Intrusion Detection and Prevention System Using Futuristic Artificial Intelligence in Cyber Security (Paperback)

Idioma: inglés

Editorial: Eliva Press, 2026

9999342264 / 9789999342261

  • Tapa blanda
  • Nuevo
Ver todos los detalles

Librería: CitiRetail, Stevenage, Reino UnidoCitiRetail

Vendedor de 5 estrellas

Vendedor de AbeBooks desde 29 de junio de 2022

Ver los artículos de este vendedor
Tapa blanda

Condición: Nuevo

EUR 76,86

Envío por EUR 43,15 
Se envía de Reino Unido a Estados Unidos de America

Cantidad disponible: 1 disponibles

Añadir al carrito
Devoluciones gratuitas de 30 días

Descripción del artículo del vendedor

Paperback. In cyber security, where threats are always evolving, state-of-the-art protections for digital infrastructures are of the utmost importance. The growing sophistication and regularity of cyber assaults makes traditional intrusion detection and prevention methods inadequate in protecting networks and systems. The integration of state-of-the-art AI into intrusion detection and prevention systems (IDPS) has been a tremendous boon in the battle against existing and future cyber dangers. Artificial intelligence (AI)-driven systems may learn from patterns, adapt to new dangers, and autonomously respond to events, providing a dynamic layer of protection that traditional static systems cannot match. Integrating AI into IDPS allows for a shift from a reactive to a proactive and predictive approach. Systems powered by AI can distinguish anomalies, anticipate attacks, and react in real-time, in contrast to traditional systems that rely mostly on predefined signatures and rules. Systems may differentiate between safe and dangerous patterns of behavior with the use of machine learning algorithms, particularly supervised and unsupervised learning models. With the help of AI, intelligent detection and prevention systems can keep a close eye on system operations, user activities, and network traffic in real-time, searching for any signs of intrusion, no matter how subtle. A lower risk of injury results from these abilities since they drastically reduce the amount of time needed to identify threats. Artificial intelligence models of the future will train themselves using enormous amounts of data, both historical and real-time, to increase their intelligence and precision. Using RNNs and other deep learning models with CNNs is a crucial component in enhancing IDPS's detection capabilities. Complex data correlations and patterns may be exposed by these models, which are typically imperceptible to traditional systems and human analysts. The speed and size with which AI systems can process and analyze data makes it possible to quickly identify and eliminate even the most minor threats. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

N° de ref. del artículo 9789999342261

Título
Intrusion Detection and Prevention System Using Futuristic Artificial Intelligence in Cyber Security (Paperback)
Autor
Dileep Singh Kushwah
Editorial
Eliva Press
Año de publicación
2026
Estado
new
Encuadernación
Paperback
Idioma
inglés
ISBN 10
9999342264
ISBN 13
9789999342261

CitiRetail

Stevenage, Reino Unido

Vendedor de 5 estrellas

Vendedor de AbeBooks desde 29 de junio de 2022

Tarifas de envío de Reino Unido a Estados Unidos de America

ArtículoDe 7 a 14 días hábilesDe 7 a 60 días hábiles
Primer artículoEUR 43,15EUR 43,15
Los plazos de entrega los establecen los vendedores y varían según el transportista y la ubicación. Los pedidos que pasan por la aduana pueden sufrir retrasos y los compradores son responsables de los aranceles o tarifas asociadas. Los vendedores pueden ponerse en contacto con usted en relación con cargos adicionales para cubrir cualquier aumento en los costes de envío de los artículos.

Métodos de pago

  • Visa
  • Mastercard
  • American Express
  • Carte Bleue
  • Apple Pay
  • Google Pay

Descripción de la tienda

Online business

Información empresarial del vendedor

ABC BOOKS LIMITED

10 John Street
London, Reino Unido WC1N 2EB