Drones are being increasingly adopted across both military and commercial domains to serve remote rendering, monitoring, surveillance and service delivery operations. Drone swarms comprise multiple drones operating cohesively as a unified system to provide collective services. Each drone in a swarm must establish mutual trust with other drones to ensure authenticity in data exchange and also to prevent the compromise of a mission. Inter-drone communication links are vulnerable to cyber threats, including unauthorized access and spoofing. While most existing studies focus on authentication mechanisms for drone-to-stationary base stations, very little research work has explored inter-drone authentication protocols specifically designed for decentralized topologies. We propose a lightweight authentication scheme for inter-drone communication that leverages a dynamic challenge-response mechanism, hash-based message authentication code and authenticated encryption to facilitate mutual authentication. We validate the efficacy of the proposed protocol through extensive informal analysis based on the Dolev–Yao and the Canetti-Krawczyk threat models and through Scyther and random oracle-based formal analysis. We also compare the protocol’s performance with state-of-the-art authentication schemes to demonstrate its efficacy and efficiency. The results obtained demonstrate the supremacy of the protocol in cost-effective threat prevention for swarms of drones.

Continuous Authentication (CA) has been proposed as a potential solution to counter complex cybersecurity attacks that exploit conventional static authentication mechanisms that authenticate users only at an ingress point. However, widely researched human user characteristics-based CA mechanisms cannot be extended to continuously authenticate Internet of Things (IoT) devices. The challenges are exacerbated with the increased adoption of device-to-device (d2d) communication in critical infrastructures. Existing d2d authentication protocols proposed in the literature are either prone to subversion or are computationally infeasible to be deployed on constrained IoT devices. In view of these challenges, we propose a novel, lightweight and secure CA protocol that leverages communication channel properties and a tunable mathematical function to generate dynamically changing session keys. Our preliminary informal protocol analysis suggests that the proposed protocol is resistant to known attack vectors and thus has strong potential for deployment in securing critical and resource-constrained d2d communication.

Internet of Things (IoT) has an immense potential for a plethora of applications ranging from healthcare automation to defence networks and the power grid. The security of an IoT network is essentially paramount to the security of the underlying computing and communication infrastructure. However, due to constrained resources and limited computational capabilities, IoT networks are prone to various attacks. Thus, safeguarding the IoT network from adversarial attacks is of vital importance and can be realised through planning and deployment of effective security controls; one such control being an intrusion detection system. In this paper, we present a novel intrusion detection scheme for IoT networks that classifies traffic flow through the application of deep learning concepts. We adopt a newly published IoT dataset and generate generic features from the field information in packet level. We develop a feed-forward neural networks model for binary and multi-class classification including denial of service, distributed denial of service, reconnaissance and information theft attacks against IoT devices. Results obtained through the evaluation of the proposed scheme via the processed dataset illustrate a high classification accuracy.

ABSTRACT A Honeynet is a network designed by the Honeynet Project organization to gather information on security threats and attacks. Honeynets are being used by numerous institutions to proactively improve network security by identifying malicious and unauthorized activities in production and private networks. A Honeynet captures a substantial amount of network data and logs. The analysis of these datasets to identify malicious activities is a challenging task. The main aim of the work in this paper is to employ an anomaly detection technique to classify different types of malicious activities present in Honeynet. In particular, we use feature‐based and volume‐based schemes for Honeynet data classification. A detailed analysis of various traffic features is carried out, and the most appropriate ones for Honeynet traffic are selected. The classification of malicious activities is achieved by applying entropy‐based distributions and traffic volume distributions. Entropy‐based distributions are used for feature‐based parameters, whereas traffic volume distributions are used for volume‐based parameters. The behavior of various anomalies or malicious activities is classified using the selected features and their respective threshold values. Finally, we propose a mapping between the various anomalies and their associated behavior, which can be further used to identify similar anomalies in other Honeynet data sets. Copyright © 2012 John Wiley & Sons, Ltd.

Reliable, fast, and power aware communication is needed for Ad-Hoc wireless networks. Current techniques based on client-server and Publish/Subscribe communication models are not suitable in multi-robot systems and generally for mobile applications. For this we propose a reliable peer-to-peer protocol based on a UDP Broadcast and Token Passing (UBTP). The protocol is implemented on a WLAN using the Stargate embedded system. For this, a customized UDP protocol with an imperative Poll-based communication is proposed. The protocol is implemented using (1) a communication thread (TC) and (2) a processing thread (TP). A test bed system which allows modules to run TC and TP, in addition to the generation of broadcast request is presented. We used symmetric code in each node. Evaluation reports the distribution of auction completion times for peer-to-peer operations. The evaluation reveals: (1) response times are comparable to UBTP operated at head node, (2) improved degree of reliability as at most 2 steps are sufficient for auctioning seven nodes, (3) proved fairness, and (4) comparable power consumption to simple UBTP.

Honey nets are an increasingly popular choice deployed by organizations to lure attackers into a trap network, for collection and analysis of unauthorized network activity. A Honey net captures substantial amount of data and logs for analysis in order to identify malicious activities perpetrated by the hacker community. The analysis of this large amount of data is a challenging task. Through this paper, we propose a technique based on the entropy and volume thresholds of selected network features to efficiently analyze Honey net data, and identify malicious activities. Our technique consists of both feature-based and volume-based schemes to identify malicious activities in the Honey net traffic. Through deployment of our proposed approach, a detailed analysis of various traffic features is conducted and the most appropriate features for Honey net traffic are thereupon selected. The anomalies are identified using entropy distributions and volume distributions, along with their corresponding threshold levels. The proposed scheme proves to be effective in identifying most types of anomalies seen in Honey net traffic.