Real‑Time Anomaly Detection Using Machine Learning: An

Investigative Study

Chennapragada Amarendra, Kamineni Sai Kamal, Challa Kireeti Vardhan,

Shaik Mohaseen and Belum Tirumala Reddy

Department of Advanced Computer Science and Engineering, Vignan's Foundation for Science, Technology & Research

(Deemed to be University), Vadlamudi, Guntur, Andhra Pradesh, India

Keywords: Anomaly Detection, Machin Learning, ARIMA, LSTM, Real Time Analysis.

Abstract: As networked systems are continuously evolving, it is imperative to establish automated solutions for

effective and efficient real-time anomaly detection for operational assurance and security. Traditional

techniques can fall short in dynamic environments, leading to the adoption of machine learning (ML) methods.

This paper investigates the performance of two models of ML in detecting anomalies in synthetic univariate

time-series datasets simulating network traffic: ARIMA (a statistical method), and LSTM (a deep-learning

model). Some advantages are, main metrics like accuracy or accuracy on learning, update to concept drift and

computational cost. Findings highlight LSTM’s superior capability at handling those non-stationary data and

seasonality challenges compared to ARIMA, although ARIMA is a possible path forward where resources

are limited. We hope that the proposed RM-ML framework can demonstrate a significant potential for real-

time monitoring of cyber threats in applications where ML can be utilized.

1 INTRODUCTION

Robust networking systems are the backbone of an

inter-connected world – the most industry-critical

enabling force to guarantee operational efficiency

and secure data intensity across multiple streams,

now and forever. These systems require ongoing

surveillance to detect anomalies like unexpected

spikes in traffic or abnormal packet activity that

might indicate security incidents, performance issues,

or impending failures. In this context, machine

learning has become a key technology, enabling the

imitation of human analytical abilities for on-

demand analysis of network metrics. These models

can learn patterns and predict future trends by

analysing timeseries data, as well as detect

anomalies. This study investigates the potential use of

an automated approach to detect anomalies in real-

time. We build our evaluation on top of a synthetic

univariate time-series dataset deliberately structured

to simulate realistic network trends, seasonality, noise

and imperfect anomalies, comparing the performance

of a statistical ARIMA model vs a deep learning

LSTM model. The study evaluates how well these

algorithms perform relative to accuracy, concept

drift, and their applicability in dynamic settings

without having access to pre-labelled data. By using

this comparative analysis, the project is intended to

identify the optimal method for scalable and efficient

network monitoring, thus offering a practical solution

to modern industry challenges.

2 LITERATURE REVIEW

2.1 Foundations of Anomaly Detection

Pioneering work by Chandola et al., established the

taxonomy of anomaly detection techniques,

categorizing approaches into statistical, machine

learning, and hybrid methods. Their survey

revealed that 78% of industrial applications still

relied on threshold-based methods in 2009, despite

known limitations in dynamic environments. Box

and Jenkins' ARIMA methodology Box et al.,

(2008) remains the gold standard for stationary

time-series analysis, though subsequent studies

have exposed its limitations in handling network

traffic's non-linear patterns.

398

Amarendra, C., Kamal, K. S., Vardhan, C. K., Mohaseen, S. and Reddy, B. T.

Realâ

STime Anomaly Detection Using Machine Learning: An Investigative Study.

DOI: 10.5220/0013883700004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 2, pages

398-402

ISBN: 978-989-758-777-1

2.2 Evolution of Time-Series

Approaches

Hyndman and Athanasopoulos (2018)

demonstrated that traditional statistical methods

like SARIMA achieve 82-89% accuracy on

seasonal data but require manual parameter tuning.

Gupta et al., (2014) extended this work through

their survey of temporal outlier detection,

highlighting ARIMA's computational efficiency (5-

10ms/prediction) compared to deep learning

approaches. Recent advancements by Malhotra et

al., (2016) introduced LSTM-based encoder-

decoder architectures, showing 12-15%

improvement in recall rates for multi-sensor data.

2.3 Deep Learning Breakthroughs

Hochreiter and Schmidhuber's LSTM

revolutionized sequential data processing with its

memory cells achieving 94.2% accuracy on

benchmark datasets. Subsequent work by Géron

demonstrated that properly tuned LSTMs could

reduce false positives by 40% compared to ARIMA

in network intrusion detection. However,

Schmidhuber (2015) cautioned about the "resource

paradox" - while LSTMs deliver superior accuracy,

they require 8-10× more computational resources

than statistical models.

2.4 Research Gaps

Our analysis identifies three unmet needs:

 Standardized benchmarking of

computational efficiency vs. detection

accuracy.

 Integrated frameworks for concept drift

adaptation in production systems.

 Quantifiable studies on model degradation

in continuous operation environments.

3 METHODOLOGY

3.1 Dataset Design

A synthetic univariate time-series dataset was

generated with:

 Trends: Linear and exponential functions.

 Seasonality: Sinusoidal patterns (e.g.,

daily/weekly cycles).

 Noise: Gaussian-distributed random values.

 Anomalies: Injected spikes, dips, and

contextual outliers.

3.2 Models

3.2.1 ARIMA

 Stationarity enforced via differencing (d=1).

 Parameters (p=2, q=1) selected via

ACF/PACF plots.

 Anomalies flagged outside 95%

confidence intervals.

3.2.2 LSTM

 Architecture: 1 hidden layer (64 units),

dropout (0.2).

 Training: Adam optimizer, MSE loss, sliding

windows (n=30).

 Anomalies detected via prediction error

thresholds.

3.3 Evaluation Metrics

 Accuracy, F1-score, latency, and

adaptability to concept drift.

4 RESULTS

4.1 ARIMA Model Performance

Figure 1: ARIMA Results – Actual vs. Predicted Values.

with Anomalies.

The ARIMA model demonstrated moderate

accuracy in detecting point anomalies but struggled

with seasonal patterns and rapid trend shifts.

Anomalies were flagged when observed values

Realâ

STime Anomaly Detection Using Machine Learning: An Investigative Study

399

deviated beyond the 95% confidence interval of

predictions. Figure 1 shows

ARIMA Results – Actual

vs. Predicted Values with Anomalies.

4.1.1 Key Observations:

• Strengths: Low computational cost (~5

ms per prediction), interpretable

parameters.

• Limitations: High false negatives for

contextual anomalies (e.g., gradual drifts).

4.2 LSTM Model Performance

The LSTM model outperformed ARIMA, capturing

long-term dependencies and seasonality.

Anomalies were identified via threshold-based

classification of prediction errors (MSE > 3σ). Figure

2 shows LSTM Results - Actual vs. Predicted

Values with Anomalies.

Figure 2: LSTM Results - Actual vs. Predicted Values with

Anomalies.

4.2.1 Key Observations:

• Strengths: High accuracy (94% F1-

score), robust to concept drift.

• Limitations: Higher latency (~50 ms per

prediction), requires GPU for training.

4.3 Quantitative Comparison

Insights:

• ARIMA is suitable for edge devices with

limited resources.

• LSTM is ideal for cloud-based systems

where accuracy outweighs latency.

Table 1: ARIMA vs. LSTM performance metrics.

Metric ARIMA LSTM Improvement

Accuracy 82% 94% +12%

F1-Score 0.78 0.91 −12%

Latency

(ms/data

oint

)

5 50 10×

slower

Concept

Drift

Adaptation

Low High Significant

5 DISCUSSION

5.1 Model Performance Analysis

• ARIMA:

o Pros: Interpretable, computationally

efficient (5ms/prediction), and effective

for stationary data.

o Cons: Struggled with seasonality (Fig.

1) and concept drift, yielding 18% false

positives.

• LSTM:

o Pros: Achieved 94% accuracy by

capturing non-linear patterns (Fig. 2)

and adapting to dynamic environments.

o Cons: Resource-intensive

(50ms/prediction) and required GPU

acceleration for training.

5.2 Comparative Advantages

• Real-Time Adaptability: oLSTM’s

recurrent architecture outperformed

ARIMA in handling concept drift (Table 1),

critical for evolving network threats.

• Scalability: o ARIMA’s simplicity suits

large-scale deployments, while LSTM’s

depth enhances precision in high-stakes

scenarios Table 1 shows ARIMA vs LSTM

Performance Metrics.

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COMMUNICATION, AND COMPUTING TECHNOLOGIES

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6 CONCLUSION AND FUTURE

WORK

6.1 Summary of Findings

This study demonstrated that:

 LSTM is superior for complex, non-

stationary data (e.g., network traffic with

seasonality).

 ARIMA remains viable for resource-

constrained, real-time applications.

6.2 Practical Implications

 Industry Adoption: LSTM integrates well

with cloud-based SIEM tools (e.g.,

Splunk). o ARIMA fits IoT devices with

limited compute power.

 Regulatory Compliance: Both models

provide auditable logs for compliance (e.g.,

GDPR, HIPAA)

6.3 Future Directions

 Hybrid Models: Combine ARIMA’s

efficiency with LSTM’s accuracy (e.g.,

ARIMALSTM ensembles).

 Edge Optimization: Develop lightweight

LSTM variants (e.g., Quantized LSTMs)

for IoT deployments.

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