Abdullah M. Alnajim

Network pharmacology examines drug action across interacting targets, pathways, and disease modules. Increasingly, however, the graph neural networks and knowledge-graph models used for this purpose are difficult to inspect. This critical narrative review considers feature attribution, attention, counterfactual and rule-based explanation, explainable graph neural networks, Bayesian and causal methods, and path-based knowledge-graph reasoning. We assess these approaches against three demands imposed by biological graphs: preservation of heterogeneous node and relation semantics, support for multi-hop reasoning, and concordance with evidence excluded from model training. Among the methods reviewed, none has yet been shown to satisfy all three demands consistently. A model-faithful explanation may still emphasize node degree, network hubs, or heavily studied entities instead of a disease-specific mechanism. To address this problem, we propose five evaluation domains: faithfulness, stability, external biological concordance, bias sensitivity, and translational utility, together with a six-item reporting standard. Both are conceptual proposals; they remain unweighted, uncalibrated, and unevaluated empirically. An exploratory, literature-based application to TxGNN, DDI-GCN, and MINERVA shows that transparency-related reporting is useful but incomplete. This exercise demonstrates how the criteria might be used; it is not an empirical evaluation or calibration of the framework. We did not retrain or rerun any model or conduct new quantitative benchmarking. The field still lacks a unified biomedical benchmark that evaluates prediction and explanation together under heterogeneous, cold-start, and provenance-aware graph settings. Progress will depend on task-matched evaluation, explicit treatment of uncertainty, and experiments designed to distinguish what a model uses from what biology actually does.

JAIT is a scientific open access journal which focuses on empirical research results and critical analysis of technology development, use, management and impacts in information technology.

JAIT is a scientific open access journal which focuses on empirical research results and critical analysis of technology development, use, management and impacts in information technology.

Distributed denial of service (DDoS) attacks are common network attacks that primarily target Internet of Things (IoT) devices. They are critical for emerging wireless services, especially for applications with limited latency. DDoS attacks pose significant risks to entrepreneurial businesses, preventing legitimate customers from accessing their websites. These attacks require intelligent analytics before processing service requests. Distributed denial of service (DDoS) attacks exploit vulnerabilities in IoT devices by launching multi-point distributed attacks. These attacks generate massive traffic that overwhelms the victim’s network, disrupting normal operations. The consequences of distributed denial of service (DDoS) attacks are typically more severe in software-defined networks (SDNs) than in traditional networks. The centralised architecture of these networks can exacerbate existing vulnerabilities, as these weaknesses may not be effectively addressed in this model. The preliminary objective for detecting and mitigating distributed denial of service (DDoS) attacks in software-defined networks (SDN) is to monitor traffic patterns and identify anomalies that indicate distributed denial of service (DDoS) attacks. It implements measures to counter the effects of DDoS attacks, and ensure network reliability and availability by leveraging the flexibility and programmability of SDN to adaptively respond to threats. The authors present a mechanism that leverages the OpenFlow and sFlow protocols to counter the threats posed by DDoS attacks. The results indicate that the proposed model effectively mitigates the negative effects of DDoS attacks in an SDN environment.

JAIT is a scientific open access journal which focuses on empirical research results and critical analysis of technology development, use, management and impacts in information technology.

Distributed Denial of Service (DDoS) is among the commonly used type of network attacks, particularly aimed at the IoT devices in the emerging future wireless services specially for strictly latency-limited applications. DDoS attacks pose serious threats for many entrepreneurial businesses as they can prevent legitimate customers from accessing network resources of corporate websites, demanding intelligent analytics prior to entertaining a service request. DDoS involves exploiting the vulnerability of IoT devices by launching distributed multi-point attacks to generate high traffic by flooding the network of victims. The consequences of DDoS attacks could be worst in any Software-Defined Networking (SDN) than in traditional networks because SDN is configured as centralised architecture, failing to address the weaknesses current paradigm. However, SDN can play a crucial role in mitigating the effects of DDoS attacks as it bears the features of easy programmability, manageability, flexibility, and scalability. In this manuscript preliminary framework to detect DDoS attacks by mitigating their threats in an SDN architecture is presented. The authors propose a defense mechanism that uses OpenFlow and sFlow to mitigate the effects of DDoS attacks. The initial results show that the proposed model can minimize efficiently the consequences of DDoS attacks in an SDN type of architectural paradigm.

Link prediction is a significant field in network science, which focuses on predicting the probability of the existence or formation of a link between nodes in a social network based on currently observed connections. The effectiveness of traditional algorithms can vary depending on the type of network, making some methods more suitable than others for specific scenarios. Recently, several efficient link prediction algorithms have been developed, demonstrating robust results in both prediction accuracy and interpretability. However, existing research has not clearly established the relationship between network characteristics and link creation mechanisms for community influence analysis and anomaly detection. The ability to predict complex networks with diverse features still requires further investigation. In light of this, we introduce a novel framework designed to combine the best features of different link prediction algorithms when applied to the network, with the aim of achieving more reliable predictions about how networks will evolve. According to the proposed framework, we first focus on the feature extraction stage. During this phase, we systematically identify and extract a comprehensive set of features from the network before moving onto the classification phase. Here, we utilize state-of-the-art ensemble learning models to assess and classify potential links within the network. By training our machine learning models on the extracted features, we can effectively predict whether a particular link is likely to form (positive link) or unlikely to form (negative link). The ML models were trained and evaluated using two datasets: Twitch and Facebook. Results: Additionally, we assessed their performance on these datasets by conducting specific preprocessing and hyperparameter tuning steps. The final prediction model achieves AUC values between 94% and 99.3%. This research contributes significantly to efforts aimed at enhancing link prediction in dynamic socisal network contexts, providing valuable insights into the effectiveness of different ML algorithms in predicting future connections and enhancing our understanding of network dynamics.