fusion is accomplished by extracting 248 features from network traffic using the Fullstats Network Feature generator and from the host using text mining, looking at the frequency of the 500 most common strings and analyzing them as word vectors. Improvements to detection performance are made by synergistically fusing network features obtained from IP packet flows and host features, obtained from text mining port, processor, logon information among others. In addition, the work compares three different machine learning algorithms and updates the script required to obtain network features. Hybrid method results outperformed host only classification by 31.7% and network only classification by 25%. The new approach also reduces the number of alerts while remaining accurate compared with the commercial IDS SNORT. These results make it such that even the most typical users could understand alert classification messages.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.