Pending Interest Table Research Articles

Distance-based dynamic caching and replacement strategy in NDN-IoT networks

The amount of data on the Internet has risen noticeably due to the exponential increase in the number of Internet of Things (IoT) devices. Recently, Information-Centric Networking (ICN) is acknowledged as a suitable framework for creating IoT network protocols. One notable feature of ICN is in-network caching, which stores the data packets on intermediate routers/nodes that can be accessed by the subscribers when required. This work proposes a novel Distance-based dynamic caching and replacement strategy (DBDCRS) which involves a light weight calculation that utilizes the request aggregation to find the request weight of the requests stored in Pending interest table (PIT) and node weight of a node based on the distance from the subscriber and IoT publisher to accomplish the caching decision. To eliminate the data redundancy, the scheme caches a particular content on a single node along the specific interest-data path. Additionally, a custom cache replacement policy is introduced which retains the essential contents in cache. Simulation results show that our proposed DBDCRS scheme outperform other in-network cache placement schemes in terms of cache hit ratio, server hit reduction ratio, average response latency, and energy consumption.

Caching Method for Information-Centric Ad Hoc Networks Based on Content Popularity and Node Centrality

In recent years, most internet communications have focused on accessing content such as video, web services, and audio. Conversely, traditional Internet communications are inefficient because they are primarily designed for data transfer between hosts. In response, Information-Centric Networking (ICN) has emerged as a content-oriented networking model. The impact of ICN in reducing the location dependency of data and its high compatibility with ad hoc networks has led to research on realizing Information-Centric ad hoc Networks (ICANET). There has also been extensive research into caching content in the network, which is one of the features of ICN. In static networks, methods have been proposed to cache highly popular content in nodes that are more likely to be used for shortest paths. However, in dynamic networks, content with high popularity should be cached on nodes that are more likely to reach all nodes, as missing nodes need to be taken into account. In this study, we propose a cache control scheme for content caching in ICANET that utilizes both content popularity and the closeness centrality of nodes within the ad hoc network as indicators. To realise the proposed method, a new packet flow based on the Pending Interest Table (PIT) and Content Store (CS) was implemented in the forwarding strategy of ICN. The experiments used ndnSIM, a protocol implementation of NDN based on Network Simulator3, which is widely used in wireless network research. The experimental results showed that the cache hit rate could be increased by up to 4.5% in situations with low content bias. In the same situation, the response delay was also reduced by up to 28.3%.

Galor: Global view assisted localized fine-grained routing for LEO satellite networks

Low Earth Orbit (LEO) satellite networks have been expected to provide global coverage for Internet services with immediacy requirements. The dynamics of an LEO satellite network topology induce the challenges of achieving efficient content retrieval. This article takes an initial step toward achieving efficient content retrieval in LEO satellite networks from the routing perspective. We start with investigating the topology characteristics of LEO satellite networks in terms of the deterministic neighbor relation and the intermittent inter-satellite links. We then propose a Global view Assisted Localized fine-grained Routing (GALOR), which is an information-centric routing mechanism customized to LEO satellite networks. Specifically, GALOR disseminates the link state within a predefined range instead of the entire constellation, incurring less convergence time and control overhead. Therefore, GALOR can calculate the routing table (to guide interest forwarding) based on the local link state and the global neighbor relations. Moreover, GALOR improves the forwarding method of the information-centric routing by reconstructing a failed Pending Interest Table (PIT) entries in response to occasional link failures. Our packet-level experiments show that GALOR outperforms state-of-the-art mechanisms (up to 103.4%) in terms of average packet delivery ratio in content-sharing.

Evaluating pending interest table performance under the collusive interest flooding attack in named data networks

Evaluating pending interest table performance under the collusive interest flooding attack in named data networks

An efficient content replacement policy to retain essential content in information-centric networking based internet of things network

In the current scenario, TCP/IP is widely used in the Internet of Things (IoT) for disseminating data, addressing, and controlling traffic. However, the exponential growth of IoT devices has incurred the TCP/IP-based network from mismanagement due to heavy traffic, address space scarcity, and low efficiency. In the last few years, Information-centric networking (ICN) has emerged as an excellent choice to address the above issues. One of the important characteristics of ICN is In-network Caching, in which contents are cached at the intermediate nodes. Most in-network caching strategies use traditional replacement policies such as First in first out, least recently used, and least frequently used, ignoring the essential content properties, eventually evicting the vital contents. This work introduces a novel content replacement scheme that proactively records the number of requests and access time of the content in the Pending interest table (PIT) and considers essential content attributes, e.g., number of requests, remaining lifetime, and upcoming request waiting time, before replacement to retain vital contents in the cache. The simulations are performed on the ndnSIM framework of NS-3 to evaluate the performance of our proposed replacement scheme with other widely used benchmark replacement schemes and tested under six different caching strategies. The experimental finding indicates that, on average, the proposed replacement scheme shows 28.94% and 27.49% increase in cache hit ratio and server hit reduction ratio while 22.30% and 30.64% reduction in response delay and energy consumption compared to NDN-FIFO, NDN-LRU, and NDN-LRFU.

Dimensioning the pending interest table in content-centric networks

In chunk-based interest-driven content-centric networks, each interest packet forwarded upstream by a node on a face implies the return of at most one data chunk to the node from that face shortly after. As a consequence, the congestion of the downstream transmission buffer in the data path of the node is highly correlated with the occupancy of the pending interest table (PIT). Therefore a systematic study and analysis of the PIT occupancy are of paramount importance to understanding congestion in CCN. In particular, in this paper, we propose an analytical model to estimate the PIT occupancy distribution via a continuous-time Markov chain (CTMC) model that considers the effects of interest blocking, interest timeout and retries. To validate our model and assumptions, we invoke simulation with realistic traffic streams and show how the filtering effects of caching and interest aggregation make the Markov assumption reasonable in the nodes that are the most susceptible to experiencing congestion. To solve the model numerically, we use two alternative approximations, state space truncation and state aggregation, and then give some numerical results to demonstrate the accuracy of our approximations.

A Machine Learning-Based Interest Flooding Attack Detection System in Vehicular Named Data Networking

A vehicular ad hoc network (VANET) has significantly improved transportation efficiency with efficient traffic management, driving safety, and delivering emergency messages. However, existing IP-based VANETs encounter numerous challenges, like security, mobility, caching, and routing. To cope with these limitations, named data networking (NDN) has gained significant attention as an alternative solution to TCP/IP in VANET. NDN offers promising features, like intermittent connectivity support, named-based routing, and in-network content caching. Nevertheless, NDN in VANET is vulnerable to a variety of attacks. On top of attacks, an interest flooding attack (IFA) is one of the most critical attacks. The IFA targets intermediate nodes with a storm of unsatisfying interest requests and saturates network resources such as the Pending Interest Table (PIT). Unlike traditional rule-based statistical approaches, this study detects and prevents attacker vehicles by exploiting a machine learning (ML) binary classification system at roadside units (RSUs). In this connection, we employed and compared the accuracy of five (5) ML classifiers: logistic regression (LR), decision tree (DT), K-nearest neighbor (KNN), random forest (RF), and Gaussian naïve Bayes (GNB) on a publicly available dataset implemented on the ndnSIM simulator. The experimental results demonstrate that the RF classifier achieved the highest accuracy (94%) in detecting IFA vehicles. On the other hand, we evaluated an attack prevention system on Python that enables intermediate vehicles to accept or reject interest requests based on the legitimacy of vehicles. Thus, our proposed IFA detection technique contributes to detecting and preventing attacker vehicles from compromising the network resources.

An Efficient Pending Interest Table Content Search In NDN through Stable Bloom Filter

AbstractNamed Data Networking (NDN) has gained importance in today’s era due to a paradigm shift in the Internet usage pattern which revolves around the content rather than the respective host addresses. Three important data structures in NDN are Content Store (CS), Pending Interest Table (PIT) and Forwarding Information Base (FIB). The search time of PIT is quite high since its size grows with the addition of new content names, and the Interest packets which are not served by CS are searched in millions of existing entries in the PIT. Hence, lookup time can be improved if, instead of checking all the available entries, initial scanning is done to determine whether the required content name exists in the PIT or not. In this paper, we propose a Stable Bloom Filter (SBF) based PIT called S-PIT, to minimize the PIT search time by identifying the existence of query content through SBF. The various experiments performed show that S-PIT outperforms existing data structures in terms of memory consumption, content insertion time, average search time and false positive rate.

MIA-NDN: Microservice-Centric Interest Aggregation in Named Data Networking.

The named data networking (NDN)-based microservice-centric in-network computation poses various challenges in terms of interest aggregation and pending interest table (PIT) lifetime management. A same-named microservice-centric interest packet may have a different number of input parameters with nonidentical input values. In addition, the same-named interest packet with the same number of parameters may have different corresponding parameter values. The vanilla NDN request aggregation (based on the interest name, while ignoring the input parameters count and/or their corresponding values) may result in false aggregation. Moreover, the microservice-centric requested computations may fail to accomplish in the default 4s PIT timer due to the input size. To address these challenges, this paper presents MIA-NDN: microservice-centric interest aggregation in named data networking. We designed microservice-centric interest-naming to enable name-based communication. MIA-NDN develops a robust interest aggregation mechanism that not only performs the interest aggregation based on the interest name but also considers the input parameter counts and their corresponding values in the interest aggregation process to avoid false packet aggregations. A dynamic PIT timer mechanism based on input size was devised that avoids the PIT entry losses if the execution time exceeds the default PIT timer value to avoid computation losses and uphold the application quality of service (QoS). Extensive software-based simulations confirm that the MIA-NDN outperforms the benchmark scheme in terms of microservice-centric interest aggregation, microservice satisfaction rate, and communication overhead.

An information-centric networking architecture with small routing tables

The basic design of the Named Data Networking (NDN) architecture is shown to incur problems, in that Interests (content requests) may go unanswered even if content is available in the network, and Pending Interest Tables (PIT) are shown to provide limited performance benefits in the presence of in- network caching. A new approach to content-centric networking is introduced that eliminates the need to maintain PITs while providing the benefits sought by NDN. Content-Centric Networking with Data Answer Routing Table (CCN-DART) replaces PITs with Data Answer Routing Tables (DARTs) to forward Interests that do not state their sources. The size of a DART is proportional to the number of routes used by Interests traversing a router, rather than the number of Interests traversing a router. It is shown that undetected Interest loops cannot occur in CCN-DART, and that Interests and responses to them are forwarded correctly independently of the state of the network. The results of simulation experiments comparing CCN-DART with NDN using the ndnSIM simulation tool show that CCN-DART attains similar or better latencies than NDN when no looping problems occur in NDN, while using a similar number of Interests and storing an order of magnitude fewer forwarding entries.

Forwarding and caching in video streaming over ICSDN: A clean-slate publish-subscribe approach

Nowadays, Internet usage has become prevalent, primarily because of high-quality heterogeneous multimedia content expectations from the subscriber (consumer), which puts tremendous pressure on the publisher (producer) in the networks. Information-Centric Networking (ICN) is a future internet architecture that optimizes data resources through content-based forwarding and caching, making it well-suited for multimedia content and video streaming (VS) scenarios. However, real-time data delivery is challenging in the current ICN-based publish–subscribe (pub-sub) mechanism, which pushes the existing pub-sub studies to prioritize more on the forwarding information base (FIB) rather than the pending interest table (PIT). This leads to issues such as inefficient caching and forwarding mechanisms, high overhead, and communication costs. To address these challenges, in this paper, we present a novel forwarding and caching solution named VS-ICSDN, integrating the combined principles of ICN-based pub-sub scheme and software-defined networking (SDN) in order to utilize the network resources more efficiently. We design a clean-slate caching strategy and name-based forwarding method to support both on-path and off-path caching on ICN nodes to coordinate flow entries among the SDN controller and clean-slate ICN nodes to maximize PIT utilization. In addition, the framework allows the content to be stored and searched in chunks with a single request to access the desired content, reducing the communication overhead and significantly improving overall performance. A simulation-based testbed and experimental result analysis validate our proposed work’s effectiveness in ensuring efficient network resource usage with low communication overhead and computational cost compared to other baseline methods.

Attention-Based LSTM Model for IFA Detection in Named Data Networking

As one of the next generation networks, Named Data Networking (NDN) performs well on content distribution. However, it is vulnerable against a new type of denial-of-service (DoS) attacks, interest flooding attacks (IFAs), one of the fatal threats to NDN. The attackers request nonexist content to occupy the Pending Interest Table (PIT), and it causes the degradation of network performance. Because of the great harm and strong concealment of this attack, it is urgent to detect and throttle the attack. This paper proposes a detection mechanism based on Long Short-Term Memory (LSTM) with attention mechanism, which uses sequence with different treatments. Once IFA is detected, the Hellinger distance is used to recognize malicious Interest prefix. The simulation results show that the proposed scheme can resist IFA effectively compared to state-of-the-art schemes.

Quality-of-Service-Linked Privileged Content-Caching Mechanism for Named Data Networks

The domain of information-centric networking (ICN) is expanding as more devices are becoming a part of connected technologies. New methods for serving content from a producer to a consumer are being explored, and Named Data Networking (NDN) is one of them. The NDN protocol routes the content from a producer to a consumer in a network using content names, instead of IP addresses. This facility, combined with content caching, efficiently serves content for very large networks consisting of a hybrid and ad hoc topology with both wired and wireless media. This paper addresses the issue of the quality-of-service (QoS) dimension for content delivery in NDN-based networks. The Internet Engineering Task Force (IETF) classifies QoS traffic as (prompt, reliable), prompt, reliable, and regular, and assigns corresponding priorities for managing the content. QoS-linked privileged content caching (QLPCC) proposes strategies for Pending Interest Table (PIT) and content store (CS) management in dedicated QoS nodes for handling priority content. QoS nodes are intermediately resourceful NDN nodes between content producers and consumers which specifically manage QoS traffic. The results of this study are compared with EQPR, PRR probability cache, and Least Frequently Used (LFU) and Least Fresh First (LFF) schemes, and QLPCC outperformed the latter-mentioned schemes in terms of QoS-node CS size vs. hit rate (6% to 47%), response time vs, QoS-node CS size (65% to 90%), and hop count vs. QoS-node CS size (60% to 84%) from the perspectives of priority traffic and overall traffic. QLPCC performed predictably when the NDN node count was increased from 500 to 1000, showing that the strategy is scalable.

2DNCF-PIT: Two-Dimensional Neighbor-Based Cuckoo Filter for Pending Interest Table Lookup in Named Data Networking

2DNCF-PIT: Two-Dimensional Neighbor-Based Cuckoo Filter for Pending Interest Table Lookup in Named Data Networking

An Efficient Probe-Based Routing for Content-Centric Networking.

With the development of new technologies and applications, such as the Internet of Things, smart cities, 5G, and edge computing, traditional Internet Protocol-based (IP-based) networks have been exposed as having many problems. Information-Centric Networking (ICN), Named Data Networking (NDN), and Content-Centric Networking (CCN) are therefore proposed as an alternative for future networks. However, unlike IP-based networks, CCN routing is non-deterministic and difficult to optimize due to frequent in-network caching replacement. This paper presents a novel probe-based routing algorithm that explores real-time in-network caching to ensure the routing table storing the optimal paths to the nearest content provider is up to date. Effective probe-selections, Pending Interest Table (PIT) probe, and Forwarding Information Base (FIB) probe are discussed and analyzed by simulation with different performance measurements. Compared with the basic CCN, in terms of qualitative analysis, the additional computational overhead of our approach is O(NCS + Nrt + NFIB ∗ NSPT) and O(NFIB) on processing interest packets and data packets, respectively. However, in terms of quantitative analysis, our approach reduces the number of timeout interests by 6% and the average response time by 0.6 s. Furthermore, although basic CCN and our approach belong to the same Quality of Service (QoS) category, our approach outperforms basic CCN in terms of real values. Additionally, our probe-based approach performs better than RECIF+PIF and EEGPR. Owing to speedup FIB updating by probes, our approach provides more reliable interest packet routing when accounting for router failures. In summary, the results demonstrate that compared to basic CCN, our probe-based routing approach raises FIB accuracy and reduces network congestion and response time, resulting in efficient routing.

Dynamically Allocated Bloom Filter-Based PIT Architectures

As a key component in implementing Named Data Networking (NDN), Pending Interest Table (PIT) requires an efficient exact-matching algorithm for a scalable and fast PIT lookup. A Bloom filter (BF) is a memory-efficient data structure for performing exact matching operations. In this paper, three different BF-based PIT architectures are proposed: PIT using functional Bloom filters (FBF-PIT), PIT using counting Bloom filters with return values (rCBF-PIT), and a refined rCBF-PIT with signatures (R-rCBF-PIT). The proposed BF-based PITs incrementally allocate a new BF for storing multiple incoming faces of Interest packets with the same content name. For a Data packet lookup, the proposed PIT architectures simultaneously access every BF structure to find matching faces and delete the faces (i.e., matching Interest packet information). The functional Bloom filter (FBF) used in an FBF-PIT is a key-value data structure that stores values only without keys. However, because the number of non-reusable conflict cells in the FBF increases as the number of stored packets increases in the FBF-PIT, the indeterminable rate increases. To decrease the indeterminable rate, we propose the rCBF-PIT, which uses counting Bloom filters with return values (rCBFs), allowing reusable conflict cells. False positives for Interest packets lead to incorrect deletions that can cause false negatives for incoming Data packets. Because most of the false positives occur in the first BF structure, we finally propose the R-rCBF-PIT, in which the first rCBF is replaced with an rCBF with a signature field. The proposed PITs also provide an aging mechanism using a valid bit and a hit bit for entry expiration. Simulation results show that rCBF-PIT and R-rCBF-PIT both reduce the indeterminable rate by more than 81% compared with FBF-PIT. The results also show that R-rCBF-PIT resolves false negatives caused by incorrect deletions by including the signature fields in the first rCBF.

Detecting and Mitigating Collusive Interest Flooding Attacks in Named Data Networking

The large expansion in network services and applications seen in the last few years requires new network architectures to satisfy an increasing number of users and enhance content delivery. Named Data Networking (NDN) has recently appeared as a new paradigm to solve many shortcomings in the current TCP/IP architecture. Its main characteristics like stateful forwarding and in-network caching made NDN networks an efficient environment for data delivery where the data is retrieved based on content names rather than IP addresses. The NDN, by its nature, defends against the well-known Distributed Denial of Service (DDoS) attacks that take place in the traditional TCP/IP architecture. However, a special kind of DoS attack called Collusive Interest Flooding Attack (CIFA) has appeared to overwhelm the resources of NDN routers by filling their Pending Interest Tables (PIT) with long-lasting malicious entries. The network throughput and consumer satisfaction rate are highly affected by CIFA. A lightweight yet efficient stateless CIFA detection algorithm is proposed in this research utilizing the non-parametric CUSUM algorithm; a change point detection approach that detects the point in time when a transition occurs in the network. The proposed algorithm is characterized by its low computational overhead, highly accurate detection, and quick response. To detect the malicious name prefixes and eliminate the CIFA effect, a mitigation algorithm that uses the average response time vales of all name prefixes is proposed in this research. Experimental results show that this approach detects CIFA after 199.5 ms from when an attack is launched in the large-scale topology. In addition, the mitigation approach effectively reduces the PIT utilization and increases the average consumer satisfaction rate.

NB-Cache: Non-Blocking In-Network Caching for High-Performance Content Routers

Information-Centric Networking (ICN) provides scalable and efficient content distribution at the Internet scale due to in-network caching and native multicast. To support these features, a content router needs high performance at its data plane, which consists of three forwarding steps: checking the Content Store (CS), then the Pending Interest Table (PIT), and finally the Forwarding Information Base (FIB). In this work, we build an analytical model of the router and identify that CS is the actual bottleneck. Then, we propose a novel mechanism called “NB-Cache” to address CS’s performance issue from a network-wide point of view. In NB-Cache, when packets arrive at a router whose CS is fully loaded, instead of being blocked and waiting for the CS, these packets are forwarded to the next-hop router, whose CS may not be fully loaded. This approach essentially utilizes Content Stores of all the routers along the forwarding path in parallel rather than checking each CS sequentially. NB-Cache follows a design pattern of on-demand load balancing and can be formulated into a non-trivial N-queue bypass model. We use the Markov chain to establish its theoretical base and find an algorithm for automated transition rate matrix generation. Experiments show significant improvement of data plane performance: 70% reduction in round-trip time (RTT) and 130% increase in throughput. NB-Cache decouples the fast packet forwarding from the slower content retrieval thus substantially reducing CS’s heavy dependency on fast but expensive memory.

I-CIFA: An improved collusive interest flooding attack in named data networking

Named Data Network (NDN) as a new network architecture, in recent years become a hot research, its security has been widespread concern. With the continuous updating of distributed denial of service (DDoS) attack methods in NDN networks, this article designs a new type of attack, called the Improved Collusive Flooding Attack (I-CIFA). I-CIFA attack combines the advantages of mainstream DDoS attack in NDN network, and is an attack method generated by low-rate DDoS attack and the cooperation of collusive producer. On the basis of the existing DDoS attack, the I-CIFA attack further improves the ability to destroy the network and the ability to resist the existing defense scheme. I-CIFA is designed on the basis of CIFA by improving the attack nodes and so on. In addition to redefining and configuring the attack parameters, improvements were also made in two aspects. First, the probing mode to probe the pending interest table (PIT) capacity of the routing nodes was added before attack started. Second, the way in which each attacker requests a packet from the collusive producer in each attack cycle has been further improved. Test results show that I-CIFA can cause 87.5% of the legitimate interest packets in the whole network to be discarded, and it is not only has a strong attack range on the network, but it is also difficult to be detected by existing CIFA-countermeasures.

A survey on the roles of Bloom Filter in implementation of the Named Data Networking

Today is the era of smart devices. Through smart devices, people remain connected with each other across the globe. But, it led the current Internet towards scalability issues. Therefore, leaving IP-based Internet behind, the world is moving to the Future Internet Architecture, called Named Data Networking (NDN). Currently, the number of nodes connected to the Internet is in the billions. Coupled with, the number of requests sent is in millions per second. NDN handles such huge numbers by modifying the existing IP architecture to meet the current requirements. NDN is scalable, generates less traffic, saves bandwidth, efficiently utilizes multiple network interfaces, provides high-level security, etc., which are essential for current communication technology. Correspondingly, Bloom Filter is a simple data structure capable of enhancing the performance of NDN. It is a probabilistic data structure for the membership query. Bloom Filter is deployed in various modules of NDN to handle the enormous number of packets. This article presents a detailed discussion on the role of Bloom Filter in implementing NDN. Moreover, the article includes a detailed discussion about Bloom Filter and the NDN architecture’s main components: Packet, Content Store, Forwarding Information Base, and Pending Interest Table. The article also provides many tables to increase the understanding of the topics.