Joris Kinable
Katholieke Universiteit Leuven
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Publication
Featured researches published by Joris Kinable.
Journal of Computer Virology and Hacking Techniques | 2011
Joris Kinable; Orestis Kostakis
Each day, anti-virus companies receive tens of thousands samples of potentially harmful executables. Many of the malicious samples are variations of previously encountered malware, created by their authors to evade pattern-based detection. Dealing with these large amounts of data requires robust, automatic detection approaches. This paper studies malware classification based on call graph clustering. By representing malware samples as call graphs, it is possible to abstract certain variations away, enabling the detection of structural similarities between samples. The ability to cluster similar samples together will make more generic detection techniques possible, thereby targeting the commonalities of the samples within a cluster. To compare call graphs mutually, we compute pairwise graph similarity scores via graph matchings which approximately minimize the graph edit distance. Next, to facilitate the discovery of similar malware samples, we employ several clustering algorithms, including k-medoids and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Clustering experiments are conducted on a collection of real malware samples, and the results are evaluated against manual classifications provided by human malware analysts. Experiments show that it is indeed possible to accurately detect malware families via call graph clustering. We anticipate that in the future, call graphs can be used to analyse the emergence of new malware families, and ultimately to automate implementation of generic detection schemes.
European Journal of Operational Research | 2013
Patrick Schittekat; Joris Kinable; Kenneth Sörensen; Marc Sevaux; Frits C. R. Spieksma; Johan Springael
Existing literature on routing of school buses has focused mainly on building intricate models that attempt to capture as many real-life constraints and objectives as possible. In contrast, the focus of this paper is on understanding the joint problem of bus route generation and bus stop selection – two important sub-problems – in its most basic form. To this end, this paper defines the school bus routing problem (SBRP) as a variant of the vehicle routing problem in which three simultaneous decisions have to be made: (1) determine the set of stops to visit, (2) determine for each student which stop (s)he should walk to, and (3) determine routes that lie along the chosen stops, so that the total traveled distance is minimized. An MIP model of this basic problem is developed.
acm symposium on applied computing | 2011
Orestis Kostakis; Joris Kinable; Hamed Mahmoudi; Kimmo Olavi Mustonen
The amount of suspicious binary executables submitted to Anti-Virus (AV) companies are in the order of tens of thousands per day. Current hash-based signature methods are easy to deceive and are inefficient for identifying known malware that have undergone minor changes. Examining malware executables using their call graphs view is a suitable approach for overcoming the weaknesses of hash-based signatures. Unfortunately, many operations on graphs are of high computational complexity. One of these is the Graph Edit Distance (GED) between pairs of graphs, which seems a natural choice for static comparison of malware. We demonstrate how Simulated Annealing can be used to approximate the graph edit distance of call graphs, while outperforming previous approaches both in execution time and solution quality. Additionally, we experiment with opcode mnemonic vectors to reduce the problem size and examine how Simulated Annealing is affected.
Journal of Scheduling | 2016
Tony Wauters; Joris Kinable; Pieter Smet; Wim Vancroonenburg; Greet Van den Berghe; Jannes Verstichel
Scheduling projects is a difficult and time consuming process, and has far-reaching implications for any organization’s operations. By generalizing various aspects of project scheduling, decision makers are enabled to capture reality and act accordingly. In the context of the MISTA 2013 conference, the first MISTA challenge, organized by the authors, introduced such a general problem model: the Multi-Mode Resource-Constrained Multi-Project Scheduling Problem (MRCMPSP). The present paper reports on the competition and provides a discussion on its results. Furthermore, it provides an analysis of the submitted algorithms, and a study of their common elements. By making all benchmark datasets and results publicly available, further research on the MRCMPSP is stimulated.
Computers & Operations Research | 2014
Joris Kinable; Tony Wauters; Greet Van den Berghe
Abstract From an operational point of view, Ready-Mixed Concrete Suppliers are faced with challenging operational problems such as the acquisition of raw materials, scheduling of production facilities, and the transportation of concrete. This paper is centered around the logistical and distributional part of the operation: the scheduling and routing of concrete, commonly known as the Concrete Delivery Problem (CDP). The problem aims at finding efficient routes for a fleet of (heterogeneous) vehicles, alternating between concrete production centers and construction sites, and adhering to strict scheduling and routing constraints. Thus far, a variety of CDPs and solution approaches have appeared in academic research. However, variations in problem definitions and the lack of publicly available benchmark data inhibit a mutual comparison of these approaches. Therefore, this work presents a more fundamental version of CDP, while preserving the main characteristics of the existing problem variations. Both exact and heuristic algorithms for CDP are proposed. The exact solution approaches include a Mixed Integer Programming (MIP) model and a Constraint Programming model. Similarly, two heuristics are studied: the first heuristic relies on an efficient best-fit scheduling procedure, whereas the second heuristic utilizes the MIP model to improve delivery schedules locally. Computational experiments are conducted on new, publicly accessible, data sets; results are compared against lower bounds on the optimal solutions.
International Transactions in Operational Research | 2014
Joris Kinable; Frits C. R. Spieksma; G. Vanden Berghe
The School Bus Routing Problem (SBRP), a generalization of the well-known Vehicle Routing Problem, involves the routing, planning, and scheduling of public school bus transportation. The problem can be divided into several subproblems, including bus stop selection, assigning students to buses, and determining the bus routes. This work presents an exact branch-and-price framework for the SBRP, with a strong emphasis on efficiency issues inherently related to column generation (CG). Experiments are conducted on a set of 128 SBRP instances. Many of these instances are solved optimally; for the remaining instances, strong lower bounds have been derived. Furthermore, better integer solutions were found for a number of instances reported in the literature. Both lower bounds computed on the optimum solution and stabilization added to the CG procedure significantly improved computation times.
European Journal of Operational Research | 2017
Joris Kinable; Bart Smeulders; Eline Delcour; Frits C. R. Spieksma
Given a weighted graph G = (V, E), the Equitable Traveling Salesman Problem (ETSP) asks for two perfect matchings in G such that (1) the two matchings together form a Hamiltonian cycle in G and (2) the absolute difference in costs between the two matchings is minimized. The problem is shown to be NP-Hard, even when the graph G is complete. We present two integer programming models to solve the ETSP problem. One model is solved through branch-and-bound-and-cut, whereas the other model is solved through a branch-and-price-and-cut framework. A simple local search heuristic is also implemented. We conduct computational experiments on different types of instances, often derived from the TSPLib. It turns out that the behavior of the different approaches varies with the type of instances; however, the branch-and-bound-and-cut approach implemented in Cplex seems to work best overall.
integration of ai and or techniques in constraint programming | 2016
Joris Kinable; Willem Jan van Hoeve; Stephen F. Smith
In cold weather cities, snowstorms can have a significant disruptive effect on both mobility and safety, and consequently the faster that streets can be cleared the better. Yet in most cities, plans for snowplowing are developed using simple allocation schemes that while easy to implement can also be quite inefficient. In this paper we consider the problem of optimizing the routes of a fleet of snow plowing vehicles, subject to street network topology, vehicle operating restrictions, and resource (salt, fuel) usage and replenishment constraints. We develop and analyze the performance of three different optimization models: a mixed-integer programming (MIP) model, a constraint programming (CP) model, and a constructive heuristic procedure that is amplified by an iterative improvement search. The models are evaluated on a set of snow plow routing problems of various sizes, constructed using Open Streets map data of Pittsburgh PA. Experimental results are presented that illustrate the differential strengths and weaknesses of each model, and suggest an alternative hybrid solution approach.
European Journal of Operational Research | 2017
Joris Kinable; André A. Ciré; Willem Jan van Hoeve
In this paper, we introduce novel optimization methods for sequencing problems in which the setup times between a pair of tasks depend on the relative position of the tasks in the ordering. Our proposed methods rely on a hybrid approach where a constraint programming model is enhanced with two distinct relaxations: One discrete relaxation based on multivalued decision diagrams, and one continuous relaxation based on linear programming. Both relaxations are used to generate bounds and enhance constraint propagation. Experiments conducted on three variants of the time-dependent traveling salesman problem indicate that our techniques substantially outperform general-purpose methods, such as mixed-integer linear programming and constraint programming models.
integration of ai and or techniques in constraint programming | 2016
Joris Kinable
A global CP constraint is presented which improves the propagation of reservoir constraints on cumulative resources in schedules with optional tasks. The global constraint is incorporated in a CP approach to solve a Single-Commodity Pickup and Delivery Problem: the Bicycle Rebalancing Problem with Time-Windows and heterogeneous fleet. This problem was recently introduced at the 2015 ACP Summer School on Constraint Programming competition. The resulting CP approach outperforms a Branch-and-Bound approach derived from two closely related problems. In addition, the CP approach presented in this paper resulted in a first place position in the competition.