Despite advancements in cybersecurity technologies and increased deployment of machine learning (ML) and deep learning (DL) in Security Operations Centers (SOCs), significant gaps remain in the detection and response mechanisms across various attack vectors. The following gaps were identified based on the critical review of existing literature and current industry practices:
1. High False Positives and False Negatives
Many existing detection systems, especially those relying on basic heuristics, rule-based filtering, or classical ML models, suffer from excessive false alarms. This overwhelms SOC analysts and increases response time, particularly in:
Spear-phishing detection, where content-based analysis is underutilized.
Web-based injection attack detection, where payloads can be obfuscated to evade simple signatures.
Trojan detection, where static analysis tools flag benign behavior due to lack of contextual awareness.
2. Limited Adaptability to Novel/Evasive Attacks
Traditional detection models are ill-equipped to handle:
Polymorphic Trojans that change their code structure.
Zero-day DDoS variants that don’t match any known signature.
Spear-phishing emails tailored for specific organizations with subtle modifications.
3. Lack of Deep Contextual Analysis
Many ML models emphasize superficial features (e.g., header metadata, traffic volume) rather than contextual patterns within the actual content (e.g., email body, payload structure). For example:
Transformer models are still underutilized for spear-phishing and injection payload analysis.
Lack of NLP-based tools in phishing detection limits semantic understanding.
4. Manual Feature Engineering Bottlenecks
Classical ML models often rely on manual extraction of features, which:
Requires domain expertise.
Is time-consuming and error-prone.
Limits scalability and adaptability in real-time SOC environments.
5. Imbalanced and Outdated Datasets
Datasets used for training are often imbalanced (e.g., more benign than malicious samples).
Many lack real-world diversity or fail to include emerging attack patterns.
This results in poor model generalization and low robustness.
6. Limited End-to-End Automation
Most SOC environments still rely on manual investigation and remediation, especially in detecting Trojans or DDoS attacks.
Lack of integration between detection, classification, reporting, and response pipelines.
7. Computational Overhead in Modern Models
While transformer-based models offer high accuracy, they are computationally expensive.
Few studies have focused on hybrid or optimized models that reduce inference time and GPU memory usage while preserving detection quality.