IDLP: an efficient intrusion detection and location-aware prevention mechanism for network coding-enabled mobile small cells

Mobile small cell technology is considered as a 5G enabling technology for delivering ubiquitous 5G services in a cost-effective and energy efficient manner. Moreover, Network Coding (NC) technology can be foreseen as a promising solution for the wireless network of mobile small cells to increase its throughput and improve its performance. However, NC-enabled mobile small cells are vulnerable to pollution attacks due to the inherent vulnerabilities of NC. Although there are several works on pollution attack detection, the attackers may continue to pollute packets in the next transmission of coded packets of the same generation from the source node to the destination nodes. Therefore, in this paper, we present an intrusion detection and location-aware prevention (IDLP) mechanism which does not only detect the polluted packets and drop them but also identify the attacker's exact location so as to block them and prevent packet pollution in the next transmissions. In the proposed IDLP mechanism, the detection and locating schemes are based on a null space-based homomorphic MAC scheme. However, the proposed IDLP mechanism is efficient because, in its initial phase (i.e., Phase 1), it is not needed to be applied to all mobile devices in order to protect the NC-enabled mobile small cells from the depletion of their resources. The proposed efficient IDLP mechanism has been implemented in Kodo, and its performance has been evaluated and compared with our previous IDPS scheme proposed in [1], in terms of computational complexity, communicational overhead, and successfully decoding probability as well

Security and Privacy for Green IoT-based Agriculture: Review, Blockchain solutions, and Challenges

open access articleThis paper presents research challenges on security and privacy issues in the field of green IoT-based agriculture. We start by describing a four-tier green IoT-based agriculture architecture and summarizing the existing surveys that deal with smart agriculture. Then, we provide a classification of threat models against green IoT-based agriculture into five categories, including, attacks against privacy, authentication, confidentiality, availability, and integrity properties. Moreover, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward secure and privacy-preserving technologies for IoT applications and how they will be adapted for green IoT-based agriculture. In addition, we analyze the privacy-oriented blockchain-based solutions as well as consensus algorithms for IoT applications and how they will be adapted for green IoT-based agriculture. Based on the current survey, we highlight open research challenges and discuss possible future research directions in the security and privacy of green IoT-based agriculture

LGTBIDS: Layer-wise Graph Theory Based Intrusion Detection System in Beyond 5G

The advancement in wireless communication technologies is becoming more demanding and pervasive. One of the fundamental parameters that limit the efficiency of the network are the security challenges. The communication network is vulnerable to security attacks such as spoofing attacks and signal strength attacks. Intrusion detection signifies a central approach to ensuring the security of the communication network. In this paper, an Intrusion Detection System based on the framework of graph theory is proposed. A Layerwise Graph Theory-Based Intrusion Detection System (LGTBIDS) algorithm is designed to detect the attacked node. The algorithm performs the layer-wise analysis to extract the vulnerable nodes and ultimately the attacked node(s). For each layer, every node is scanned for the possibility of susceptible node(s). The strategy of the IDS is based on the analysis of energy efficiency and secrecy rate. The nodes with the energy efficiency and secrecy rate beyond the range of upper and lower thresholds are detected as the nodes under attack. Further, detected node(s) are transmitted with a random sequence of bits followed by the process of re-authentication. The obtained results validate the better performance, low time computations, and low complexity. Finally, the proposed approach is compared with the conventional solution of intrusion detection.Comment: in IEEE Transactions on Network and Service Management, 202

Intrusion Tolerant Routing Protocols for Wireless Sensor Networks

This MSc thesis is focused in the study, solution proposal and experimental evaluation of security solutions for Wireless Sensor Networks (WSNs). The objectives are centered on intrusion tolerant routing services, adapted for the characteristics and requirements of WSN nodes and operation behavior. The main contribution addresses the establishment of pro-active intrusion tolerance properties at the network level, as security mechanisms for the proposal of a reliable and secure routing protocol. Those properties and mechanisms will augment a secure communication base layer supported by light-weigh cryptography methods, to improve the global network resilience capabilities against possible intrusion-attacks on the WSN nodes. Adapting to WSN characteristics, the design of the intended security services also pushes complexity away from resource-poor sensor nodes towards resource-rich and trustable base stations. The devised solution will construct, securely and efficiently, a secure tree-structured routing service for data-dissemination in large scale deployed WSNs. The purpose is to tolerate the damage caused by adversaries modeled according with the Dolev-Yao threat model and ISO X.800 attack typology and framework, or intruders that can compromise maliciously the deployed sensor nodes, injecting, modifying, or blocking packets, jeopardizing the correct behavior of internal network routing processing and topology management. The proposed enhanced mechanisms, as well as the design and implementation of a new intrusiontolerant routing protocol for a large scale WSN are evaluated by simulation. For this purpose, the evaluation is based on a rich simulation environment, modeling networks from hundreds to tens of thousands of wireless sensors, analyzing different dimensions: connectivity conditions, degree-distribution patterns, latency and average short-paths, clustering, reliability metrics and energy cost

A novel intrusion detection and prevention scheme for network coding-enabled mobile small cells

Network coding (NC)-enabled mobile small cells are observed as a promising technology for fifth-generation (5G) networks that can cover the urban landscape by being set up on demand at any place and at any time on any device. Nevertheless, despite the significant benefits that this technology brings to the 5G of mobile networks, major security issues arise due to the fact that NC-enabled mobile small cells are susceptible to pollution attacks; a severe security threat exploiting the inherent vulnerabilities of NC. Therefore, intrusion detection and prevention mechanisms to detect and mitigate pollution attacks are of utmost importance so that NC-enabled mobile small cells can reach their full potential. Thus, in this article, we propose for the first time, to the best of our knowledge, a novel intrusion detection and prevention scheme (IDPS) for NC-enabled mobile small cells. The proposed scheme is based on a null space-based homomorphic message authentication code (MAC) scheme that allows detection of pollution attacks and takes proper risk mitigation actions when an intrusive incident is detected. The proposed scheme has been implemented in Kodo and its performance has been evaluated in terms of computational overhead

The Wireless Control Network: Monitoring for Malicious Behavior

We consider the problem of stabilizing a plant with a network of resource constrained wireless nodes. In a companion paper, we developed a protocol where each node repeatedly transmits an appropriate (stabilizing) linear combination of the values in its neighborhood. In this paper, we design an Intrusion Detection System (IDS) for this control scheme, which observes the transmissions of certain nodes and uses that information to (a) recover the plant outputs (for datalogging and diagnostic purposes) and (b) identify malicious behavior by any of the wireless nodes in the network. We show that if the connectivity of the network is sufficiently high, the IDS only needs to observe a subset of the nodes in the network in order to achieve this objective. Our approach provides a characterization of the set of nodes that should be observed, a systematic procedure for the IDS to use to identify the malicious nodes and recover the outputs of the plant, and an upper bound on the delay required to obtain the necessary information

Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses

The ongoing deployment of the fifth generation (5G) wireless networks constantly reveals limitations concerning its original concept as a key driver of Internet of Everything (IoE) applications. These 5G challenges are behind worldwide efforts to enable future networks, such as sixth generation (6G) networks, to efficiently support sophisticated applications ranging from autonomous driving capabilities to the Metaverse. Edge learning is a new and powerful approach to training models across distributed clients while protecting the privacy of their data. This approach is expected to be embedded within future network infrastructures, including 6G, to solve challenging problems such as resource management and behavior prediction. This survey article provides a holistic review of the most recent research focused on edge learning vulnerabilities and defenses for 6G-enabled IoT. We summarize the existing surveys on machine learning for 6G IoT security and machine learning-associated threats in three different learning modes: centralized, federated, and distributed. Then, we provide an overview of enabling emerging technologies for 6G IoT intelligence. Moreover, we provide a holistic survey of existing research on attacks against machine learning and classify threat models into eight categories, including backdoor attacks, adversarial examples, combined attacks, poisoning attacks, Sybil attacks, byzantine attacks, inference attacks, and dropping attacks. In addition, we provide a comprehensive and detailed taxonomy and a side-by-side comparison of the state-of-the-art defense methods against edge learning vulnerabilities. Finally, as new attacks and defense technologies are realized, new research and future overall prospects for 6G-enabled IoT are discussed