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Apply digital coding technology and badminton men's technical and tactical statistical analysis system to analyze men's doubles competition data, provide statistical results of the position and line of the hitting technique, perform relevant analysis of technical and tactical applications between the opponent and the opponent, and analyze historical events. In addition, the use of computer coding technology to analyze the data, for the athletes to formulate the battle plan before the game, provide effective basis for timely and targeted adjustment of technical and tactics, and make a comprehensive summary after the game.

Background The technical and tactical diagnosis of table tennis is extremely important in the preparation for competition which is complicated by an apparent nonlinear relationship between athletes’ performance and their sports quality. The neural network model provides a high nonlinear dynamic processing ability and fitting accuracy that may assist in the diagnosis of table tennis players’ technical and tactical skill. The main purpose of this study was to establish a technical and tactical diagnosis model of table tennis matches based on a neural network to analyze the influence of athletes’ techniques and tactics on the competition results. Methods A three-layer Back Propagation (BP) neural network model for table tennis match diagnosis were established. A Double Three-Phase evaluation method produced 30 indices that were closely related to winning table tennis matches. A data sample of 100 table tennis matches was used to establish the diagnostic model ( n  = 70) and evaluate the predictive ability of the model ( n  = 30). Results The technical and tactical diagnosis model of table tennis matches based on BP neural network had a high-level of prediction accuracy (up to 99.997%) and highly efficient in fitting ( R 2  = 0.99). Specifically, the technical and tactical diagnosis results indicated that the scoring rate of the fourth stroke of Harimoto had the greatest influence on the winning probability. Conclusion The technical and tactical diagnosis model of table tennis matches based on BP neural network was highly accurate and efficiently fit. It appears that the use of the model can calculate athletes’ technical and tactical indices and their influence on the probability of winning table tennis matches. This, in turn, can provide a valuable tool for formulating player’s targeted training plans.

To compare the muscular characteristics of “hit” and “miss” actions in roundhouse kicks among taekwondo athletes, and explore the similarities, differences, and implications for training, motion tests were conducted on ten taekwondo athletes using Noraxon32 and VICON. The results showed no significant differences ( p > 0.05) in integrated electromyography (EMG) during the initiation and kicking phases between “miss” and “hit” actions. However, during the retraction phase, significant differences ( p < 0.05) were observed in the left rectus femoris, left peroneus longus, right biceps femoris, right semitendinosus, and right tibialis anterior muscles. The tibialis anterior muscle of the swinging leg was activated first in the “hit” action, while the biceps femoris was activated first in the “miss” action. The supporting-side rectus femoris was activated first in the “hit” action, whereas it was the biceps femoris in the “miss” action. In both techniques, the gluteus maximus was the last muscle to be activated. The “miss” action had a longer cycle, and the duration of muscle work was longer than in the “hit” action. During the retraction phase of the front leg roundhouse kick, the muscles worked more than during the kicking phase, with the erector spinae and tibialis anterior being the core force-producing muscles in both techniques, characterized by high EMG values and long activation times. In the “miss” action, the thigh muscles drove the calf muscles, while the “hit” action exhibited the opposite pattern. “Hit” actions had a faster cycle compared to “miss,” with greater force generation in “miss.” The hip flexors and knee extensors of the kicking leg were the core force-producing muscles during the kicking process, determining the effectiveness and completion of the action.

The concept of body-education fusion integrates educational concepts and sports resources while focusing on theoretical knowledge and skills knowledge, which has become another new form of the development of sports and soccer education and teaching in the new era. This paper analyzes the innovation orientation of the technical and tactical teaching mode of soccer special students under the model of body-teaching fusion, combines the characteristics of soccer technical and tactical teaching, constructs the teaching idea, and divides the teaching content stage. Applying the evaluation of the soccer teaching effect, it constructs the evaluation system of the technical and tactical teaching effect of college students specializing in soccer with four first-level indexes, namely, athletic ability, healthy behavior, thinking development, and team cooperation. Using the hierarchical analysis method to calculate the weight of each index, the fuzzy comprehensive evaluation method is used to carry out a comprehensive evaluation of the technical and tactical teaching mode of soccer specialization students with the integration of body teaching. Combined with the results of the fuzzy comprehensive evaluation of the body-teaching fusion model, we analyze and verify the advantages of the application of the body-teaching fusion model in the technical and tactical teaching of soccer special students. The fuzzy evaluation results of the technical and tactical teaching mode of students in the integration of sports and education football accounted for 45.79%, 32.81%, 19.05%, 2.35%, and 0, respectively, in the expert evaluation grades of “excellent”, “good”, “average”, “passing” and “to be improved”, respectively, and the comprehensive score was 87.03 points, which was significantly better than the interactive teaching mode before the implementation of the concept of sports and education integration, and had certain teaching advantages.

Volleyball is a very strong confrontation, with a series of basic techniques, complex tactical routines of the collective project. In order to improve the technical and tactical level of volleyball match, it is necessary to study the basic situation, law and development trend of the game. As the technical and tactical analysis of volleyball matches requires high professionalism and relatively complex data, it is necessary to actively explore the computer-aided application strategy. Through the establishment of volleyball game skills, tactics analysis system, provide more comprehensive and effective after the game for the learners of the skills and tactics evaluation model, in order to make up for and improve the level of volleyball game.

Early disruption of Advanced Persistent Threat (APT) campaigns hinges on recognising the attackers’ tactics, techniques and procedures (TTPs), which remain stable even after individual indicators of compromise (IOC) are rotated. Most prior studies derive such patterns from vendor-curated cyber-threat-intelligence reports, sources that can reflect commercial or geopolitical bias. To reduce this bias, a corpus of 2,097 malware samples confidently attributed to ten established APT groups was assembled, and every sample was detonated in a high-fidelity sandbox. The sandbox generates lengthy, unstructured text traces that capture both static artefacts and dynamic behaviour. The contributions are fourfold: this bias-reduced malware–TTP corpus is published, LTDCT-TTPDBIO is introduced to turn raw sandbox logs into precise ATT&CK labels with low latency, and an extensive comparative evaluation is provided that links TTP prevalence to APT groups’ capabilities. These raw traces are then processed by LTDCT-TTPDBIO, a latent-topic model tuned with a Birch-inspired optimiser and paired with a random-forest classifier, which converts them into precise MITRE ATT&CK labels in about 1.45 min per sample. This latency is roughly 4.5 times shorter than the fastest baseline and six to eight times shorter than recent transformer- and graph-based approaches, yet the model still delivers the best detection quality. With an 80–20 train–test split, it reaches an accuracy of 95.33%, a precision of 97.32%, a recall of 94.61% and an F1-score of 95.65%; with a 70–30 split, the F1-score climbs to 95.78%. These figures outperform the eight baseline algorithms evaluated in this study, with the highest F1-score among them being 93.38%. The resulting structured behaviour ground-truth dataset quantifies how malware and tactics are distributed across the ten APT groups. It pinpoints the most frequently observed techniques together with their defensive implications. Efficient extraction of TTPs from raw sandbox text therefore offers a durable and bias-resistant foundation for proactive APT defence.

In an advanced and dynamic cyber threat environment, organizations need to yield more proactive methods to handle their cyber defenses. Cyber threat data known as Cyber Threat Intelligence (CTI) of previous incidents plays an important role by helping security analysts understand recent cyber threats and their mitigations. The mass of CTI is exponentially increasing, most of the content is textual which makes it difficult to analyze. The current CTI visualization tools do not provide effective visualizations. To address this issue, an exploratory data analysis of CTI reports is performed to dig-out and visualize interesting patterns of cyber threats which help security analysts to proactively mitigate vulnerabilities and timely predict cyber threats in their networks.

The increasing complexity and scale of cyber threats demand advanced, automated methodologies for extracting actionable cyber threat intelligence (CTI). The automated extraction of Tactics, Techniques, and Procedures (TTPs) from unstructured threat reports remains a challenging task, constrained by the scarcity of labeled data, severe class imbalance, semantic variability, and the complexity of multi-class, multi-label learning for fine-grained classification. To address these challenges, this work proposes the Threat Intelligence Extraction Framework (TIEF) designed to autonomously extract Indicators of Compromise (IOCs) from heterogeneous textual threat reports and represent them by the STIX 2.1 standard for standardized sharing. TIEF employs the DistilBERT Base-Uncased model as its backbone, achieving an F1 score of 0.933 for multi-label TTP classification, while operating with 40% fewer parameters than traditional BERT-base models and preserving 97% of their predictive performance. Distinguishing itself from existing methodologies such as TTPDrill, TTPHunter, and TCENet, TIEF incorporates a multi-label classification scheme capable of covering 560 MITRE ATT&CK classes comprising techniques and sub-techniques, thus facilitating a more granular and semantically precise characterization of adversarial behaviors. BERTopic modeling integration enabled the clustering of semantically similar textual segments and captured the variations in threat report narratives. By operationalizing sub-technique-level discrimination, TIEF contributes to context-aware automated threat detection.

In today’s cyber warfare realm, every stakeholder in cyberspace is becoming more potent by developing advanced cyber weapons. They have equipped with the most advanced malware and maintain a hidden attribution. The precocious cyber weapons, targeted and motivated with some specific intention are called as Advanced Persistent Threats (APT). Developing defense mechanisms and performing attribution analysis of such advanced attacks are extremely difficult due to the intricate design of attack vector and sophisticated malware employed with high stealth and evasive techniques. These attacks also include advanced zero-day and negative-day exploits and payloads. This paper provides a comprehensive survey on the evolution of advanced malware design paradigms, APT attack vector and its anatomy, APT attack Tactics, Techniques, and Procedures (TTP) and specific case studies on open-ended APT attacks. The survey covers a detailed discussion on APT attack phases and comparative study on threat life-cycle specification by various organizations. This work also addresses the APT attack attribution and countermeasures against these attacks from classical signature and heuristic based detection to modern machine learning and genetics based detection mechanisms along with sophisticated zero-day and negative day malware countermeasure by various techniques like monitoring of network traffic and DNS logs, moving target based defense, and attack graph based defenses. Furthermore, the survey addresses various research scopes in the domain of APT cyber-attacks.

This paper aims to evaluate the contribution quality of table tennis matches comprehensively and explore the ranking characteristics of evaluation results and the rationality of grading. Through the application of the documentation method, videos, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Rank Sum Ratio (RSR), the contribution quality evaluation index system of table tennis matches was established. After then, the technical and tactical performances of 38 matches between H (anonymous), who is currently highly concerned and active in the international table tennis world from 2018 to 2020 were comprehensively evaluated. According to research results, H had 8 matches with the C i value > 0.5 in serve rounds, 4 with the C i value > 0.5 in receive rounds, and 5 with the RSR value > 0.6 in the comprehensive strength. These findings were generally consistent with the final match results. Furthermore, Pearson Correlation showed that the three indicators were significantly correlated with competition performance (CP) ( P  < 0.01). Each race could be divided into four grades, and there was a very significant difference among them by variance test ( F  = 60.281, P  < 0.01). Meanwhile, SNK pairwise comparison between four grades had statistical significance ( P  < 0.05). Therefore, researchers could conclude that the combination of TOPSIS and RSR could objectively and accurately reflect the contribution quality of table tennis matches. This method could be promoted and applied in the competition performance evaluation of other net games.