Nowadays, because of its increased popularity, Android is target to a growing number of attacks and malicious applications, with the purpose of stealing private information and consuming credit by subscribing to premium services. Most of the current commercial antivirus solutions use static signatures for malware detection, which may fail to detect different variants of the same malware and zero‐day attacks. In this paper, we present a behavior‐based, dynamic analysis security solution, called Android Malware Detection System, for detecting both well‐known and zero‐day malware. The proposed solution uses a machine learning classifier in order to differentiate between the behaviors of legitimate and malicious applications. In addition, it uses the application statistics for determining its reputation. The final decision is based on a combination of the classifier's result and the application reputation. The solution includes a unique and extensive set of data collectors, which gather application‐specific data that describe the behavior of the monitored application. We evaluated our solution on a set of legitimate and malicious applications and obtained a high accuracy of 0.985. Our system is able to detect zero‐day malware samples that are not detected by current commercial solutions. Our solution outperforms other similar solutions running on mobile devices. Copyright © 2015 John Wiley & Sons, Ltd.
Wireless Sensor Networks are used in domains such as medical monitoring, homeland security, industrial automation and military applications, therefore it is very critical to protect the network against malicious attacks. This paper presents a new security protocol that provides conversation authentication, integrity, intrusion prevention and anti-replay protection. The protocol uses two methods in order to meet these requirements: the last MAC method and an authentication handshake. The protocol has been implemented in TinyOS in two layers of the communication stack: the MAC and Authentication layer. The MAC layer is the implementation of the last MAC method and the Authentication layer generates and verifies the four messages exchanged during the authentication handshake. Authentication and Anti-replay Security Protocol has been tested with TOSSIM in various attack scenarios and has proved that it is able to reject malicious attempts to communicate with the network nodes.
Unikernels are famous for providing excellent performance in terms of boot times, throughput and memory consumption, to name a few metrics. However, they are infamous for making it hard and extremely time consuming to extract such performance, and for needing significant engineering effort in order to port applications to them. We introduce Unikraft, a novel micro-library OS that (1) fully modularizes OS primitives so that it is easy to customize the unikernel and include only relevant components and (2) exposes a set of composable, performance-oriented APIs in order to make it easy for developers to obtain high performance.Our evaluation using off-the-shelf applications such as nginx, SQLite, and Redis shows that running them on Unikraft results in a 1.7x-2.7x performance improvement compared to Linux guests. In addition, Unikraft images for these apps are around 1MB, require less than 10MB of RAM to run, and boot in around 1ms on top of the VMM time (total boot time 3ms-40ms). Unikraft is a Linux Foundation open source project and can be found at www.unikraft.org.
In the last decade, file sharing systems have generally been dominated by P2P solutions. Whereas e-mail and HTTP have been the killer apps of the earlier Internet, a large percentage of the current Internet backbone traffic is BitTorrent traffic [12]. BitTorrent has proven to be the perfect file sharing solution for a decentralized Internet, moving the burden from central servers to each individual station and maximizing network performance by enabling unused communication paths between clients.Although there have been extensive studies regarding the performance of the BitTorrent protocol and the impact of network and human factors on the overall transfer quality, there has been little interest in evaluating, comparing and analyzing current real world implementations. With hundreds of BitTorrent clients, each applying different algorithms and performance optimization techniques, we consider evaluating and comparing various implementations an important issue.In this paper, we present a BitTorrent performance evaluation framework that we are using to test and compare current real world BitTorrent implementations. The framework is fully automated and clients have been instrumented to output transfer status data and extensive logging information.
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