The accurate classification of brain tumors based on magnetic resonance imaging (MRI) remains a persistent problem due to high intra-class diversity and subtle inter-class disparity in tumor shape. In this work, we introduce an innovative ensemble framework for tumor classification from axial T1-weighted contrast-enhanced MRI by integrating heterogeneous feature representations using a meta-learning-based intermediate fusion approach. The architecture consists of three encoding branches: a transfer learning path relying on a pre-trained MobileNetV2 for encoding anatomical priors, a residual dilated encoder for spatial continuity and long-range dependencies capture, and a multi-scale inception-based branch to gather features at different receptive fields. Instead of early or late fusion, the model performs intermediate fusion where the outputs from all three branches are concatenated and then further refined with a spatial attention-based meta-learner. This module re-weights the importance of features and improves discriminative representation before classification. A compact dense module is adopted to generate the final prediction for multi-class tumor subtype labeling. The model obtained classification accuracies of 96.4 % and 98.42 %, respectively, on Masoud Nickparvar's and Cheng-Jun brain tumor datasets, while having an AUC greater than 99 % for all tumor classes in both cases. The results demonstrate the effectiveness of ensemble fusion along with attention-based meta-learning for representing complex tumor characteristics, thereby providing a promising direction for generalizable brain tumor diagnosis.

Named Data Networking (NDN) is a promising future generation Internet architecture that addresses critical limitations of the current IP-based model, including scalability bottlenecks, high content retrieval latency, and reliance on single points of failure. By integrating in-network caching, NDN enables efficient content dissemination and improved network performance. However, existing caching strategies often suffer from inefficient cache utilization, redundant content storage, and excessive lookup overhead, limiting their effectiveness. To overcome these challenges, we propose SYNC-NDN, a hybrid caching strategy that optimizes both content lookup and placement by dynamically coordinating on-path and off-path routers. During content discovery, SYNC-NDN minimizes latency by avoiding redundant queries across intermediate routers. For content placement, it intelligently selects the most suitable router (either on-path or off-path) to maximize Cache Hit Ratio (CHR) while minimizing redundancy. Designed for low overhead, SYNC-NDN is particularly suited for resource-constrained environments such as IoT and edge networks. We evaluate SYNC-NDN across diverse real-world topologies, demonstrating its superiority over state-of-the-art NDN caching schemes in terms of CHR, latency reduction, and operational efficiency. The experimental results confirm its robustness in optimizing content delivery without introducing significant overhead.

Modern operating systems generate massive volumes of complex log data, making it challenging to promptly detect malicious activities and system anomalies. Traditional security systems are primarily designed to counter high-volume attacks but often fail to identify low-rate threats that blend into normal traffic patterns. Existing anomaly detection methods typically rely on feature-based models and struggle to capture the underlying behavioral patterns reflected in logs. To address this gap, we propose a behavior-based anomaly detection system that leverages log sequence analysis. By transforming log entries into unique log sequences and modeling normal system behavior, the system can identify subtle deviations indicative of malicious activity. Experimental results demonstrate that the proposed approach enables accurate, real-time detection.

Named Data Networking (NDN), a promising future Internet architecture, enhances network efficiency through in-network caching, where intermediate routers store content to reduce latency and optimize bandwidth utilization. However, existing simulators such as Icarus support only limited network topologies, restricting the evaluation of caching strategies under diverse conditions. To address this limitation, we develop a Topology Integration Module for Icarus (TIM4I) that enables the incorporation of real-world Internet topologies from multiple datasets, allowing comprehensive and scalable performance assessments of NDN caching schemes. Further, we propose a Trust-based Popularity-Aware Caching Strategy (TPACS) that inherently mitigates Cache Pollution Attacks (CPA) during the caching process. Unlike conventional methods that require separate detection mechanisms, TPACS integrates CPA prevention into the content placement phase by assigning a trust score based on content popularity, prefix reputation, and freshness. Only content with sufficient trust is cached, thereby preventing the storage of valid but unpopular data that causes pollution. This integrated approach eliminates additional detection overhead on routers while maintaining high cache efficiency and improved network security. Experimental evaluations using TIM4I demonstrate that TPACS achieves higher cache hit ratios, lower latency, and effective CPA mitigation across diverse real-world topologies.

The TCP/IP architecture has been the backbone of the Internet for decades, but its host-centric design is becoming less suitable for the data-centric communication demands of today. As the demand for efficient content distribution and retrieval grows, Named Data Networking (NDN) emerges as a promising alternative. NDN shifts the focus from host-centric to data-centric networking, with packets routed based on content names rather than IP addresses. A key feature of NDN is in-network caching, which attempts to reduce latency, alleviate network congestion and enhance content availability. However, the known NDN caching schemes do not consider the dynamic content demand that changes with respect to time and location. This causes the end users to encounter relatively higher content access latency. To address this challenge, in this paper, we propose a novel dynamic behaviour strategy that can be integrated into the known NDN caching schemes. This strategy can enable the NDN routers to make cooperative decisions and move the content copy to an edge router that requests the content most frequently. We comprehensively evaluate the performance of state-of-the-art NDN caching schemes with and without our proposed dynamic strategy using several real-world topologies. Our experimental results show that incorporating dynamic behaviour into these schemes leads to significantly better outcomes in terms of CHR, content latency, and path stretch. Specifically, the best improvements include a threefold increase in CHR, an 80% reduction in content access latency, and nearly a 45% decrease in path stretch. As an aside, we also develop a framework for the Icarus simulator to automate the process of performance assessment of different NDN caching schemes on a large number of real-world topologies.

The foundational concepts of the Internet were developed in the 1960s and 1970s with the goal of interconnecting hosts using the TCP/IP architecture. While this architecture has significantly impacted communication and commerce, it struggles to accommodate the Internet's vast user base and diverse applications. Named Data Network (NDN), a next-generation Internet architecture is designed to overcome the current TCP/IP based Internet architecture's limitations. NDN's basic operations make it resilient against several traditional DoS/DDoS attacks. However, NDN remains vulnerable to Interest Flooding Attack (IFA), a class of DoS attacks that can exhaust the routers’ as well as the producers’ resources to disrupt network functionality. To detect these attacks, researchers came up with a few approaches. However, existing detection techniques focus on specific IFA variants but struggle to detect other variants. To address this challenge, in this paper, we propose a statistical abnormality detection scheme to identify all variants of IFA. Additionally, we generate a comprehensive NDN traffic dataset through our experiments and use it to evaluate the performance of the detection scheme. The experimental results show that our scheme can detect all variants of IFA with high accuracy. Towards the end, we also present a sensitivity analysis study that shows the impact of varying a few parameters on the detection performance of the proposed scheme.

Named Data Network (NDN), a future Internet architecture is introduced to address the shortcomings of the current Internet architecture. NDN supports in-network caching to facilitate scalable content distribution and enhance overall network performance. However, the known NDN caching strategies suffer from a few common drawbacks, such as inefficient cache utilization, high content redundancy, and overhead due to lookup repetition. To address these issues, in this paper, we propose a novel caching strategy called ProxaDyn for efficient content lookup, placement, and replacement. During the content lookup phase, ProxaDyn interacts exclusively with the router responsible for caching a particular content. This eliminates interaction with other intermediate routers, thereby significantly reducing content access latency. For content placement, ProxaDyn strategically selects an on-path router based on content popularity. Popular content is placed in the cache of a router closer to the consumer, while less popular content is cached in a router away from the consumer. This approach significantly improves the cache hits and reduces the access latency. We test ProxaDyn over a diverse range of real-world network topologies. Using extensive experiments, we show that ProxaDyn could achieve significantly better results compared to the state-of-the-art NDN caching strategies.

TCP/IP protocol suite has served as the backbone of the Internet for many years, facilitating robust global communication. However, with the evolving Internet landscape, inherent limitations of this suite, such as inefficient content retrieval and constraints in mobility and multicast support, have become increasingly evident. To overcome these limitations, researchers developed the Named Data Networks (NDN) concept. NDN shifts the focus from IP addresses to data names and improves data retrieval by enabling in-network caching. This caching reduces latency and network congestion, making data access faster and more efficient. Lately, NDN has been found to be vulnerable to Interest Flooding Attacks (IFA), where attacks overwhelm intermediate routers with malicious Interest packets. This leads to resource exhaustion and service interruptions. To detect these attacks, researchers in the community proposed several rate-limiting mechanisms. However, these mechanisms cannot detect the newly disclosed induced IFA variants. In this paper, we propose a chi-square-based detection approach that can be deployed as the first line of defense to detect these newly disclosed attacks. The proposed approach compares a normal NDN traffic profile with the traffic profiles generated during the testing phase. If the profiles deviate significantly from each other, the proposed approach detects the presence of anomalous traffic. We conducted extensive experiments by considering various parameters to test the detection performance of the proposed approach and furnish the results. The results show that the approach can detect attacks in different scenarios with high accuracy.

Named Data Network (NDN) is the future-generation Internet architecture proposed to address the issues in the current Internet architecture (TCP/IP) such as high content access latency, single point of failure, etc. NDN supports in-network caching that significantly enhances the network performance and facilitates scalable content distribution. However, the state-of-the-art in-network caching approaches suffer from drawbacks such as high lookup repetition overhead, poor cache utilization, and high content redundancy. To overcome these issues, in this paper, we propose a new content retrieval and content placement approach for NDN. The proposed approach reduces the lookup repetition overhead by minimizing the number of router consultations required for content retrieval. Moreover, the proposed approach improves cache utilization and reduces content redundancy by optimally placing the content on the most suitable router. The experimental results show that this approach improves the overall performance of the NDN architecture in terms of both content access latency and Cache Hit Ratio (CHR). We also compare the performance of the proposed approach with state-of-the-art approaches in a real-world topology and show that it outperforms the previously known approaches.

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