International audience

ICSOFT 2019, 14th International Conference on Software Technologies, Prague, TCHÈQUE, RÉPUBLIQUE, 26-/07/2019 - 28/07/2019

Selective forwarding attack is considered among the most dangerous attack in wireless sensor networks, particularly in mobile environment. The attackers compromise legitimate nodes and selectively drop some packets. In addition to that, the movement of some nodes increases link failures, collisions and packet loss. So, it will be more difficult to detect malicious nodes from legitimates ones. This paper focuses on detecting selective forwarding attackers in IPv6-based mobile wireless sensor networks when the standardized IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) is used. Contrarily to previous works which propose solutions to detect selective forwarding attack in static wireless sensor networks, this paper deals with the case of networks with mobile sensors and proposes a novel intrusion detection system that combines Sequential Probability Ratio Test with an adaptive threshold of acceptable probability of dropped packets. Extensive simulations were performed and the results show that the detection probability is 100%.

Security in mobile wireless sensor networks is a big challenge because it adds more complexity to the network in addition to the problems of mobility and the limited sensor node resources. Even with authentication and encryption mechanisms, an attacker can compromise nodes and get all the keying materials. Therefore, an intrusion detection system is necessary to detect and defend against the insider attackers. Currently, there is no intrusion detection system applied to IPv6-based mobile wireless sensor networks. This paper is mainly interested in detecting the selective forwarding and clone attacks because they are considered among the most dangerous attackers. In this work, the authors design, implement, and evaluate a novel intrusion detection system for mobile wireless sensor networks based on IPv6 routing protocol for low power and lossy networks. The new intrusion detection system can be extended to other attacks such as wormhole and sybil attacks. The simulations results show that the detection probability is 100% for selective attackers under some cases.

Many companies spend vast amounts of resources to collect, transform and store the massive amounts of data that flows through their business processes. When it comes to doing analysis and machine learning such as clustering on this data, time and compute speed gate determine how much data can be analyzed. Moreover, most Big Data clustering algorithms do not look at a complete, large dataset. Instead, they look at a subsample and work on approximations. However, work on samples can spread useful data that can be sources of value. In this paper, we use sampling combined with consensus strategy to dissemble the whole Big Data into small subsets, then basic partitions are locally generated from them using parallel processing. For the sampling part, we propose a partial data clustering (PDC) according to different nodes to classify the current sub-samples of partial data access (PDA) merged together with optimal prototypes generated from the last PDC and condensed into weighted points. For the consensus part, we apply a split-and-merge fuzzy clustering to equivalently transfer the consensus clustering problem into an optimization clustering one. Extensive experiments on several datasets demonstrate the ability to handle massive data and the consensus computing make the proposed classifier promising candidate for Big Data clustering.

This paper focuses on the performance of wireless sensor networks characterized by a hybrid topology composed of mobile and static sensor nodes. The Routing Protocol for Low power and lossy networks (RPL), which is standardized as an IPv6 routing protocol for low power and lossy networks, uses the trickle timer algorithm to handle changes in the network topology. However, this algorithm is not well adapted to dynamic environments. This paper enhances the trickle timer in order to fit with mobility requirements. Most of previous works have improved this algorithm without considering the random movement of nodes. In this work, the proposed timer algorithm takes into consideration the random trajectory of mobile nodes, pause time and node's velocity. It is also dynamically adjusted to prevent from node disconnections. The performance of the modified protocol is evaluated and compared with native RPL, MERPL and RPL with reverse Trickle. The results show that our protocol optimization offers better performance.

With consensus tendency, fuzzy clustering results are given in advance and the goal is to reconcile the results by building aggregate partition and prototypes. In this paper, we present a consensus-driven fuzzy clustering algorithm in which distributed data are progressively merged into intersites' clusters through global aggregation based on similarity relationships. The ensuring algorithm considers dispersion and dissimilarity between local partitions while respecting the confidentiality constraints which prohibits the data sharing. Several illustrative numeric tests using both synthetic data and those coming from publicity available machine learning repositories are also included. The experimental component of the study shows the efficiency of the proposed algorithm.

This paper focuses on wireless sensor networks (WSNs) for healthcare and medical applications which aim to look after patients by monitoring vital signs, diagnostics and drug administrations. WSNs in such applications are usually composed of different types of small-size battery powered sensors. In fact, wearable sensors are used to collect information about the signs of a person that needs a medical care. Static sensors, deployed inside the hospital or the home building, are used to collect information about indoor environment. The collected data is then transmitted towards the sink. Since some patients can move, WSN topology is hybrid and composed of mobile and static nodes. Routing Protocol for Low Power and Lossy Networks (RPL), an IPv6 distance vector routing protocol designed for static wireless sensor network, can be adapted to our need. So in this paper, we propose to modify RPL in order to fit with an hybrid topology. The performance of the modified protocol is evaluated and compared with the existing RPL protocol. The results show that our protocol optimization for healthcare application offers better performance.

Cloud computing has become a widely used concept. It is characterized by its elasticity, its on demand services and the unlimited provided resources. However, because of the complexity of customers service requests, their provision became an increasingly difficult task. In fact, in order to meet these requirements, services should be combined to implement a composite one performing the requested task. It is in this sense that the use of SOA architecture, which provides service composition, seems a useful solution. This paper investigates existing works on services discovery and composition in SOA as well as Cloud architectures. We study and compare these works and present an architecture of services composition based on SOA architecture and Cloud computing.

Traditional modelling and analysis of workflow aims at verifying the correctness of its control flow. When dealing with workflow security, the compliance of information flow with the adopted security policies needs also to be analyzed. In this paper, we propose a two-steps verification approach. While the first step is concerned by soundness of the workflow, the second one is concerned by the data consistency with respect to a multilevel security policy where the granting of access rights to objects by the workflow system is done according to information flow rules of Bell-LaPadula model. Our approach is based on the ECATNet formalism. It offers means to incorporate the security constraints on information flow into an initial WF net modeling the control flow of a workflow specification. We then show how to analyze the impact of the security rules on the whole Workflow through the model checker of the MAUDE environment and how to relax them before producing the correct specification and submitting it to the workflow system.

Fault avoidance, fault removal and fault tolerance represent three successive lines of defense against the contingency of faults in software systems and their impact on system reliability. Beyond the colorful discussions of the relative merits of these techniques, the law of diminishing returns advocates that they be used in concert, where each is applied whenever it is most effective. Such a premise remains an idle act of faith so long as these techniques cannot be captured by a uniform model. This paper proposes such a model and illustrates how it can be used in practice to improve the quality of software products.