Drug combination synergy is crucial in pharmacology, as it can enhance disease treatment efficacy or reduce drug resistance when administered in combination. Accurate prediction of drug combination synergy is vital for optimizing therapeutic regimens and improving treatment effectiveness. However, existing computational methods primarily rely on drug sequence and structural features, making them difficult to capture complex network relationships and global information-especially lacking the ability to perform cross-modal fusion. In this study, we proposed a method called SDCGDCP for predicting drug combination synergy. It processed drug molecular structures (graph structures and molecular fingerprints), target biological activity information and integrated cell line whole-genome expression profiles to construct multi-level combined node representations. A drug-cell line pair graph was accordingly generated. SDCGDCP updated node and edge representations via a GatedGCN module and derived five types of structural encodings (centrality, spatial, edge, Laplacian positional and node-similarity encodings), after which the graph language model Graphormer is employed to capture long-range node interactions. Finally, drug combination synergy was predicted using an MLP and SoftMax classifier. Extensive evaluations showed that SDCGDCP outperforms other state-of-the-art methods on the DrugCombDB dataset, achieving an AUROC of 0.923 and AUPRC of 0.885. Ablation experiments validated the effectiveness of each feature and encoding module. Meanwhile, we conducted case analyses on the predicted drug combination synergies. The results were supported by evidence from several pharmaceutical studies. This highlights potential of SDCGDCP in enhancing drug synergy prediction and optimizing combination therapies. The source code and data for SDCGDCP are available at https://github.com/Philosopher-Zhao/SDCGDCP.

Verifiable robust machine learning, as a new trend of ML security defense, enforces security properties (e.g., Lipschitzness, Monotonicity) on machine learning models and achieves satisfying accuracy-security trade-off. Such security properties identify a series of evasion strategies of ML security attackers and specify logical constraints on their effects on a classifier (e.g., the classifier is monotonically increasing along some feature dimensions). However, little has been done so far to understand the side effect of those security properties on the model privacy.

Cryptocurrency mining is a crucial operation in blockchains, and miners often join mining pools to increase their chances of earning rewards. However, the energy-intensive nature of PoW cryptocurrency mining has led to its ban in New York State of the United States, China, and India. As a result, mining pools, serving as a central hub for mining activities, have become prime targets for regulatory enforcement. Furthermore, cryptojacking malware refers to self-owned stealthy mining pools to evade detection techniques and conceal profit wallet addresses. However, no systematic research has been conducted to analyze it, largely due to a lack of full understanding of the protocol implementation, usage, and port distribution of the stealth mining pool.

Malicious activities on the Internet continue to grow in volume and damage, posing a serious risk to society. Malware with remote control capabilities is considered one of the most threatening malicious activities, as it can enable arbitrary types of cyber-attacks. As a countermeasure, many malware detection methods are proposed to identify malicious behaviours based on traffic characteristics. However, the emerging encryption and evasion techniques pose substantial barriers to the full exploitation of network information. This significantly impairs the effectiveness of existing malware detection methods relying on a singular type of characteristics. In this paper, we propose ST-Graph to resolve this issue. In addition to traditional stream attributes, ST-Graph explores spatial and temporal characteristics of network behaviours based on a graph representation learning algorithm and integrates all available information to boost the detection decision. To illustrate the effectiveness of ST-Graph, we evaluate it on two datasets. Experimental results demonstrate that ST-Graph outperforms state-of-the-art malware detection systems and also shows good performance in efficiency, generalizability, and robustness. Specifically, it achieves over 99% precision and recall, and its False Positive Rate is even two orders of magnitude lower than (nearly 0.02 times) that of baseline models. Meanwhile, the deployment of ST-Graph in two real network scenarios for around one year shows an outstanding efficiency with only 160 seconds time cost for 5-minute traffic in 1.7 Gbps bandwidth.