Buffer overflow vulnerabilities in CUDA: a preliminary analysis

We present a preliminary study of buffer overflow vulnerabilities in CUDA software running on GPUs. We show how an attacker can overrun a buffer to corrupt sensitive data or steer the execution flow by overwriting function pointers, e.g., manipulating the virtual table of a C++ object. In view of a potential mass market diffusion of GPU accelerated software this may be a major concern.     

Subroutine based detection of APT malware

Statistical detection of mass malware has been shown to be highly successful. However, this type of malware is less interesting to cyber security officers of larger organizations, who are more concerned with detecting malware indicative of a targeted attack. Here we investigate the potential of statistically based approaches to detect such malware using a malware family associated with a large number of targeted network intrusions. Our approach is complementary to the bulk of statistical based malware classifiers, which are typically based on measures of overall similarity between executable files. One problem with this approach is that a malicious executable that shares some, but limited, functionality with known malware is likely to be misclassified as benign. Here a new approach to malware classification is introduced that classifies programs based on their similarity with known malware subroutines. It is illustrated that malware and benign programs can share a substantial amount of code, implying that classification should be based on malicious subroutines that occur infrequently, or not at all in benign programs. Various approaches to accomplishing this task are investigated, and a particularly simple approach appears the most effective. This approach simply computes the fraction of subroutines of a program that are similar to malware subroutines whose likes have not been found in a larger benign set. If this fraction exceeds around 1.5 %, the corresponding program can be classified as malicious at a 1 in 1000 false alarm rate. It is further shown that combining a local and overall similarity based approach can lead to considerably better prediction due to the relatively low correlation of their predictions.     

Building 3D semantic maps for mobile robots using RGB-D camera

The wide availability of affordable RGB-D sensors changes the landscape of indoor scene analysis. Years of research on simultaneous localization and mapping (SLAM) have made it possible to merge multiple RGB-D images into a single point cloud and provide a 3D model for a complete indoor scene. However, these reconstructed models only have geometry information, not including semantic knowledge. The advancements in robot autonomy and capabilities for carrying out more complex tasks in unstructured environments can be greatly enhanced by endowing environment models with semantic knowledge. Towards this goal, we propose a novel approach to generate 3D semantic maps for an indoor scene. Our approach creates a 3D reconstructed map from a RGB-D image sequence firstly, then we jointly infer the semantic object category and structural class for each point of the global map. 12 object categories (e.g. walls, tables, chairs) and 4 structural classes (ground, structure, furniture and props) are labeled in the global map. In this way, we can totally understand both the object and structure information. In order to get semantic information, we compute semantic segmentation for each RGB-D image and merge the labeling results by a Dense Conditional Random Field. Different from previous techniques, we use temporal information and higher-order cliques to enforce the label consistency for each image labeling result. Our experiments demonstrate that temporal information and higher-order cliques are significant for the semantic mapping procedure and can improve the precision of the semantic mapping results.     

Adaptive RSSI-based localization scheme for wireless sensor networks

Range of applications for Wireless Sensor Networks (WSNs) is increasing rapidly. One class of such applications is Energy-Aware Wireless Positioning Systems for situation awareness. Localization deals with determining a target node’s position in WSN by analyzing signals exchanged between nodes. Received Signal Strength Indicator (RSSI) represents the ratio between received signal power and a reference power, and is typically used to estimate distances between nodes. RSSI distance estimations are affected by many factors. This paper aims to enhance the accuracy of RSSI-based localization techniques in ZigBee Networks through studying the communication channel status between two nodes. As the network nodes are exposed to high noise levels, position estimation accuracy deteriorates. A novel adaptive localization scheme is proposed; Two-State Markov model with moving average is employed to detect unpredictable RSSI readings that may reflect badly on the estimation. The proposed scheme achieves better estimation accuracy, for example, the estimation error was reduced from 11.7 m to just 3 m using the proposed scheme.     

Providing security and fault tolerance in P2P connections between clouds for mHealth services

The mobile health (mHealth) and electronic health (eHealth) systems are useful to maintain a correct administration of health information and services. However, it is mandatory to ensure a secure data transmission and in case of a node failure, the system should not fall down. This fact is important because several vital systems could depend on this infrastructure. On the other hand, a cloud does not have infinite computational and storage resources in its infrastructure or would not provide all type of services. For this reason, it is important to establish an interrelation between clouds using communication protocols in order to provide scalability, efficiency, higher service availability and flexibility which allow the use of services, computing and storage resources of other clouds. In this paper, we propose the architecture and its secure protocol that allows exchanging information, data, services, computing and storage resources between all interconnected mHealth clouds. The system is based on a hierarchic architecture of two layers composed by nodes with different roles. The routing algorithm used to establish the connectivity between the nodes is the shortest path first (SPF), but it can be easily changed by any other one. Our architecture is highly scalable and allows adding new nodes and mHealth clouds easily, while it tries to maintain the load of the cloud balanced. Our protocol design includes node discovery, authentication and fault tolerance. We show the protocol operation and the secure system design. Finally we provide the performance results in a controlled test bench.     

Taking a free ride for routing topology inference in peer-to-peer networks

A Peer-to-Peer (P2P) network can boost its performance if peers are provided with underlying network-layer routing topology. The task of inferring the network-layer routing topology and link performance from an end host to a set of other hosts is termed as network tomography, and it normally requires host computers to send probing messages. We design a passive network tomography method that does not require any probing messages and takes a free ride over data flows in P2P networks. It infers routing topology based on end-to-end delay correlation estimation (DCE) without requiring any synchronization or cooperation from the intermediate routers. We implement and test our method in the real world Internet environment and achieved the accuracy of 92 % in topology recovery. We also perform extensive simulation in OMNeT++ to evaluate its performance over large scale networks, showing that its topology recovery accuracy is about 95 % for large networks.     

An incentive game based evolutionary model for crowd sensing networks

Crowd sensing networks can be used for large scale sensing of the physical world or other information service by leveraging the available sensors on the phones. The collector hopes to collect as much as sensed data at relatively low cost. However, the sensing participants want to earn much money at low cost. This paper examines the evolutionary process among participants sensing networks and proposes an evolutionary game model to depict collaborative game phenomenon in the crowd sensing networks based on the principles of game theory in economics. A effectively incentive mechanism is established through corrected the penalty function of the game model accordance with the cooperation rates of the participant, and corrected the game times in accordance with it’s payoff. The collector controls the process of game by adjusting the price function. We find that the proposed incentive game based evolutionary model can help decision makers simulate evolutionary process under various scenarios. The crowd sensing networks structure significantly influence cooperation ratio and the total number of participant involved in the game, and the distribution of population with different game strategy. Through evolutionary game model, the manager can select an optimal price to facilitate the system reach equilibrium state quickly, and get the number of participants involved in the game. The incentive game based evolutionary model in crowd sensing networks provides valuable decision-making support to managers.     

Emotional Loneliness and Coping Strategies: A Reference to Older Malaysians at Nursing Homes

This article presents an in-depth qualitative study using a phenomenological approach to understand loneliness among elderly individuals in Malaysia. The objective of the study was to understand how the Malaysian elderly perceive and understand social isolation as well as loneliness, with the aim of identifying the factors that cause emotional loneliness among the elderly in nursing homes. In addition, this study also explored their coping strategies when dealing with loneliness. Semi-structured interviews were conducted with ten elderly participants from two different nursing homes in Kuala Lumpur and Selangor with representatives from the three major ethnic groups of Malaysia. Based on the results, there are several factors that cause the elderly to feel lonely – health factors, lack of family ties, and the lack of communication and cognitive factors, such as memory and perception. It was also found that internal (expectations and optimism) and external (work and activities) coping strategies play major roles in overcoming loneliness. In conclusion, some recommendations are made to respective party families and the government to consider when developing plans to help the elderly overcome loneliness, which could strengthen the family and social support system in Malaysia.     

Socio-Cultural Connections and Ruptures: Social Roles of Older Adults in Chile

This research focused on determining the sociocultural factors that build the social perception of the social roles of senior citizens in Chile in the 2002–2012 period. For this purpose, public opinion was studied in newspapers El Mercurio and La Cuarta. Specialized opinion included the Senior Citizen Program of Pontificia Universidad Católica de Chile, the National Service for Senior Citizens, the Social Observatory for Aging and Old Age of Universidad de Chile, and the Society of Geriatrics and Gerontology of Chile. A comprehensive methodology was used, along with a bibliographical four-stage design. The following ten sociocultural factors were identified: Health and Quality of Life, Studies and Data, Social Images of Aging, Gerontological Concepts and Sociopolitical Participation, Gerontological Policies, Plans and Principles, Aging, Production and Gender, Institutionalization, Demographic Growth, Gerontological Training, and Exclusion and Old Age. This work suggests modifying the conceptual premise that emphasizes the inexistence of a social role for elderly people with a vision centered on the heterogeneity of social roles under permanent sociocultural construction.