1,720,984 research outputs found
ABAKA: A novel attribute-based k-anonymous collaborative solution for LBSs
Full text linkThe increasing use of mobile devices, along with advances in telecommunication systems, increased the popularity of Location-Based Services (LBSs). In LBSs, users share their exact location with a potentially untrusted Location-Based Service Provider (LBSP). In such a scenario, user privacy becomes a major con- cern: the knowledge about user location may lead to her identification as well as a continuous tracing of her position. Researchers proposed several approaches to preserve users’ location privacy. They also showed that hiding the location of an LBS user is not enough to guarantee her privacy, i.e., user’s pro- file attributes or background knowledge of an attacker may reveal the user’s identity. In this paper we propose ABAKA, a novel collaborative approach that provides identity privacy for LBS users considering users’ profile attributes. In particular, our solution guarantees p -sensitive k -anonymity for the user that sends an LBS request to the LBSP. ABAKA computes a cloaked area by collaborative multi-hop forwarding of the LBS query, and using Ciphertext-Policy Attribute-Based Encryption (CP-ABE). We ran a thorough set of experiments to evaluate our solution: the results confirm the feasibility and efficiency of our proposal
Forensics Analysis of Android Mobile VoIP Apps
No full textVoice over Internet Protocol (VoIP) applications (apps) provide convenient and low-cost means for users to communicate and share information with each other in real-time. Day by day, the popularity of such apps is increasing, and people produce and share a huge amount of data, including their personal and sensitive information. This might lead to several privacy issues, such as revealing user contacts, private messages, or personal photos. Therefore, having an up-to-date forensic understanding of these apps is necessary.This chapter presents analysis of forensically valuable remnants of three popular Mobile VoIP (mVoIP) apps on Google Play store, namely: Viber, Skype, and WhatsApp Messenger, in order to figure out to what extent these apps reveal forensically valuable information about the users activities. We performed a thorough investigative study of these three mVoIP apps on smartphone devices. Our experimental results show that several artifacts, such as messages, contact details,..
Investigating Storage as a Service Cloud Platform: pCloud as a Case Study
No full textDue to the flexibility, affordability, and portability of cloud storage, individuals and companies envisage cloud storage as one of the preferred storage media nowadays. This attracts the eyes of cyber criminals, since much valuable information such as user credentials and private customer records are stored in the cloud. There are many ways for criminals to compromise cloud services; ranging from nontechnical attack methods, such as social engineering, to deploying advanced malwares. Therefore, it is vital for cyber forensics examiners to be equipped and informed about best methods for investigation of different cloud platforms. In this chapter, using pCloud (an extensively used online cloud storage service) as a case study, and we elaborate on different kinds of artifacts retrievable during a forensic examination. We carried out our experiments on four different virtual machines running four popular operating systems: a 64 bit Windows 8, Ubuntu 14.04.1 LTS, Android 4.4.2, and iOS 8.1..
Intelligent conditional collaborative private data sharing
Full text linkWith the advent of distributed systems, secure and privacy-preserving data sharing between different entities (individuals or organizations) becomes a challenging issue. There are several real-world scenarios in which different entities are willing to share their private data only under certain circumstances, such as sharing the system logs when there is indications of cyber attack in order to provide cyber threat intelligence. Therefore, over the past few years, several researchers proposed solutions for collaborative data sharing, mostly based on existing cryptographic algorithms. However, the existing approaches are not appropriate for conditional data sharing, i.e., sharing the data if and only if a pre-defined condition is satisfied due to the occurrence of an event. Moreover, in case the existing solutions are used in conditional data sharing scenarios, the shared secret will be revealed to all parties and re-keying process is necessary. In this work, in order to address the aforementioned challenges, we propose, a “conditional collaborative private data sharing” protocol based on Identity-Based Encryption and Threshold Secret Sharing schemes. In our proposed approach, the condition based on which the encrypted data will be revealed to the collaborating parties (or a central entity) could be of two types: (i) threshold, or (ii) pre-defined policy. Supported by thorough analytical and experimental analysis, we show the effectiveness and performance of our proposal
On the Feasibility of Attribute-Based Encryption on Internet of Things Devices.
Full text linkAttribute-based encryption (ABE) could be an effective cryptographic tool for the secure management of Internet of Things (IoT) devices, but its feasibility in the IoT has been under-investigated thus far. This article explores such feasibility for well-known IoT platforms, namely, Intel Galileo Gen 2, Intel Edison, Raspberry pi 1 model B, and Raspberry pi zero, and concludes that adopting ABE in the IoT is indeed feasible
Forensic Investigation of Cooperative Storage Cloud Service: Symform As a Case Study.
Full text linkResearchers envisioned Storage as a Service (StaaS) as an effective solution to the distributed management of digital data. Cooperative storage cloud forensic is relatively new and is an under-explored area of research. Using Symform as a case study, we seek to determine the data remnants from the use of cooperative cloud storage services. In particular, we consider both mobile devices and personal computers running various popular operating systems, namely Windows 8.1, Mac OS X Mavericks 10.9.5, Ubuntu 14.04.1 LTS, iOS 7.1.2, and Android KitKat 4.4.4. Potential artefacts recovered during the research include data relating to the installation and uninstallation of the cloud applications, log-in to and log-out from Symform account using the client application, file synchronization as well as their time stamp information. This research contributes to an in-depth understanding of the types of terrestrial artifacts that are likely to remain after the use of cooperative storage cloud on client devices
Leveraging machine learning techniques for windows ransomware network traffic detection
No full textRansomware has become a significant global threat with the ransomware-as-a-service model enabling easy availability and deployment, and the potential for high revenues creating a viable criminal business model. Individuals, private companies or public service providers e.g. healthcare or utilities companies can all become victims of ransomware attacks and consequently suffer severe disruption and financial loss. Although machine learning algorithms are alreadybeing used to detect ransomware, variants are being developed to specifically evade detection when using dynamic machine learning techniques. In this paper we introduce NetConverse, a machine learning analysis of Windows ransomware network traffic to achieve a high, consistent detection rate. Using a dataset created from conversation-based network traffic features we achieved a true positive detection rate of 97.1% using the Decision Tree (J48) classifier
Leveraging machine learning techniques for windows ransomware network traffic detection
No full textRansomware has become a significant global threat with the ransomware-as-a-service model enabling easy availability and deployment, and the potential for high revenues creating a viable criminal business model. Individuals, private companies or public service providers e.g. healthcare or utilities companies can all become victims of ransomware attacks and consequently suffer severe disruption and financial loss. Although machine learning algorithms are alreadybeing used to detect ransomware, variants are being developed to specifically evade detection when using dynamic machine learning techniques. In this paper we introduce NetConverse, a machine learning analysis of Windows ransomware network traffic to achieve a high, consistent detection rate. Using a dataset created from conversation-based network traffic features we achieved a true positive detection rate of 97.1% using the Decision Tree (J48) classifier
- …
