Interpretable Unsupervised Anomaly Detection For RAN Cell Trace Analysis

Chawla, Ashima; Jacob, Paul; Feghhi, Saman; Rughwani, Devashish; Meer, Sven van der; Fallon, Sheila

doi:10.23919/cnsm50824.2020.9269108

Cited by 8 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To elaborate, if explanations become available for adversarial attacks, they become easier to defend against [30]. Additionally, explanations can support effective root cause analysis and localisation [31].…”

Section: Explainable Aimentioning

confidence: 99%

See 1 more Smart Citation

Open or not open: Are conventional radio access networks more secure and trustworthy than Open-RAN?

Klement¹,

Katzenbeisser²,

Ulitzsch³

et al. 2022

Preprint

View full text Add to dashboard Cite

The Open RAN architecture is a promising and future-oriented architecture. It is intended to open up the radio access network (RAN) and enable more innovation and competition in the market. This will lead to RANs for current 5G networks, but especially for future 6G networks, evolving from the current highly integrated, vendor-specific RAN architecture towards disaggregated architectures with open interfaces that will enable to better tailor RAN solutions to the requirements of 5G and 6G applications. However, the introduction of such an open architecture substantially broadens the attack possibilities when compared to conventional RANs. In the past, this has often led to negative headlines that in summary have associated Open RAN with faulty or inadequate security. In this paper, we analyze what components are involved in an Open RAN deployment, how to assess the current state of security, and what measures need to be taken to ensure secure operation.

show abstract

Section: Explainable Aimentioning

confidence: 99%

“…Vernetzt." Joint project 6G-RIC, project identification number: 16KISK020K and 16KISK0 [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35].…”

Section: Acknowledgmentmentioning

confidence: 99%

Open or not open: Are conventional radio access networks more secure and trustworthy than Open-RAN?

Klement¹,

Katzenbeisser²,

Ulitzsch³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In a premier attempt to apply XAI methods to generate explanations for AENN outputs, Antwarg et al [5] compared the performance of both SHAP and LIME and found that SHAP generates superior explanations. Additional publications demonstrated the usefulness of combining SHAP and AENNs [10,14,33]. Recently, two publications focused on applying XAI in the financial audit domain were published.…”

Section: Explainable Ai In Anomaly Detectionmentioning

confidence: 99%

RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations

Müller¹,

Schreyer²,

Sattarov³

et al. 2022

Preprint

View full text Add to dashboard Cite

Detecting accounting anomalies is a recurrent challenge in financial statement audits. Recently, novel methods derived from Deep-Learning (DL) have been proposed to audit the large volumes of a statement's underlying accounting records. However, due to their vast number of parameters, such models exhibit the drawback of being inherently opaque. At the same time, the concealing of a model's inner workings often hinders its real-world application. This observation holds particularly true in financial audits since auditors must reasonably explain and justify their audit decisions. Nowadays, various Explainable AI (XAI) techniques have been proposed to address this challenge, e.g., SHapley Additive exPlanations (SHAP). However, in unsupervised DL as often applied in financial audits, these methods explain the model output at the level of encoded variables. As a result, the explanations of Autoencoder Neural Networks (AENNs) are often hard to comprehend by human auditors. To mitigate this drawback, we propose Reconstruction Error SHapley Additive exPlanations Extension (RESHAPE), which explains the model output on an aggregated attribute-level. In addition, we introduce an evaluation framework to compare the versatility of XAI methods in auditing. Our experimental results show empirical evidence that RESHAPE results in versatile explanations compared to state-of-the-art baselines. We envision such attribute-level explanations as a necessary next step in the adoption of unsupervised DL techniques in financial auditing. CCS CONCEPTS• Computing methodologies → Machine learning; • Information systems → Enterprise resource planning.

show abstract

XcARet: XAI based Green Security Architecture for Resilient Open Radio Access Networks in 6G

Porambage,

Pinola,

Rumesh

et al. 2023

2023 Joint European Conference on Networks and Communications &Amp; 6G Summit (EuCNC/6G Summit)

View full text Add to dashboard Cite

Interpretable Unsupervised Anomaly Detection For RAN Cell Trace Analysis

Cited by 8 publications

References 13 publications

Open or not open: Are conventional radio access networks more secure and trustworthy than Open-RAN?

Open or not open: Are conventional radio access networks more secure and trustworthy than Open-RAN?

RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations

XcARet: XAI based Green Security Architecture for Resilient Open Radio Access Networks in 6G

Contact Info

Product

Resources

About