Cache poisoning attacks in Vehicular Named Data Networking (V-NDN) pose a serious threat by injecting false content into the Content Store, compromising network integrity. This paper proposes a threshold-based reputation algorithm to detect and mitigate such attacks in V-NDN using ndnSIM with a V2V multi-hop topology of 31 nodes across three urban road segments. The algorithm assigns reputation values to all nodes, applying penalties for malicious behavior and blocking nodes below a threshold of 0.5. Two scenarios are evaluated: an attack scenario without mitigation and an attack scenario with the proposed reputation algorithm. Results show a Detection Rate (DR) of 75%, False Positive Rate (FPR) of 0%, and False Cache Poisoning Rate (FCP) of 5.87%, with Cache Hit Ratio (CHR) improving from 28.94% to 30.99% after mitigation, confirming the algorithm's effectiveness in detecting and mitigating cache poisoning attacks in dynamic vehicular network environments.
Zhikya Sekar Lutfi Purnomo, L. V. Yovita, Istikmal· International Seminar on Int...· 0 citations
The rapid growth of internet users has significantly increased data traffic, demanding efficient content distribution architectures. Named Data Networking (NDN), an implementation of Information-Centric Networking (ICN), addresses this through in-network caching at router nodes. However, existing NDN caching strategies tend to address either cache placement or cache replacement in isolation. Placement strategies such as PoolCache effectively eliminate intra-cluster content redundancy through cooperative caching, but rely on standard Least Recently Used (LRU) replacement. Replacement strategies such as Least Recently and Frequently Used (LRFU) improve cache utilization through Combined Recency-Frequency (CRF) scoring, but without collaborative placement coordination. This paper proposes an integrated caching strategy that combines PoolCache and LRFU to simultaneously address both challenges. PoolCache manages caches across neighboring nodes as a shared resource, assigning each content to exactly one node per cluster via consistent hashing without extensive inter-node communication. LRFU complements this by evicting content with the lowest CRF score, retaining content that is both recently and frequently accessed. The integration is implemented and evaluated in ndnSIM 2.7 under two scenarios: variation of Content Store (CS) deployment percentage (5%-100%) and consumer demand rate (5, 20, 50, and 100 pkt/s) with Zipf-Mandelbrot content distribution $(\alpha=0.7)$. Results show that PoolCache+LRFU consistently outperforms PoolCache+LRU across all evaluated conditions. Under the CS percentage scenario, PoolCache+LRFU achieves a hit ratio up to 0.3666 compared to 0.3574 for PoolCache+LRU at $\mathbf{C S} \boldsymbol{=} \mathbf{1 0 0} \boldsymbol{\%}$, with the largest gap at $\mathbf{C S} \boldsymbol{=} \mathbf{2 0} \boldsymbol{\%}(\mathbf{0. 1 7 4 5}$ vs. 0.1375). Under varying demand, PoolCache+LRFU maintains stable average retrieval latency of 0.0318 s at 100 pkt/s, whereas PoolCache+LRU saw a performance decline to 0.0470 seconds, representing a 32.3% increase. Content redundancy is effectively suppressed by PoolCache in both configurations.
Muhammad Farhan, L. V. Yovita, Istikmal· International Seminar on Int...· 0 citations
Smart building Internet of Things (IoT) deployments generate diverse sensor data with varying operational criticality and temporal characteristics, creating significant challenges for efficient content delivery in Named Data Networking (NDN) environments. Security camera feeds require prioritized cache treatment for real-time monitoring, while environmental sensor readings can tolerate lower service levels. Traditional NDN caching strategies employ uniform policies that fail to differentiate between these heterogeneous traffic patterns, treating all content equally regardless of operational importance. This paper presents a Priority-Freshness Aware Caching Strategy integrating three-tier priority differentiation with sensor-specific freshness evaluation for smart building environments, assigning high priority to security cameras (5-second freshness), medium to air quality monitors (10-second), and low to temperature sensors (30-second freshness). A multiplicative scoring mechanism combines priority levels with freshness status to guide cache admission and eviction decisions, evaluated using ndnSIM 2.7 across varying cache sizes (CS50-CS300) and user loads (30-200 users). Results demonstrate that at baseline configuration (CS100, 100 users), high-priority sensors achieve 64.98% cache hit ratio compared to 44.65% for low-priority sensors (45.5% relative improvement), with performance gaps widening from 10.39 to 21.72 percentage points as user load increases from 30 to 200 users.
Ratna Widya Ningrum, L. V. Yovita, Istikmal· International Seminar on Int...· 0 citations
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