Graph Neural Network-Based Detection and Attribution of Advanced Persistent Threats
Language: English
Published by Nala Kala, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 38.19
Quantity: 2 available
Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - Advanced Persistent Threats (APTs) represent a significant cybersecurity challenge because they are typically characterized by prolonged, targeted, and coordinated activities across multiple stages of a cyberattack. Graph Neural Network-Based Detection and Attribution of Advanced Persistent Threats provides a focused technical examination of APT detection, attack attribution, graph-based cybersecurity analysis, graph neural networks, machine learning, and intelligent threat analysis. The book connects network security, artificial intelligence, graph representation learning, cyber threat intelligence, and anomaly detection within an integrated framework.The book introduces the fundamental characteristics of advanced persistent threats and examines the challenges involved in identifying sophisticated malicious activity within complex digital environments. APT campaigns can involve multiple hosts, accounts, processes, network connections, and attack stages, making it difficult to detect malicious behavior using isolated event analysis. Representing relationships between entities as graphs provides an alternative perspective for analyzing these interconnected activities.A central focus is placed on graph neural networks (GNNs) as machine learning models for cybersecurity analysis. Graph neural networks can learn representations from nodes, edges, and their surrounding neighborhoods, allowing relationships between entities to contribute to predictive analysis. The book examines general concepts associated with graph construction, node and edge representation, graph embeddings, message passing, model training, classification, and threat detection.The text further explores the detection of APT activity through graph-based representations. Security events can be modeled as relationships among users, devices, processes, IP addresses, domains, files, and other entities. Such representations can help capture behavioral and structural relationships that may be difficult to identify when analyzing individual events independently.APT attribution is examined as a related but distinct cybersecurity problem. Detection seeks to determine whether activity may be malicious, while attribution attempts to associate observed activity with a particular threat source, campaign, or behavioral profile. The book discusses attribution from a technical and analytical perspective, emphasizing evidence-based classification rather than assuming that attribution can always be determined with certainty.The book also considers feature learning in graph-based security systems. Traditional cybersecurity models often depend on manually engineered features, whereas graph neural networks can learn representations directly from graph structures and associated information. This can support analysis of complex relationships between entities and security events while reducing reliance on manually defined relational features.…
Seller Inventory # 9798240844669
- Title
- Graph Neural Network-Based Detection and Attribution of Advanced Persistent Threats
- Author
- Shon Kon
- Publisher
- Nala Kala
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798240844669
- Item weight
- 209 grams
- Dimensions
- 229x152x8 mm
Advanced Persistent Threats (APTs) represent a significant cybersecurity challenge because they are typically characterized by prolonged, targeted, and coordinated activities across multiple stages of a cyberattack. Graph Neural Network-Based Detection and Attribution of Advanced Persistent Threats provides a focused technical examination of APT detection, attack attribution, graph-based cybersecurity analysis, graph neural networks, machine learning, and intelligent threat analysis. The book connects network security, artificial intelligence, graph representation learning, cyber threat intelligence, and anomaly detection within an integrated framework.
The book introduces the fundamental characteristics of advanced persistent threats and examines the challenges involved in identifying sophisticated malicious activity within complex digital environments. APT campaigns can involve multiple hosts, accounts, processes, network connections, and attack stages, making it difficult to detect malicious behavior using isolated event analysis. Representing relationships between entities as graphs provides an alternative perspective for analyzing these interconnected activities.
A central focus is placed on graph neural networks (GNNs) as machine learning models for cybersecurity analysis. Graph neural networks can learn representations from nodes, edges, and their surrounding neighborhoods, allowing relationships between entities to contribute to predictive analysis. The book examines general concepts associated with graph construction, node and edge representation, graph embeddings, message passing, model training, classification, and threat detection.
The text further explores the detection of APT activity through graph-based representations. Security events can be modeled as relationships among users, devices, processes, IP addresses, domains, files, and other entities. Such representations can help capture behavioral and structural relationships that may be difficult to identify when analyzing individual events independently.
APT attribution is examined as a related but distinct cybersecurity problem. Detection seeks to determine whether activity may be malicious, while attribution attempts to associate observed activity with a particular threat source, campaign, or behavioral profile. The book discusses attribution from a technical and analytical perspective, emphasizing evidence-based classification rather than assuming that attribution can always be determined with certainty.
The book also considers feature learning in graph-based security systems. Traditional cybersecurity models often depend on manually engineered features, whereas graph neural networks can learn representations directly from graph structures and associated information. This can support analysis of complex relationships between entities and security events while reducing reliance on manually defined relational features.
"Synopsis" may belong to another edition of this title.
Shipping rates from Germany to U.S.A.
| Item | 7 to 10 business days | 5 to 7 business days |
|---|---|---|
| First item | £ 29.57 | £ 38.02 |
Payment methods
- Bank Wire Transfer
- Check
- Paypal
Store description
Das Unternehmen AHA-BUCH GmbH: Seit der Gründung von AHA-BUCH im Juli 2005 ist unser Hauptziel, zufriedenen Kunden so schnell und so preisgünstig wie möglich ihren Bücherwunsch zu erfüllen. Unsere Firma beschäftigt 16 Mitarbeiter, die nur ein Ziel kennen: den Kunden und seine Wünsche! Auf über 3700 m2 Fläche haben wir über 100.000 Bücher, Modernes Antiquariat und Spiele auf Lager.
Specialty
Kinderbücher & Kinderhör Casetten, German Books, Software, Natur & Tiere, Ratgeber, Sachbücher, Englische Bücher, Medizin & Gesundheit, Universität & StudiumSeller's business information
AHA-BUCH GmbH
Garlebsen 48
Einbeck, Germany 37574
Terms of sale
Imprint
Seller Info:
AHA-BUCH GmbH
represented by the managing director Christel Glass
Garlebsen 48
37574 Einbeck
Deutschland
Telefon: 055639996039
Telefax: 055639995974
E-Mail: abebooks@aha-buch.de
USt-IdNr.: DE261904229
registered at Commercial register Amtsgerichtes Göttingen
Handelsregisternummer HRB 200691
Alternative dispute resolution:
The European Commission provides a platform for out-of-court online dispute resolution (ODR platform), which can be accessed under https://ec.europa.eu/odr.
We have been a member of the "FairCommerce" initiative since 25.05.2018.
For more information, see www.fair-commerce.de.
Right of withdrawal
If you are a consumer you can withdraw from the contract in accordance with the following. Consumer means any natural person who is acting for purposes which are outside his trade, business, craft or profession.
Information regarding the right of withdrawal
Statutory right to withdraw
You have the right to withdraw from this contract within 14 days without giving any reason.
The withdrawal period will expire after 14 days from the day on which you acquire, or a third party other than the carrier and indicated by you acquires, physical possession of the last good or the last lot or piece.
To exercise the right of withdrawal, electronically fill in and submit a clear statement on our website, under "My Purchases" in "My Account". We will communicate to you an acknowledgement of receipt of such a withdrawal on a durable medium (e.g. by e-mail) without delay.
To meet the withdrawal deadline, it is sufficient for you to send your communication concerning your exercise of the right of withdrawal before the withdrawal period has expired.
Effects of withdrawal
If you withdraw from this contract, we will reimburse to you all payments received from you, including the costs of delivery (except for the supplementary costs arising if you chose a type of delivery other than the least expensive type of standard delivery offered by us).
We may make a deduction from the reimbursement for loss in value of any goods supplied, if the loss is the result of unnecessary handling by you.
We will make the reimbursement without undue delay, and not later than 14 days after the day on which we are informed about your decision to withdraw from this contract.
We will make the reimbursement using the same means of payment as you used for the initial transaction, unless you have expressly agreed otherwise; in any event, you will not incur any fees as a result of such reimbursement.
We may withhold reimbursement until we have received the goods back, or you have supplied evidence of having sent back the goods, whichever is the earliest.
You shall send back the goods or hand them over to AHA-BUCH GmbH, Einbeck, Germany, without undue delay and in any event not later than 14 days from the day on which you communicate your withdrawal from this contract to us. The deadline is met if you send back the goods before the period of 14 days has expired. You will have to bear the direct cost of returning the goods. You are only liable for any diminished value of the goods resulting from the handling other than what is necessary to establish the nature, characteristics and functioning of the goods.
Exceptions to the right of withdrawal
The right of withdrawal does not apply to:
- The delivery of newspapers, journals or magazines with the exception of subscription contracts; and
- The supply of digital content which is not supplied on a tangible medium (e.g. on a CD or DVD) if you accepted when you placed your order that we could start to deliver it, and that you could not withdraw once delivery had started.
Shipping terms
We ship your order after we received them
for articles on hand latest 24 hours,
for articles with overnight supply latest 48 hours.
In case we need to order an article from our supplier our dispatch time depends on the reception date of the articles, but the articles will be shipped on the same day.
Our goal is to send the ordered articles in the fastest, but also most efficient and secure way to our customers.