Original paragraph (high similarity in Turnitin)
“Machine learning techniques have been widely used in intrusion detection systems due to their ability to automatically learn patterns from network traffic data. However, traditional machine learning models suffer from high false positive rates and poor generalization when dealing with unseen attack types.”
Why Turnitin flags this
The sentence structure, phrasing, and technical expressions are very common and closely match multiple published papers. Even if cited, this wording is too similar to existing sources.
Poor revision attempt (still high similarity)
“Machine learning methods are commonly applied in intrusion detection systems because they can automatically learn patterns from network traffic. However, conventional machine learning approaches have high false alarm rates and low generalization for new attacks.”
This still gets flagged because only a few words were changed while the structure and flow remain almost identical.
Correct revision (low similarity, acceptable)
Several studies indicate that data-driven intrusion detection approaches can extract meaningful behavioral patterns from network traffic without manual feature engineering. Despite this advantage, many existing models struggle to maintain robustness in real-world environments, particularly when encountering previously unseen attack behaviors, which limits their practical effectiveness (Author et al., Year).
Why this works
The idea is preserved, but the sentence structure, flow, and wording are completely different. The explanation is more analytical and contextual rather than copied. A proper citation is included, making the reuse of ideas ethical and acceptable.
Second example with a longer paragraph
Original paragraph (high similarity)
“Deep learning models outperform traditional methods in image classification by learning hierarchical feature representations from large datasets. Convolutional neural networks are especially effective due to their ability to capture spatial features.”
Correct rewritten version (low similarity)
Recent research demonstrates that advanced neural architectures achieve superior image recognition performance by progressively extracting low-level to high-level representations from extensive training data. In particular, convolution-based architectures are well suited for visual analysis tasks because they effectively model spatial correlations within images (Author et al., Year).
What changed
The paragraph structure was altered, the explanation was expanded slightly, and different academic phrasing was used. The idea is synthesized rather than rewritten sentence by sentence.
Key takeaway
To reduce Turnitin similarity, do not rewrite line by line. Understand the idea, step back, and explain it as if you are teaching it in your own words, then cite the source. This approach consistently brings similarity down while improving paper quality.