A Reputation Value-Based Early Detection Mechanism Against the Consumer-Provider Collusive Attack in Information-Centric IoT

Abstract

As the Internet of Things (IoT) has connected large number of devices to the Internet, it is urgently needed to guarantee the low latency, security, scalable content distribution of the IoT network. The benefits of Information-Centric Networking (ICN) in terms of fast and efficient data delivery and improved reliability have raised ICN as a highly promising networking model for IoT environments. However, with the widely spread of the viruses and the explosion of kinds of network devices, the attackers can easily control the devices to form a botnet such as the Mirai. Once the devices are under control, the attackers can launch a consumer-provider collusive attack in the Information-Centric IoT context. In this attack, the malicious clients issue Interest packets that can only be satisfied by the malicious content provider, and the malicious provider replies to the clients just before exceeding the Pending Interest Table entry's expiration time, to occupy the limited resources. In this paper, we expound the model of the consumer-provider collusive attack and analyze the negative effect of the attack. Then we propose a Reputation Value based Early Detection (RVED) mechanism to relieve the impact of the collusive attack. The method aims to adjust the packet dropping rates of different interfaces based on their reputation value, thus to protect the legitimate packets from being dropped as possible. We implement the consumer-provider collusive model and evaluate our defend mechanism in the simulator, and simulation results verify the feasibility and effectiveness against the collusive attack of the RVED mechanism.INDEX TERMS Information-centric networking, Internet of Things, collusive attack, reputation value, early detection.

Interest Flooding Attacks in Named Data Networking: Survey of Existing Solutions, Open Issues, Requirements, and Future Directions

“…ChoKIFA+ [21], Zhi et al [129], FROG [70], IFBN [89], OPT [78], iForest [29], FITT [124], MSIDN [23], mTBAD [62], InterestFence [39], Wu et al [108] Ghali et al [44], Alston et al [17] Fig. 7.…”

“…Proactive solutions (12%) Reactive solutions (88%) Salah et al [83], Salah et al [82], Yin et al [114], Cheng et al [31], Alhisnawi et al [16] Nguyen et al [74,72], Xin et al [110], Afanasyev et al [10], Poseidon [33], Dai et al [35], Xin et al [109], Shigeyasu et al [78], DPE [105], TDM [104], Vassilakis et al [98] [46], ChoKIFA [22], ChoKIFA+ [21], Zhi et al [129], FROG [70], IFBN [89], OPT [78], iForest [29], FITT [124], MSIDN [23], mTBAD [62], InterestFence [39], Wu et al [108] Wang et al [107], Zhang et al [121], InterestCash [59], PERSIA [97], BLAM [61] Fig. In a different approach, the solution proposed in [17] uses cryptographic tokens to route packets.…”

“…Besides the fact that legitimate traffic is penalized by the charge/reward mechanism, relying only on the satisfaction ratio to predict the consumer's state may lead to false detection. [46], ChoKIFA [22], ChoKIFA+ [21], Zhi et al [129], FROG [70],…”

Hybrid Deep Neural Network Model for Detection of Security Attacks in IoT Enabled Environment

“…Hence, the standard attack detection model is required, which could analyze the behavior of attacks in network. The rising of deep learning (DL) has alleviated the limitations of the conventional machine learning (ML) schemes due to the combined implementation of classifier and feature extraction, and its strong representative ability [10] [11] [12] Further, DL model is used for avoiding the overhead of manual selection of features, and that is an essential section for traditional classification systems [13] [14].…”

Neural Network Model for Detecting Network Scanning Attacks

“…In this flow, it is more and more difficult to ensure the security of information systems and the information processed. The mechanisms for detecting cyberattacks are constantly being improved [3][4][5]. However, the development of mechanisms for the implementation of threats also does not stand still.…”