Session Program

Abstract - Modern vehicles have lots of connectivity, this is the reason why protect in-vehicle network from cyber-attacks becomes an important issue. Controller Area Network (CAN) is a de facto standard for the in-vehicle network. However, lack of security features of CAN protocol makes vehicles vulnerable to attacks. The message injection attack is a representative attack type which injects fabricated messages to deceive original Electronic Control Units (ECUs) or to cause malfunctions. In this paper we propose a method able to detect four different type of attacks targeting the CAN protocol adopting fuzzy algorithms. We obtain encouraging result in attack identification, with a precision ranging from 0.85 to 1 using the fuzzy NN algorithm in the identification of attacks targeting CAN protocol.

Abstract - Mobile systems have become essential for communication and productivity but are also become target of continuous malware attacks. New malware are often obtained as variants of existing malicious code. This work describes an approach for dynamic malware detection based on the combination of Process Mining (PM) and Fuzzy Logic (FL) techniques. The firsts are used to characterize the behavior of an application identifying some recurring execution expressed as a set of declarative constraints between the system calls. Fuzzy logic is used to classify the analyzed malware applications and verify their relations with the existing malware variants. The combination of the two techniques allows to obtain a fingerprint of an application that is used to verify its maliciousness/trustfulness, establish if it belongs from a known malware family and identify the differences between the detected malware behavior and the other variants of the some malware family. The approach is applied on a dataset of 3000 trusted and malicious applications across twelve malware families and has shown a very good discrimination ability that can be exploited for malware detection and family identification.

Abstract - In the last years, the growing adoption of cloud-based multi-tiers systems has strongly increased the levels of resource sharing among companies, improving the enterprise efficiency, thanks to a refined business dynamism and a rapid decrease in costs. However, in spite of their advantages, this new business model highlights the emergence of new computational approaches aimed at the distribution and the optimization of resources sharing along so-called multi-tenants system, i.e., cloud-based architecture where a single instance of software runs on a single server and serves multiple companies (tenants). This paper faces this challenging gap by proposing an auto-scaling cloud computing multi-tenancy architecture where process mining and fuzzy-based load-balancing systems synergistically interact to provide an improved and optimized resource management distribution. A case study is carried out to show the proposed architecture in operation.

Abstract - The automatic diagnosis of systems is essential in several industries such as aeronautics. This paper introduces a method to diagnose systems with respect to the constraints of the aeronautics field: robustness and low computation costs. The proposed methodology is based on the combination of Support Vector Machine and Fuzzy Membership Functions (SVM-MBF). The distances, which are computed by the SVM, are fuzzified in order to give a degree of confidence in the classification. Besides, using SVM-MBF allows estimating the severity of a fault. The architecture of the proposed diagnosis system consists in putting in series one classifier to detect faults, with a set of classifiers, one per fault, to assess the severity. The method is applied to the diagnosis of inter- turn short-circuits of a Permanent Magnet Synchronous Machine (PMSM). The data come from measurements performed on a machine designed for aeronautics applications. The method is evaluated in terms of robustness and computation time by using cross validation. The results show the suitability of the methodology for aeronautics applications.

Abstract - Denial of service flood attacks are among the most common and powerful attacks which abuse the computational resources and the bandwidth of a network. In this paper, a heterogeneous defense method is proposed based on a combination of the Software Defined controller and fuzzy decision making. Numerical results show that the proposed method has a lower computational load and response time compared to the traditional methods centralized in the controller.

Abstract - Menu remains a key Menu remains a key element in influencing the success of restaurants which is very dynamic and highly competitive with a high failure rate within the first three years of operation. Menu engineering refers to the specific techniques used to evaluate performance of individual menu items leading to strategic decision. Many influencing elements are consolidated to two elements of popularity index and contribution margin providing four different combinations that can be used to choose decision options. Decision makers carry out menu engineering against manually set target values which are imprecise and choice of strategic options becomes erroneous and tedious. As a step toward providing a more powerful decision making tool this study presents a fuzzy multi- criteria decision making model to choose strategy decision options that extends the set of combinations from four to nine. The model uses trapezoidal fuzzy numbers for normalization and linguistic variables for fuzzification. The applicability of the model is tested using thirty menu items in four categories.