Biblio

A vehicular Ad-hoc network (VANET) allows groups of autonomous or semi-autonomous vehicles to share information and content with each other and infrastructure. Named Data Networking (NDN) is recently proposed as one of the future internet architectures, which allows communication in network-based upon content name. It has originated from Information-centric networking (ICN). NDN-based VANET uses NDN as an underlying communication paradigm. NDN-based VANET suffers from several security attacks, one such attack is the Interest Flooding Attack (IFA) that targets the core forwarding mechanism of NDN-based VANET. This paper focuses on the detection and mitigation of IFA in NDN-based VANET. We proposed a method FIFA to detect and mitigate IFA. Our proposed method is capable of detecting normal IFA as well as a low-rate IFA. Along with that FIFA also ensures non-repudiation in networks. We have compared our proposed method with the existing mechanism to detect and mitigate IFA named IFAMS. Experiment results show that our method detects and mitigates normal IFA and low-rate IFA in the network.

Due to the harsh environment and energy limitation, maintaining efficient communication is crucial to the lifetime of Underwater Sensor Networks (UWSN). Named Data Networking (NDN), one of future network architectures, begins to be applied to UWSN. Although Underwater Named Data Networking (UNDN) performs well in data transmission, it still faces some security threats, such as the Denial-of-Service (DoS) attacks caused by Interest Flooding Attacks (IFAs). In this paper, we present a new type of DoS attacks, named as Synergetic Denial-of-Service (SDoS). Attackers synergize with each other, taking turns to reply to malicious interests as late as possible. SDoS attacks will damage the Pending Interest Table, Content Store, and Forwarding Information Base in routers with high concealment. Simulation results demonstrate that the SDoS attacks quadruple the increased network traffic compared with normal IFAs and the existing IFA detection algorithm in UNDN is completely invalid to SDoS attacks. In addition, we analyze the infection problem in UNDN and propose a defense method Trident based on carefully designed adaptive threshold, burst traffic detection, and attacker identification. Experiment results illustrate that Trident can effectively detect and resist both SDoS attacks and normal IFAs. Meanwhile, Trident can robustly undertake burst traffic and congestion.

Information-Centric Networking (ICN) 1 is a significant networking paradigm for the Internet of Things, which is an information-centric network in essence. The ICN paradigm owns inherently some security features, but also brings several new vulnerabilities. The most significant one among them is Interest flooding, which is a new type of Denial of Service (DoS) attack, and has even more serious effects to the whole network in the ICN paradigm than in the traditional IP paradigm. In this paper, we suggest a new mechanism to mitigate Interest flooding attack. The detection of Interest flooding and the corresponding mitigation measures are implemented on the edge routers, which are directly connected with the attackers. By using statistics of Interest satisfaction rate on the incoming interface of some edge routers, malicious name-prefixes or interfaces can be discovered, and then dropped or slowed down accordingly. With the help of the network information, the detected malicious name-prefixes and interfaces can also be distributed to the whole network quickly, and the attack can be mitigated quickly. The simulation results show that the suggested mechanism can reduce the influence of the Interest flooding quickly, and the network performance can recover automatically to the normal state without hurting the legitimate users.

Named Data Networking (NDN) is one of the most promising data-centric networks. NDN is resilient to most of the attacks that are possible in TCP/IP stack. Since NDN has different network architecture than TCP/IP, so it is prone to new types of attack. These attacks are Interest Flooding Attack (IFA), Cache Privacy Attack, Cache Pollution Attack, Content Poisoning Attack, etc. In this paper, we discussed the detection of IFA. First, we model the IFA on linear topology using the ndnSIM and CCNx code base. We have selected most promising feature among all considered features then we applied diïňĂerent machine learning techniques to detect the attack. We have shown that result of attack detection in case of simulation and implementation is almost same. We modeled IFA on DFN topology and compared the results of different machine learning approaches.

The Named-Data Networking (NDN) has emerged as a clean-slate Internet proposal on the wave of Information-Centric Networking. Although the NDN's data-plane seems to offer many advantages, e.g., native support for multicast communications and flow balance, it also makes the network infrastructure vulnerable to a specific DDoS attack, the Interest Flooding Attack (IFA). In IFAs, a botnet issuing unsatisfiable content requests can be set up effortlessly to exhaust routers' resources and cause a severe performance drop to legitimate users. So far several countermeasures have addressed this security threat, however, their efficacy was proved by means of simplistic assumptions on the attack model. Therefore, we propose a more complete attack model and design an advanced IFA. We show the efficiency of our novel attack scheme by extensively assessing some of the state-of-the-art countermeasures. Further, we release the software to perform this attack as open source tool to help design future more robust defense mechanisms.