Machine learning techniques, that are based on semantic analysis of behavioural attack patterns, have not been successfully implemented in cyber threat intelligence. This is because of the error prone and time-consuming manual process of deep learning solutions, which is commonly used for searching correlated cyber-attack tactics, techniques and procedures in cyber-attacks prediction techniques. The aim of this paper is to improve the prediction accuracy and the processing time of cyber-attacks prediction mechanisms by proposing enhanced Naïve Bayes posterior probability (ENBPP) algorithm. The proposed algorithm combines two functions; a modified version of Naïve Bayes posterior probability function and a modified risk assessment function. Combining these two functions will enhance the threat prediction accuracy and decrease the processing time. Five different datasets were used to obtain the results. Five different datasets containing 328,814 threat samples were used to obtain the processing time and the prediction accuracy results for the proposed solution. Results show that the proposed solution gives better prediction accuracy and processing time when different examination types and different scenarios are taken into consideration. The proposed solution provides a significant prediction accuracy improvement in threat analysis from 92–96% and decreases the average processing time from 0.043 to 0.028 s compared with the other method. The proposed solution successfully enhances the overall prediction accuracy and improves the processing time by solving the TTPs dependency and the prediction sets threshold problems. Thus, the proposed algorithm reaches a more reliable threat prediction solution.

基于行为攻击模式语义分析的机器学习技术尚未在网络威胁情报中成功实现。这是由于深度学习解决方案容易出错且耗时的手动过程，该过程通常用于搜索网络攻击预测技术中的相关网络攻击策略，技术和过程。本文的目的是通过提出增强的朴素贝叶斯后验概率（ENBPP）算法来提高网络攻击预测机制的预测准确性和处理时间。该算法结合了两个功能。朴素贝叶斯后验概率函数和风险评估函数的修改版本。结合这两个功能可以提高威胁预测的准确性，并减少处理时间。使用五个不同的数据集来获得结果。使用五个包含328,814个威胁样本的不同数据集来获得所提出解决方案的处理时间和预测准确性结果。结果表明，在考虑不同检查类型和不同场景的情况下，提出的解决方案具有较好的预测准确性和处理时间。所提出的解决方案与其他方法相比，可以将威胁分析的预测准确度从92–96％显着提高，并将平均处理时间从0.043 s减少至0.028 s。所提出的解决方案通过解决TTP依赖性和预测集阈值问题，成功地提高了整体预测精度并缩短了处理时间。从而，