Broadcast authentication is a fundamental security service in wireless sensor networks (WSNs). Although symmetric-key-based µTESLA-like schemes were employed due to their energy efficiency, they all suffer from DoS attacks resulting from the nature of delayed message authentication. Recently, several public-key-based schemes were proposed to achieve immediate broadcast authentication that may significantly improved security strength. However, while the public-key-based schemes obviate the security vulnerability inherent to symmetric-key-based µTESLA-like schemes, their signature verification is time-consuming. Thus, speeding up signature verification is a problem of considerable practical importance, especially in resource-constrained environments. This paper exploits the cooperation among sensor nodes to accelerate the signature verification of vBNN-IBS, a pairing-free identity-based signature with reduced signature size. We demonstrate through on extensive performance evaluation study that the accelerated vBNN-IBS achieves the longest network lifetime compared to both the traditional vBNN-IBS and the accelerated ECDSA schemes. The accelerated vBNN-IBS runs 66% faster than the traditional signature verification method. Results from theoretical analysis, simulation, and real-world experimentation on a MICAz platform are provided to validate our claims.

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Abstract-This research aims to examine the effectiveness and efficiency of fuzzing hashing algorithm in the identification of similarities in Malware Analysis. More precisely, it will present the benefit of using fuzzy hashing algorithms, such as ssdeep, sdhash, mvHash and mrsh v2, in identifying similarities in Malware domain. The obtained results will be compared with the traditional and most common Cryptographic Hashes, such as the MD5, SHA-1 and SHA-256. Furthermore, it will highlight the pros and cons of fuzzy and cryptographic hashing, as well as their adoption in real world applications.

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Several solutions have been proposed in the literature to address the Unmanned Aerial Vehicles (UAVs) collision avoidance problem. Most of these solutions consider that the ground controller system (GCS) determines the path of a UAV before starting a particular mission at hand. Furthermore, these solutions expect the occurrence of collisions based only on the GPS localization of UAVs as well as via object-detecting sensors placed on board UAVs. The sensors' sensitivity to environmental disturbances and the UAVs' influence on their accuracy impact negatively the efficiency of these solutions. In this vein, this paper proposes a new energy-and delay-aware physical collision avoidance solution for UAVs. The solution is dubbed EDC-UAV. The primary goal of EDC-UAV is to build in-flight safe UAVs trajectories while minimizing the energy consumption and response time. We assume that each UAV is equipped with a global positioning system (GPS) sensor to identify its position. Moreover, we take into account the margin error of the GPS to provide the position of a given UAV. The location of each UAV is gathered by a cluster head, which is the UAV that has either the highest autonomy or the greatest computational capacity. The cluster head runs the EDC-UAV algorithm to control the rest of the UAVs, thus guaranteeing a collisionfree mission and minimizing the energy consumption to achieve different purposes. The proper operation of our solution is validated through simulations. The obtained results demonstrate the efficiency of EDC-UAV in achieving its design goals.

The proliferation of malware in recent times have accounted for the increase in computer crimes and prompted for a more aggressive research into improved investigative strategies, to keep up with the menace. Recent techniques and tools that have been developed and adopted to keep up in an arms race with malware authors who have resorted to the use of evasive techniques to avoid analysis during investigation is an on-going concern. Exploring dynamic analysis is unarguably, a positive step to supporting static evidence with malware dynamic behaviour logs. In view of this, analysing this huge generated reports raises concerns about speed, accuracy and performance.This research proposes an Automated Malware Investigative Framework Model, a component based approach that is designed to support investigation by integrating both malware analysis and data mining clustering techniques as part of an effort to solve the problem of overly generated reports. Thus, grouping analysed suspicious samples that exhibit similar behavioural features to make investigation easy and more intuitive. The focus of this paper however, is on implementing sub-components of the framework that directly deals with the problem at hand.