A High-Confidence Assessment Method for Network Alarm Logs Based on OOD Technology

doi:10.3969/j.issn.1671-1122.2026.04.010

Abstract

Abstract:

To address the issue of a large number of false positives generated by network probes, this paper proposed a high-confidence assessment method for network alarm logs based on OOD technology. This method optimized the alarm feature extraction strategy by constructing a multi-dimensional confidence interval encompassing distance, label consistency, and model score, and combining with BPE tokenization and lightweight models. It also designd a long-short-term iterative optimization mechanism for high-confidence samples to achieve low-overhead automated security operation support while ensuring the accuracy and interpretability of model judgment. Experimental results show that on real SQL injection alarm datasets, the number of parameters of this method is less than 1% of that of traditional deep learning models, the accuracy within the high-confidence interval reaches 0.973, and the sample coverage rate is 66%. Furthermore, the inherent iterative optimization mechanism of the proposed method enables the model to achieve an overall judgment accuracy of 0.965 on the full dataset with only one single iteration. This significantly remedies the deficiency in the judgment of samples falling outside the high-confidence interval in the initial state, and renders the method highly applicable to complex and dynamic cybersecurity operation scenarios.

Key words: alarm logs, OOD detection, confidence interval, attack judgment, automated security operations

CLC Number:

TP309

SHU Zhan, MA Yilan, NIE Kaifeng, LI Zongpeng. A High-Confidence Assessment Method for Network Alarm Logs Based on OOD Technology[J]. Netinfo Security, 2026, 26(4): 626-641.

Figures/Tables 9

References 44

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数据集	SQL 注入类别数 /种	样本数量
数据集	SQL 注入类别数 /种	样本总数/条	黑样本数量/条	白样本数量/条
数据集 1	370	10200	8160	2040
数据集 2	262	3050	2440	610
数据集 3	207	2980	2384	596
数据集 4	318	3020	2416	604
数据集 5	380	2950	2360	590

方法	指标1	指标2	指标3
本文方法	42.00%	8.81%	88.33%
ConjNorm	44.87%	11.10%	82.76%
ODIN	51.31%	10.35%	79.46%
Energy-Based	45.11%	10.29%	75.24%
MSP	82.33%	12.14%	76.28%
马氏距离	85.67%	12.27%	76.56%

采样迭代方法	置信区间内样本占比	区间内准确率	区间内精确率	区间内召回率
无迭代	47.14%	0.945	0.990	0.935
迭代（随机采样100条）	48.24%	0.946	0.991	0.935
迭代（本文方法采样100条）	50.74%	0.948	0.992	0.938
迭代（全部数据2980条）	53.96%	0.952	0.992	0.944

场景	一致性	距离	模型分数	全部数据准确率	置信区间占比	置信区间准确率
木马后门通信	0.4	0.90	0.83/0.27	0.983	96.84%	0.952
远程代码/ 命令执行	0.6	0.54	0.62/0.38	0.933	95.02%	0.954
未授权访问/ 越权访问	0.6	0.68	0.72/0.28	0.892	94.92%	0.937
内网隧道或敏感命令执行	0.6	0.80	0.70/0.30	0.938	96.64%	0.955
全场景	0.4	0.46	0.64/0.36	0.913	89.51%	0.946

方法	置信区间样本占比	区间内准确率	区间内精确率	区间内召回率
整体方法	88.33%	0.951	0.984	0.942
w/o 一致性	96.13%	0.914	0.944	0.921
w/o 距离	88.52%	0.950	0.984	0.940
w/o 模型分数	90.59%	0.949	0.981	0.940