SearcharxivSearch

arXiv · 2604.22001

Can SOC Operators Explain their Decisions while Triaging Alarms? A Real-World Study

Abstract

Security Operations Centers (SOCs) are pivotal in modern enterprises. Tasked to monitor complex network environments constantly under attack, SOCs can be active 24/7 and can include hundreds of operators supported by state-of-the-art technologies. Abundant research has studied the internal processes of SOCs, highlighting their pros and cons, as well as the challenges faced by SOC analysts -- such as dealing with the overwhelming number of false alarms triggered by automated security mechanisms. In this context, we wonder: given that "someone" must triage the alarms, and that such triaging must be grounded on established knowledge or evidence-based reasoning, can SOC employees justify why a certain decision was taken while triaging alarms? Answering such a research question (RQ) can better guide future efforts. We hence tackle this RQs. First, via a systematic literature review across 257 research documents, we provide evidence that such RQ received limited attention so far. Then, we partner-up with a real-world SOC and carry out a field study (n=12) with SOC employees. We show them real alarms raised in their SOC, and inquire whether such alarms are indicative of true security problems or not. Then, we ask to explain their decision. We found that while most analysts were able to separate "true from false" alarms (the decision was correct in 83% of the cases), a correct justification was hardly provided (only 39% of the provided explanations reflected the actual root cause). Ultimately, our results highlight the need for decision-support systems that help SOC analysts not only make the right call -- but also understand and articulate why it is right.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jessica Moosmann, Irdin Pekaric, Giovanni Apruzzese. 2026-04-23. Can SOC Operators Explain their Decisions while Triaging Alarms? A Real-World Study. https://arxiv.org/abs/2604.22001

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Empirical Evaluation of Membership Inference Attacks on NLP Text Classifiers: A Baseline Study on SST-2

Membership inference attacks (MIAs) try to determine whether a specific record was used to train a model, a privacy risk that matters in natural language processing (NLP), where training data can contain sensitive user text. This paper presents a controlled benchmark of membership inference vulnerability for text classification on the GLUE SST-2 sentiment dataset. A TF-IDF + Logistic Regression pipeline and a fine-tuned DistilBERT classifier are compared under a loss-threshold MIA, with utility measured by development accuracy and macro F1. DistilBERT reached 0.9466 accuracy and 0.9460 macro F1 against 0.8756 and 0.8727 for Logistic Regression, yet both models leaked membership signal (Attack AUC 0.5615 and 0.5800, respectively). Two mitigations were tested. Stronger regularization reduced leakage for Logistic Regression at a visible utility cost, whereas fine-tuning DistilBERT for 2 epochs instead of 3 reduced leakage with negligible accuracy loss. Lightweight training adjustments can improve the privacy-utility trade-off without complex defenses.

cs.CR

Empirical Evaluation of Data Poisoning Attacks in Supervised Learning

Data poisoning corrupts training data to degrade a model or to plant attacker-controlled behavior. This study evaluates two representative training-time attacks, label flipping and backdoor poisoning, on MNIST and Fashion-MNIST with three baseline classifiers: Logistic Regression, Linear SVM, and Random Forest. Clean training is compared with poisoning rates of 5%, 10%, and 20% using clean-test accuracy, macro-precision, macro-recall, macro-F1, and, for backdoors, attack success rate. Label flipping caused clear degradation, largest for Logistic Regression and Linear SVM, while Random Forest stayed comparatively stable. Backdoor poisoning reached attack success rates from 0.9667 to 1.0000 on both datasets and all three models while often keeping clean-test performance near baseline. The results separate indiscriminate poisoning, which shows up in standard metrics, from targeted backdoor poisoning, which stays comparatively stealthy while embedding highly effective malicious behavior, and they support security-oriented evaluation beyond conventional clean-test metrics.

cs.CR

DeFiFusion: Combining Transaction Events with Smart Contracts to Detect Price Manipulation Attacks

Decentralized Finance (DeFi) has emerged as a rapidly growing blockchain-based financial service, where market transaction dynamics and underlying smart contract logic are intricately intertwined. This autonomous interplay, while eliminating centralized intermediaries, significantly expands the vulnerability surface of DeFi protocols to Price Manipulation Attacks (PMAs), which have already inflicted catastrophic financial losses. Despite their gravity, existing detection paradigms suffer from fundamental limitations. Transaction-centric methods lack awareness of contract execution semantics, making them prone to false positives under legitimate market volatility, while static contract analyses ignore real transaction behaviors and frequently report vulnerabilities that are infeasible to exploit in practice. We present DeFiFusion, a dual-modal PMA detection framework that closes this gap by jointly modeling transaction events and smart contract semantics within a unified pipeline. Our core insight is that PMA maliciousness emerges only from the interaction between transaction behaviors and the contract logic they exploit; neither signal suffices in isolation. Accordingly, we derive price-manipulation-aware event encoding for extracting fine-grained temporal and economic features tailored to manipulation patterns. We further introduce LLM-based contract semantic extraction to supply the execution-logic context that prior behavioral methods lack. To fuse these modalities, we propose a Dual-Modal Projection-Fusion Transformer with T5-style relative positional encoding, capturing the cyclic multi-stage execution structures that distinguish PMAs from benign market activity. Extensive experiments demonstrate that DeFiFusion consistently achieves state-of-the-art detection performance, effectively recalling 222 of the 225 PMA cases while maintaining a precision of 96.10%.

cs.CR