Masquerading attacks detection in mobile ad hoc networks
Open accessAbstract
Due to the lack of centralized identity management and the broadcast nature, wireless ad hoc networks are always a palatable target for masquerading attacks. The attackers can spoof identities of privileged legitimate users for various malicious reasons, such as to launch DoS or DDoS attacks, to access unauthorized information, and to evade the detection and accountability. In the current and limited literature, masquerading attacks are mostly counteracted by signal-strength-based detection systems. However, these schemes are mostly proposed to work for infrastructure-based IEEE 802.11 wireless networks using fixed access points, air monitors, or fixed anchor nodes, which are not suitable for the ad hoc architecture. In this paper, we propose a detection system for masquerading attacks without using fixed anchor nodes or air monitors. We develop an anomaly detection model based on the statistical significant testing for our masquerading detection system that takes into consideration the signal strength fluctuation. We conduct a test bed of Samsung Galaxy-based smartphones in order to analyze the real-world signal strength variation. We also plug the real-world signal strength variation in our model for the evaluation of the detection accuracy. We propose the received signal-strength-based masquerading attack detection scheme, which is carried out first by each node in its one-hop vicinity and then extended to five-hop proximity for broader detection scope and improved accuracy. The proposed scheme is evaluated using an NS-2 network simulator for detection accuracy in different environments. The results obtained indicate that our proposed scheme produces more than 90% true positives.
Integrity signals
Registry
Cites
Statistics
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?
A hardware-independent technique based on an adaptive anomaly detection model that takes signal strength variations into account can detect masquerade attacks.
“The authors present a hardware-independent technique for detecting masquerade attacks that is based on an adaptive anomaly detection model that takes signal strength variations into account.”
From Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions (2024) · cites this paper as [120]
An anomaly detection model using signal strength fluctuations can detect masquerading attacks, with malicious nodes detected by factoring maximum speed and time of reception.
“In [120], to address this problem, network specialists recommend an anomaly detection model that detects attacks using signal strength fluctuations. The malicious nodes are detected by factoring in the maximum speed of the nodes and the time of reception.”
From Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions (2024) · cites this paper as [120]
The citation lists haven’t been fetched yet
References 44 · Cited by 28
- Authors
- Abbas, S., Faisal, M., Ur Rahman, H., Khan, M. Z., Merabti, M., & Khan, A. U. R.
- Affiliations
- University of Sharjah (AE)
- University of Malakand (PK)
- Sohar University (OM)
- Venue
- IEEE Access
- Published
- 2018
- Volume
- 6
- Pages
- 55013-55025
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2894477425
- Cited by
- 28
- References
- 44
- Citations by year
- 2025: 1 · 2024: 1 · 2022: 8 · 2021: 5 · 2020: 11 · 2019: 2
- FWCI
- 2.79
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Mobile Ad Hoc Networks
- Physical Sciences › Computer Science › Computer Networks and Communications › Security in Wireless Sensor Networks
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Keywords
- masquerading attacks, mobile ad hoc networks, identity spoofing, received signal strength, anomaly detection, statistical significance testing, signal strength fluctuations, denial-of-service attacks, DDoS attacks, wireless network security
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review