A Gray-Box Vulnerability Discovery Model Based on Path Coverage

Web browsers are primary targets of attacks because of their extensive uses and the fact that they interact with sensitive data. Vulnerabilities present in a web browser can pose serious risk to millions of users. Thus, it is pertinent to address these vulnerabilities to provide adequate protection for personally identifiable information. Research done in the past has showed that few vulnerability discovery models (VDMs) highlight the characterization of vulnerability discovery process. In these models, severity which is one of the most crucial properties has not been considered. Vulnerabilities can be categorized into different levels based on their severity. The discovery process of each kind of vulnerabilities is different from the other. Hence, it is essential to incorporate the severity of the vulnerabilities during the modelling of the vulnerability discovery process. This paper proposes a model to assess the vulnerabilities present in the software quantitatively with consideration for the severity of the vulnerabilities. It is possible to apply the proposed model to approximate the number of vulnerabilities along with vulnerability discovery rate, future occurrence of vulnerabilities, risk analysis, etc. Vulnerability data obtained from one of the major web browsers (Google Chrome) is deployed to examine goodness-of-fit and predictive capability of the proposed model. Experimental results justify the fact that the model proposed herein can estimate the required information better than the existing VDMs.

Software vulnerabilities trend over time has been proposed by various researchers and academicians in recent years. But none of them have considered operational coverage function in vulnerability discovery modeling. In this research paper, we have proposed a generalized statistical model that determines the relationship between operational coverage function and the number of expected vulnerabilities. During the operational phase, possible vulnerable sites are covered and vulnerabilities present at a particular site are discovered with some probability. We have assumed that the proposed model follows the nonhomogeneous Poisson process properties; thus, different distributions are used to formulate the model. The numerical illustration shows that the proposed model performs better and has the good fitness to the Google Chrome data. The second focus of this research paper is to evaluate the total cost incurred by the developer after software release and to identify the optimal vulnerability disclosure time through multiobjective utility function. The proposed vulnerability discovery helps in optimization. The optimal time problem depends on the combined effect of cost, risk, and effort.

A combined strategy of an improved simulated annealing (SA) algorithm and genetic algorithm is presented, with the goal of reducing the computational expenses. The improvements made on the SA algorithm include two parts, i.e., the adaptive regulating for the step vector, and the dynamic testing for the equilibrium of the Metropolis process. The proposed strategy has both the advantage of SA algorithm, the ability to avoid being trapped in a local optimum, and that of genetic algorithm, the ability to use the information about the searched states for the next iteration. A practical application on geometry optimization of pole shoes in large salient pole synchronous generators is effectively implemented using the strategy. The numerical results show that the number of iterations used by executing the combined strategy are only about 75% of those by executing basic SA algorithm.

To report or not to report? Extending Protection Motivation Theory to Vulnerability Discovery and Disclosure

As demonstrated in Chap. 3, different evolutionary algorithms, including genetic algorithm (GA), simulated annealing (SA) algorithm, hybrid GASA algorithm, particle swarm optimization (PSO) algorithm, continuous ant colony optimization (CACO) algorithm, artificial bee colony (ABC) algorithm, and immune algorithm (IA), are employed to determine suitable for parameter combination of an SVR-based electric load forecasting model. These forecasting results indicate that almost all SVR-based models with different evolutionary algorithms are superior to other competitive forecasting models (including ARIMA, HW, GRNN, and BPNN models); however, these algorithms almost lack knowledge memory or storage mechanisms which would be neither time-consuming nor inefficient in searching the suitable parameters, that is, premature convergence (being trapped in local optimum). For example, for SVRGA model, in GA processing, new individuals are generated by the following operators, selection, crossover, and mutation. For all types of objective functions, the generation begins with a binary coding for the parameter set. Based on this special binary coding process, GA is able to solve some specified problems which are not easily to be solved by traditional algorithms. GA can empirically provide a few best fitted offsprings from the whole population; however, after some generations, due to low diversity of the population, it might lead to a premature convergence [1]. For SVRSA model, SA algorithm is a generic probabilistic search technique that simulates the material physical process of heating and controlled cooling. Each step of SA attempts to replace the current state by a random move. The new state may then be accepted with a probability that depends both on the difference between the corresponding function values and also on a global parameter, temperature. Thus, SA has some institution to reach more ideal solutions. However, SA algorithm requires the subtle and skillful adjustment in the annealing schedule; therefore, the settings of the size of the temperature steps during annealing, the temperature range, the number of restarts, and the redirection of the search will be taken into account carefully [1]; in addition, its Monte Carlo scheme and lack of knowledge memory mechanism will also lead to time-consuming and inefficient searching in annealing process.

Novel computing paradigms for parameter estimation in Hammerstein controlled auto regressive auto regressive moving average systems

The forward kinematic of the 3-RPR parallel manipulator is solved using a hybrid meta-heuristic technique where the simulated annealing algorithm replaces the mutation operator in a genetic algorithm. The results from the hybrid meta-heuristic approach is compared with the standard simulated annealing and genetic algorithm. The results show that the simulated annealing algorithm outperforms genetic algorithm in terms of computation time and overall accuracy of the solution. The hybrid meta-heuristic search algorithm shows better performance than the standard genetic algorithm.

Aiming at the disadvantage of premature convergence of basic genetic algorithm, an adaptive simulated annealing genetic tabu search algorithm is proposed. This algorithm fully combines the global convergence and adaptability of simulated annealing algorithm and the strong climbing ability and high efficiency of tabu search strategy. It has strong convergence and adaptability. The simulation results of the adaptive simulated annealing genetic tabu search algorithm are given and compared with the basic genetic algorithm and simulated annealing algorithm. The simulation results show that the algorithm has better convergence and optimization performance, and can better solve the combinatorial optimization problem.

In this study, simulated annealing (SA) and genetic algorithm (GA) soft computing techniques are integrated to search for a set of optimal cutting conditions value that leads to the minimum value of machining performance. Twointegration systems are proposed; integrated SA–GA-type1 and integrated SA–GA-type2. The considered machining performance is surface roughness (R a) in end milling. The results of this study showed that both of the proposed integration systems managed to estimate the optimal cutting conditions, leading to the minimum value ofmachining performance when compared to the result of real experimental data. The proposed integration systems have also reduced the number of iteration in searching for the optimal solution compared to the conventional GA and conventional SA, respectively. In other words, the time for searching the optimal solution can be made faster by using the integrated SA–GA.

The LEACH is a popular protocol used in wireless sensor network analysis and simulation. This paper analyses the advantages and disadvantages of LEACH protocol and then puts forward a clustering routing protocol for energy balance of wireless sensor network based on simulated annealing and genetic algorithm. When the sensor nodes are deployed randomly in the area, Firstly, we cluster the sensor nodes by simulated annealing and genetic algorithm and then calculate the cluster center of each cluster. If the energy of the node in the cluster is higher than the average energy of the cluster, it will become the candidate cluster head; at last the candidate cluster head becomes the cluster head according to the distance from the cluster center of the cluster. Simulations show that the new program could improve Energy Hotspot caused by the uneven distribution of cluster head in LEACH protocol, thus it can balance the wireless sensor network load balance and extend the lifecycle of wireless sensor network.

Genetic Algorithm (GA) is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it avoids converging to the local optimum. However, it takes too much CPU time in the late process of GA. On the other hand, in the late process Simulated Annealing (SA) converges faster than GA but it is easily trapped to local optimum. In this letter, a useful method that unifies GA and SA is introduced, which utilizes the advantage of the global search ability of GA and fast convergence of SA. The experimental results show that the proposed algorithm outperforms GA in terms of CPU time without degradation of performance. It also achieves highly comparable placement cost compared to the state-of-the-art results obtained by Versatile Place and Route (VPR) Tool.

Scheduling final exams for large numbers of courses and students in universities is an intractable problem. Where scheduling is done manually, conflicts and unfairness are inevitable. Conflicts occur when simultaneous exams are scheduled for the same student, and unfairness to a student refers to consecutive exams or more than two exams on the same day. A good exam schedule should aim to minimize conflicts and the two unfairness factors based on user-assigned weights to these three factors and subject to some constraints such as classrooms’ number and capacities. In this work, we use a modified weighted-graph coloring problem formulation and adapt two stochastic search algorithms for solving the problem. The two algorithms are a simulated annealing algorithm (SA) and a genetic algorithm (GA). We also propose an improvement to a ‘good’ clustering-based heuristic procedure, known as FESP, by using simulated annealing procedures. The improved heuristic is referred to as FESP-SA. Then, we empirically compare the three proposed algorithms and FESP using realistic data. Our experimental results show that SA and GA produce good exam schedules that are better than those of FESP heuristic procedure. Also, SA and GA allow a reduction in the number of exam days without much aggravating conflicts and unfairness. However, SA is more favorable since it is faster than GA.

At present, researches on intelligent optimization problems such as China travel salesman problem (C-TSP) have obtained research results such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), Simulated Annealing Algorithm (SA), etc., but the single algorithm also exposes practical problems such as low efficiency and accuracy in the solving process. In order to improve the present situation effectively, this paper proposed an intelligent optimization pipeline method based on the idea of industrial production line flow. This method solved the C-TSP problem by combining the genetic simulated annealing (GA-SA), ant colony optimization simulated annealing (ACO-SA) algorithm pipeline. Experiments show that the efficiency of this method is improved by 4%-10%.

SUMMARY Many interesting inverse problems in geophysics are non-linear and multimodal. Parametrization of these problems leads to an objective function, or measure of agreement between data and model predictions, that has a complex topography with many local minima. Optimization algorithms that rely on local gradients in the objective function or that search the model space locally may become trapped in these local minima. By combining simulated annealing with the downhill simplex method, a hybrid global search algorithm is presented in this paper for non-linear, multimodal, inverse problems. The hybrid algorithm shares the advantages of both local search methods that perform well if the local model is suitable, and global methods that are able to explore efficiently the full model space. The hybrid algorithm also utilizes a larger and more complex memory to store information on the objective function than simulated annealing algorithms. The effectiveness of this new scheme is evaluated in three problems: minimization of the multidimensional Rosenbrock function, non-linear, 1 -D, acoustic waveform inversion, and residual statics. The performance of the hybrid algorithm is compared with simulated annealing and genetic algorithms and is shown to converge more rapidly and to have a higher success rate of locating the global minimum for the cases investigated.

The forward kinematics of the general Gough platform, namely the 6–6 parallel manipulator is solved using hybrid meta-heuristic techniques in which the simulated annealing algorithm replaces the mutation operator in a genetic algorithm. The results are compared with the standard simulated annealing and genetic algorithm. It shows that the standard simulated annealing algorithm outperforms standard genetic algorithm in terms of computation time and overall accuracy of the solution on this problem. However, the hybrid meta-heuristic paradigm shows the best performance in terms of accuracy and success rate.