The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Using Contextual Information to Identify Cyber-Attacks
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Due to the lack of semantic inference, IDSs cannot analyze semantic relationships between alerts, which makes it difficult for security experts to make correct decisions.”
From Towards enhanced cybersecurity in industrial control systems: a systematic review of context-based modeling, digital twins, and machine learning approaches (2025) · cites this paper as [38]
In the citing paper
“There are some important deficiencies in current intrusion detection systems. First, intrusion detection systems that cannot analyze contextual information make early detection difficult because they ignore the relationships between events.”
From Towards enhanced cybersecurity in industrial control systems: a systematic review of context-based modeling, digital twins, and machine learning approaches (2025) · cites this paper as [38]
In the citing paper
“Finally, unknown attacks cannot be detected effectively by current systems.”
From Towards enhanced cybersecurity in industrial control systems: a systematic review of context-based modeling, digital twins, and machine learning approaches (2025) · cites this paper as [38]
In the citing paper
“In addition, techniques that analyze data at a low level of abstraction lead to high false alarm rates because they do not take contextual factors into account.”
From Towards enhanced cybersecurity in industrial control systems: a systematic review of context-based modeling, digital twins, and machine learning approaches (2025) · cites this paper as [38]
In the citing paper
“Second, current systems cannot make predictions without knowing the current situation, and it is difficult to determine which types of attacks are possible without knowing the state of the system.”
From Towards enhanced cybersecurity in industrial control systems: a systematic review of context-based modeling, digital twins, and machine learning approaches (2025) · cites this paper as [38]
Citations
Cites
Towards a taxonomy of intrusion-detection systems
Debar, H., Dacier, M., & Wespi, A. (1999) · Computer Networks · doi:10.1016/s1389-1286(98)00017-6 · Cited by 829
A hybrid machine learning approach to network anomaly detection
Shon, T., & Moon, J. (2007) · Information Sciences · doi:10.1016/j.ins.2007.03.025 · Cited by 440
An Operational Definition of Context
Zimmermann, A., Lorenz, A., & Oppermann, R. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-74255-5_42 · Cited by 412
A revised taxonomy for intrusion-detection systems
Debar, H., Dacier, M., & Wespi, A. (2000) · Annales Des Télécommunications · doi:10.1007/bf02994844 · Cited by 359
Y-means: a clustering method for intrusion detection
Guan, Y., Ghorbani, A. A., & Belacel, N. (2004) · Conference proceedings - Canadian Conference on Electrical and Computer Engineering · doi:10.1109/ccece.2003.1226084 · Cited by 178
Intrusion Detection and Correlation
Kruegel, C., Valeur, F., & Vigna, G. (2005) · Kluwer Academic Publishers eBooks · doi:10.1007/b101493 · Cited by 167
A Contextual Anomaly Detection Approach to Discover Zero-Day Attacks
AlEroud, A. F., & Karabatis, G. (2012) · doi:10.1109/cybersecurity.2012.12 · Cited by 49
Self-citationA Generalized Feature Extraction Scheme to Detect 0-Day Attacks via IDS Alerts
Song, J., Takakura, H., & Kwon, Y. (2008) · IEEE/IPSJ International Symposium on Applications and the Internet (SAINT) · doi:10.1109/saint.2008.85 · Cited by 35
AIN'T misbehaving—A taxonomy of anti-intrusion techniques
Highland, H. J. (1995) · Computers & Security · doi:10.1016/0167-4048(96)81669-5 · Cited by 31
Toward Zero-Day Attack Identification Using Linear Data Transformation Techniques
AlEroud, A. F., & Karabatis, G. (2013) · doi:10.1109/sere.2013.16 · Cited by 25
Self-citation
Research works printed but not linked: 2
- Gross, T., Specht, M.: Awareness in context-aware information systems. In: Mensch & computer conference, vol. 1, pp. 173–182, Citeseer, Germany, (2001)
- AlEroud, A., Karabatis, G.: Discovering unknown cyber attacks using contextual misuse and anomaly detection. ASE Sci. J. 1(1), 106–120 (2013)
Cited by
Big data in cybersecurity: a survey of applications and future trends
Alani, M. M. (2021) · Journal of Reliable Intelligent Environments · doi:10.1007/s40860-020-00120-3 · Cited by 67
What makes a review a reliable rating in recommender systems?
Margaris, D., Vassilakis, C., & Spiliotopoulos, D. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102304 · Cited by 58
Information Fusion for Cyber-Security Analytics
Alsmadi, I. M., Karabatis, G., & AlEroud, A. F. (2016) · Studies in computational intelligence · doi:10.1007/978-3-319-44257-0 · Cited by 28
Self-citationBhardwaj, S., & Dave, M. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103521 · Cited by 17
Abraham, D., Erceylan, G., Gkioulos, V., & Houmb, S. H. (2025) · International Journal of Information Security · doi:10.1007/s10207-025-01158-1 · Cited by 10
Metadata
- Authors
- AlEroud, A., & Karabatis, G.
- Affiliations
- Yarmouk University (JO)
- University of Maryland, Baltimore County (US)
- Corresponding author
- AlEroud, Ahmed
- Venue
- Studies in Computational Intelligence
- Publisher
- Springer International Publishing
- Published
- 2016-10-22
- Pages
- 1-16
- ISSN
- 1860-949X (ISSN-L)
- 1860-9503
- OpenAlex
- W2533518312
- Cited by
- 12
- References
- 15
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 1 · 2021: 2 · 2020: 2 · 2018: 1 · 2017: 1 · 2016: 1
- FWCI
- 2.96
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Studies in computational intelligence (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Immune Systems Applications
- Keywords
- cyberattack detection, contextual information, context-aware prediction, network traffic analysis, vulnerability exploitation, dimensionality reduction, low computational overhead
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Using Contextual Information to Identify Cyber-Attacks. https://app.cochat.ai/papers/10.1007/978-3-319-44257-0_1
- The paper
- AlEroud, A., & Karabatis, G. (2017). Using Contextual Information to Identify Cyber-Attacks. Studies in Computational Intelligence. https://doi.org/10.1007/978-3-319-44257-0_1
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
