A novel word-independent gesture-typing continuous authentication scheme for mobile devices

In this study, we produce a new continuous authentication scheme for gesture-typing on mobile devices. Our scheme is the first scheme that authenticates gesture-typing interactions in a word-independent format. The scheme relies on groupings of features extracted from the word gesture after it has been reduced to parts common to all gestures. We show that movement sensors are also important in differentiating between users. We describe the feature extraction processes and analyse our proposed feature set. The unique process of our authentication scheme is presented and described. We collect our own gesture typing dataset including data collected during sitting, standing and walking activities for realism. We test our features against state-of-the-art touch-screen interaction features and compare feature extraction times on real mobile devices. Our scheme authenticates users with an equal error rate of 3.58% for a single word-gesture. The equal error rate is reduced to 0.81% when 3 word-gestures are used to authenticate

Evaluating the Provision of Botnet Defences using Translational Research Concepts.

Botnet research frequently draws on concepts from other fields. An example is the use of epidemiological models when studying botnet propagation, which facilitate an understanding of bot spread dynamics and the exploration of behavioural theory. Whilst the literature is rich with these models, it is lacking in work aimed at connecting the insights of theoretical research with day-to-day practice. To address this, we look at botnets through the lens of implementation science, a discipline from the field of translational research in health care, which is designed to evaluate the implementation process. In this paper, we explore key concepts of implementation science, and propose a framework-based approach to improve the provision of security measures to network entities. We demonstrate the approach using existing propagation models, and discuss the role of implementation science in malware defence

Modelling the Spread of Botnet Malware in IoT-Based Wireless Sensor Networks

The propagation approach of a botnet largely dictates its formation, establishing a foundation of bots for future exploitation. The chosen propagation method determines the attack surface, and consequently, the degree of network penetration, as well as the overall size and the eventual attack potency. It is therefore essential to understand propagation behaviours and influential factors in order to better secure vulnerable systems. Whilst botnet propagation is generally well-studied, newer technologies like IoT have unique characteristics which are yet to be thoroughly explored. In this paper, we apply the principles of epidemic modelling to IoT networks consisting of wireless sensor nodes. We build IoT-SIS, a novel propagation model which considers the impact of IoT-specific characteristics like limited processing power, energy restrictions, and node density on the formation of a botnet. Focusing on worm-based propagation, this model is used to explore the dynamics of spread using numerical simulations and the Monte Carlo method, and to discuss the real-life implications of our findings

Secure Anonymous Routing for MANETs Using Distributed Dynamic Random Path Selection

Most of the MANET security research has so far focused on providing routing security and confidentiality to the data packets, but less has been done to ensure privacy and anonymity of the communicating entities. In this paper, we propose a routing protocol which ensures anonymity, privacy of the user. This is achieved by randomly selecting next hop at each intermediate. This protocol also provides data security using public key ciphers. The protocol is simulated using in-house simulator written in C with OpenSSL crypto APIs. The robustness of our protocol is evaluated against known security attacks

Patient privacy protection using anonymous access control techniques

Objective: The objective of this study is to develop a solution to preserve security and privacy in a healthcare environment where health-sensitive information will be accessed by many parties and stored in various distributed databases. The solution should maintain anonymous medical records and it should be able to link anonymous medical information in distributed databases into a single patient medical record with the patient identity. Methods: In this paper we present a protocol that can be used to authenticate and authorize patients to healthcare services without providing the patient identification. Healthcare service can identify the patient using separate temporary identities in each identification session and medical records are linked to these temporary identities. Temporary identities can be used to enable record linkage and reverse track real patient identity in critical medical situations. Results: The proposed protocol provides main security and privacy services such as user anonymity, message privacy, message confidentiality, user authentication, user authorization and message replay attacks. The medical environment validates the patient at the healthcare service as a real and registered patient for the medical services. Using the proposed protocol, the patient anonymous medical records at different healthcare services can be linked into one single report and it is possible to securely reverse track anonymous patient into the real identity. Conclusion: The protocol protects the patient privacy with a secure anonymous authentication to healthcare services and medical record registries according to the European and the UK legislations, where the patient real identity is not disclosed with the distributed patient medical records

Security analysis of the micro transport protocol with a misbehaving receiver

BitTorrent is the most widely used Peer-to-Peer (P2P) protocol and it comprises the largest share of traffic in Europe. To make BitTorrent more Internet Service Provider (ISP) friendly, BitTorrent Inc. invented the Micro Transport Protocol (uTP). It is based on UDP with a novel congestion control called Low Extra Delay Background Transport (LEDBAT). This protocol assumes that the receiver always gives correct feedback, since otherwise this deteriorates throughput or yields to corrupted data. We show through experimental investigation that a misbehaving uTP receiver, which is not interested in data integrity, can increase the bandwidth of the sender by up to five times. This can cause a congestion collapse and steal large share of a victim’s bandwidth. We present three attacks, which increase the bandwidth usage significantly. We have tested these attacks in a real world environment and show its severity both in terms of number of packets and total traffic generated. We also present a countermeasure for protecting against the attacks and evaluate the performance of that defence strategy

Security in networks of unmanned aerial vehicles for surveillance with an agent-based approach inspired by the principles of blockchain

Unmanned aerial vehicles (UAVs) can support surveillance even in areas without network infrastructure. However, UAV networks raise security challenges because of its dynamic topology. This paper proposes a technique for maintaining security in UAV networks in the context of surveillance, by corroborating information about events from different sources. In this way, UAV networks can conform peer-to-peer information inspired by the principles of blockchain, and detect compromised UAVs based on trust policies. The proposed technique uses a secure asymmetric encryption with a pre-shared list of official UAVs. Using this technique, the wrong information can be detected when an official UAV is physically hijacked. The novel agent based simulator ABS-SecurityUAV is used to validate the proposed approach. In our experiments, around 90% of UAVs were able to corroborate information about a person walking in a controlled area, while none of the UAVs corroborated fake information coming from a hijacked UAV

Adaptive threshold scheme for touchscreen gesture continuous authentication using sensor trust

In this study we produce a continuous authentication scheme for mobile devices that adjusts an adaptive threshold for touchscreen interactions based on trust in passively collected sensor data. Our framework unobtrusively compares real-time sensor data of a user to historic data and adjusts a trust parameter based on the similarity. We show that the trust parameter can be used to adjust an adaptive threshold in continuous authentication schemes. The framework passively models temporal, spatial and activity scenarios using sensor data such as location, surrounding devices, wi-fi networks, ambient noise, movements, user activity, ambient light, proximity to objects and atmospheric pressure from study participants. Deviations from the models increases the level of threat the device perceives from the scenario. We also model the user touchscreen interactions. The touchscreen interactions are authenticated against a threshold that is continually adjusted based on the perceived trust. This scheme provides greater nuance between security and usability, enabling more refined decisions. We present our novel framework and threshold adjustment criteria and validate our framework on two state-of-the-art sensor datasets. Our framework more than halves the false acceptance and false rejection rates of a static threshold system

Trust Model for Optimized Cloud Services

Cloud computing with its inherent advantages draws attention for business critical applications, but concurrently expects high level of trust in cloud service providers. Reputation-based trust is emerging as a good choice to model trust of cloud service providers based on available evidence. Many existing reputation based systems either ignore or give less importance to uncertainty linked with the evidence. In this paper, we propose an uncertainty model and define our approach to compute opinion for cloud service providers. Using subjective logic operators along with the computed opinion values, we propose mechanisms to calculate the reputation of cloud service providers. We evaluate and compare our proposed model with existing reputation models

Efficient Privacy-Preserving Facial Expression Classification

This paper proposes an efficient algorithm to perform privacy-preserving (PP) facial expression classification (FEC) in the client-server model. The server holds a database and offers the classification service to the clients. The client uses the service to classify the facial expression (FaE) of subject. It should be noted that the client and server are mutually untrusted parties and they want to perform the classification without revealing their inputs to each other. In contrast to the existing works, which rely on computationally expensive cryptographic operations, this paper proposes a lightweight algorithm based on the randomization technique. The proposed algorithm is validated using the widely used JAFFE and MUG FaE databases. Experimental results demonstrate that the proposed algorithm does not degrade the performance compared to existing works. However, it preserves the privacy of inputs while improving the computational complexity by 120 times and communication complexity by 31 percent against the existing homomorphic cryptography based approach