Hunt Process Research Articles

An Efficient Approach of Threat Hunting Using Memory Forensics

The capacity and occurrence of new cyber-attacks have shattered in recent years. Such measures have very complicated workflows and comprise multiple illegal actors and organizations. Threat hunting demonstrates the process of proactively searching through networks for threats based on zero-day attacks by repeating the hunting process again and again. Unlike threat intelligence, it uses different automated security tools to collect logs in order to provide a pattern for making new intelligence-based tools by following those logs. According to our research findings about “threat hunting tools” there’s a major flaw that the designed tools are limited to the collection of logs. It works completely on logs for generating new patterns avoiding system’s main memory. Codes written directly to memory fail this process to provide proactive hunting. To overcome this major challenge, we are proposing two distinct methods, either by generating malicious code alerts or by binding memory forensics processes with threat hunting tools to make active hunting possible

An Approach Using Adaptive Weighted Least Squares Support Vector Machines Coupled with Modified Ant Lion Optimizer for Dam Deformation Prediction

A dam deformation prediction model based on adaptive weighted least squares support vector machines (AWLSSVM) coupled with modified Ant Lion Optimization (ALO) is proposed, which can be utilized to evaluate the operational states of concrete dams. First, the Ant Lion Optimizer, a novel metaheuristic algorithm, is used to determine the punishment factor and kernel width in the least squares support vector machine (LSSVM) model, which simulates the hunting process of antlions in nature. Second, aiming to solve the premature convergence phenomenon, Levy flight is introduced into the ALO to improve the global optimization ability. Third, according to the statistical characteristics of the datum error, an improved normal distribution weighting rule is applied to update the weighted value of data samples based on the learning result of the LSSVM model. Moreover, taking a concrete arch dam in China as an example, the horizontal displacement recorded by a pendulum is used as a study object. The accuracy and validity of the proposed model are verified and evaluated based on the four evaluating criteria, and the results of the proposed model are compared with those of well-established models. The simulation results demonstrate that the proposed model outperforms other models and effectively overcomes the influence of outliers on the performance of the model. It also has high prediction accuracy, produces excellent generalization performance, and can be a promising alternative technique for the analysis and prediction of dam deformation and other fields, including flood interval prediction, the stock price market, and wind speed forecasting.

Visual computing resources distribution and balancing by multimodal cat swarm optimization

According to the fast development of Internet technologies, nowadays more and more web applications require accessing and processing massive-scale visual data, such as content image search and automatic navigation. Practically, the computing power of a single personal PC is insufficient for handling such massive-scale visual elements. Owing to the advancement of cloud platforms, this problem can be well handled. In this work, we study the problem of how to optimally distribute the P visual processing tasks to Q computing resources. Specifically, we proposed an enhanced multimodal cat swarm optimization (MCSO) algorithm to fulfill this task. Given a huge number of images/videos, we first extract color, texture, and semantic channel to represent the visual content of each image/video. Afterward, we develop a multi-view feature learning algorithm to intelligently combine the multiple features into a descriptive one, wherein the weights of different feature channels are adjusted automatically. Subsequently, we use the CSO algorithm to assign each image/video to different remote servers. The CSO algorithm mimics the conventional cat hunting process, that is, a set of cats is divided into two groups, one for searching and the other for tracking. The two patterns are interacted by the mixture ratio, which indicates how many cats will conduct tracking in the next round. Based on the output of CSO, each image/video will be assigned to the most optimal remote sensor. Finally, based on the optimal assignment, the image/video processing can be conducted with the minimal time consumption. Comprehensive experimental comparisons have demonstrated the advantages of our method, that is, our MCSO can achieve a visual processing time three times faster than the second best performer.

Antlion optimization algorithm for optimal non-smooth economic load dispatch

This paper presents applications of Antlion optimization algorithm (ALO) for handling optimal economic load dispatch (OELD) problems. Electricity generation cost minimization by controlling power output of all available generating units is a major goal of the problem. ALO is a metaheuristic algorithm based on the hunting process of Antlions. The effect of ALO is investigated by solving a 10-unit system. Each studied case has different objective function and complex level of restraints. Three test cases are employed and arranged according to the complex level in which the first one only considers multi fuel sources while the second case is more complicated by taking valve point loading effects into account. And, the third case is the highest challenge to ALO since the valve effects together with ramp rate limits, prohibited operating zones and spinning reserve constraints are taken into consideration. The comparisons of the result obtained by ALO and other ones indicate the ALO algorithm is more potential than most methods on the solution, the stabilization, and the convergence velocity. Therefore, the ALO method is an effective and promising tool for systems with multi fuel sources and considering complicated constraints.

Hunting problems of multi-quadrotor systems via bearing-based hybrid protocols with hierarchical network**Project supported by the National Natural Science Foundation of China (Grant Nos. 61673217 and 61673214), the National Defense Basic Scientific Research Program of China (Grant No. JCKY2019606D001), and the China Scholarship Council.

Bearing-based hunting protocols commonly adopt a leaderless consensus method, which requests an entire state of the target for each agent and ignores the necessity of collision avoidance. We investigate a hunting problem of multi-quadrotor systems with hybrid bearing protocols, where the quadrotor systems are divided into master and slave groups for reducing the onboard loads and collision avoidance. The masters obtain the entire state of the target, whose hybrid protocols are based on the displacement and bearing constraints to maintain formation and to avoid the collision in the hunting process. However, the slaves’ protocols merely depend on the part state of the masters to reduce loads of data transmission. We also investigate the feasibility of receiving the bearing state from machine vision. The simulation results are given to illustrate the effectiveness of the proposed hybrid bearing protocols.

Large deviations for intersection measures of some Markov processes

AbstractConsider an intersection measure of p independent (possibly different) m‐symmetric Hunt processes up to time t in a metric measure space E with a Radon measure m. We derive a Donsker–Varadhan type large deviation principle for the normalized intersection measure on the set of finite measures on E as , under the condition that t is smaller than life times of all processes. This extends earlier work by W. König and C. Mukherjee [16], in which the large deviation principle was established for the intersection measure of p independent N‐dimensional Brownian motions before exiting some bounded open set . We also obtain the asymptotic behavior of logarithmic moment generating function, which is related to the results of X. Chen and J. Rosen [7] on the intersection measure of independent Brownian motions or stable processes.Our results rely on assumptions about the heat kernels and the 1‐order resolvents of the processes, hence include rich examples. For example, the assumptions hold for with when the processes enjoy (sub‐)Gaussian type or jump type heat kernel estimates, where is determined by the Hausdorff dimension of E and the so‐called walk dimensions of the processes.

Faber-Krahn type inequalities and uniqueness of positive solutions on metric measure spaces

We consider a general class of metric measure spaces equipped with a strongly local regular Dirichlet form and provide a lower bound on the hitting time probabilities of the associated Hunt process. Using these estimates we establish (i) a generalization of the classical Lieb's inequality, and (ii) uniqueness of nonnegative super-solutions to semi-linear elliptic equations on metric measure spaces. Finally, using heat-kernel estimates we generalize the local Faber-Krahn inequality recently obtained in [28] to local and non-local Dirichlet spaces.

Hybrid Binary Gray Wolf Optimization for finding Optimal Features in Classification Problems

Finding the essential symptoms(features) is highly demanded in the area of medical applications. Binary Gray wolf optimizer (BGWO) is one of the latest bio-inspired optimization techniques, which simulate the hunting process of gray wolves in nature. In this work, the binary gray wolf optimization (BGWO) is applied to select important feature subset for classification purposes and to attain maximum accuracy with minimum number of features using various classification algorithms and data sets. The classification error rate and the number of features are considered in the objective function. The wolf with low error rate and minimal features is considered as the best wolf and this kind of problem is a minimization problem. In BGWO, at each iteration the positions of three wolves (alpha wolf , beta wolf, delta wolf ) are identified. All the wolves move toward these three wolves to find out the target position. If the position of best solution (alpha wolf) is stuck in local minimum, the genetic algorithm (GA) is employed to get rid of it. The Hybrid binary gray wolf optimization with genetic algorithm (HBGWO) is used for classification problems in finding out the optimal feature subset with maximizing the classification accuracy while minimizing the number of selected features. The proposed HBGWO is used with the classification algorithms such as Naive Bayes, K-Nearest Neighbour and Decision Tree for different medical datasets. Results proved that the capability proposed HBGWO improves classification accuracy over BGWO.

A Multi-criteria Approach for Distribution Network Expansion Through Pooled MCDEA and Shannon Entropy

Abstract Power distribution network expansion planning (DNEP), based on innovative load flow analysis and optimization techniques, has drawn great attention of researchers around the world to cater for ever increasing demand of electrical power. In the present work, a new approach based on Multi-criteria Data Envelopment Analysis and Shannon Entropy analysis (MCDEA-SEA) is presented that strengthens the solution hunt process of DNEP problems. In the first stage of proposed methodology, various probable configurations are determined through load flow analysis considering different objective functions and constraints. In the next stage, large amount of complex data sets generated for various configurations from power flow perusal, then analyzed using pooled MCDEA-SEA. This multi-stage approach expedites search for best and impeccable solution, which leaves no space for sub-optimality. The efficacy of the proposed method has been verified by applying it on IEEE 33-bus test distribution system, considering impending load scenarios and the results show that the proposed methodology can effectively solve DNEP problem and provide optimal solution for DNEP problems, consuming lesser computation time compared to conventional approaches.

Application of Modified ALO to Economic Load Dispatch for Coal Fired Stations

This manuscript explores the novel Modified Ant Lion (MALO) Optimization to resolve the load dispatch problem optimally. The primary role of Optimal Economic Load Dispatch (OELD) is to obtain an efficient and economical operation of a power system network. The main aim of performing the OELD is for minimizing the fuel cost of the real power generation when the losses are neglected. An Modified Ant Lion (MALO) Optimization is a narrative nature-inspired algorithm and apes the tracking mechanism of ant lions in life. The random walk of ants, building traps, entrapment of ants in traps, catching preys, and re-building traps are the five main steps involved in the hunting process. The principal objective of OELD is to curtail the total generation cost while honoring effective constraints of accessible generating resources. The anticipated Modified ALO technique is utilized on three and six unit test system to various load demands for solving the load dispatch problem economically. A statistical result clarifies that the anticipated MALO method has lowest fuel cost and superior in quality of solution than other optimization techniques accounted in most latest literature.

Tuning of Control Parameters of Grey Wolf Optimizer using Fuzzy Inference

The Grey Wolf Optimizer (GWO) is a recent metaheuristic that can be explored in many applications. This paper proposes a mechanism to tune the control parameters that influence the hunting process in the GWO in order to improve its efficiency. This adjustment is made by a fuzzy inference system that uses the normalized fitness value of each wolf and the hunting mechanism control parameters of the GWO. The proposed fuzzy mechanism is tested and compared with the conventional GWO and another version that uses a fuzzy system as input information the ratio of the current iteration number and the maximum number of iterations. For performance analysis of the proposed fuzzy mechanism, all tested optimizers in ten benchmark optimization functions ran 1000 times. Simulation results show that the proposed fuzzy mechanism improves the convergence of the conventional GWO and it is competitive in relation to another fuzzy version adopted in the GWO design.

Efficient boosted grey wolf optimizers for global search and kernel extreme learning machine training

Grey wolf optimizer (GWO) is a new nature-inspired algorithm that simulates the predatory behaviors of grey wolves in nature. The GWO mainly divides the whole hunting process into three stages: encircling, hunting, and attacking when they are nearby the prey. Since its introduction, the GWO has found its applications in a wide range of engineering and science fields. However, when tackling more complex optimization problems, especially the high dimensional and multimodal tasks, GWO may easily fall into the local optima or be unsuccessful in finding the global best. In addition, convergence behaviors may not be very satisfying. In this study, the performance of basic GWO is enhanced using effective exploratory and exploitative mechanisms such as random leaders, opposition-based learning, levy fight patterns, random spiral-form motions, and greedy selection. These concepts are utilized to improve the global exploration and local exploitation capacities of the conventional technique and deepen the searching advantages of GWO in dealing with more complex problems. Also, the proposed mechanisms can ameliorate the convergence inclinations and the quality of the solutions. In order to verify the efficacy of the proposed method, which is called OBLGWO; it is compared to a comprehensive set of the new and state-of-the-art optimizers on 23 benchmark test sets and 30 well-known CEC problems. Additionally, the proposed OBLGWO is also applied to the tuning of the key parameters of kernel extreme learning machine (KELM) in dealing with two real-world problems. The experimental results and analysis demonstrate that the proposed OBLGWO can significantly outperform GWO, previous enhanced GWO variants and some of the other well-established algorithms in terms of convergence speed and the quality of solutions.

Multi-Threshold Image Segmentation Based on Improved Grey Wolf Optimization Algorithm

Threshold selection is the key to image threshold segmentation. The threshold determines the good or bad of the image segmentation results. As the number of thresholds increases, the computational process of image segmentation becomes more and more complicated. In order to select a better threshold for image segmentation, an improved Grey Wolf Optimization (IGWO) algorithm is proposed in this paper. The good point set method is applied to the GWO to generate the initial population, the weight is introduced in the position update of gray wolf hunting process, and the algorithm is used to solve the global optimization problem with the Kapur partition function as the objective function. The IGWO algorithm has good global convergence and computational robustness, can effectively avoid falling into local optimum, and is especially suitable for solving high-dimensional and multi-peak complex function problems, and can be well integrated into the image segmentation process. The results of theoretical analysis and simulation experiments show that compared with the image segmentation results of particle swarm optimization (PSO), the algorithm has better segmentation effect when multiple segmentation thresholds are selected. The segmentation efficiency, the threshold range obtained by optimization is more stable, and the segmentation quality is higher.

Doubly Feller Property of Brownian Motions with Robin Boundary Condition

In this paper, we consider first order Sobolev spaces with Robin boundary condition on unbounded Lipschitz domains. Hunt processes are associated with these spaces. We prove that the semigroup of these processes are doubly Feller. As a corollary, we provide a condition for semigroups generated by these processes being compact.

Shark Smell Optimizer applied to identify the optimal parameters of the proton exchange membrane fuel cell model

Proton Exchange Membrane Fuel Cells regarded as promising devices for energy conversion systems. This study aims to introduce an exact modeling scheme for the proton exchange membrane fuel cell, which can imitate and model the electrical, electrochemical features of an actual Proton Exchange Membrane Fuel Cell stack. Most of the developed schemes are experimental, multi-variable, and comprise various non-linear terms which must be assessed precisely to assure a decent estimation. In this study, a novel optimization model is employed to determine the Proton Exchange Membrane fuel cell parameters accurately. The Shark Smell Optimizer simulates the hunting process for a Shark. Hence, Shark Smell Optimizer is a nature-inspired and metaheuristic optimization algorithm depends on the rules of metaheuristic proficiency, exploration and exploitation terms to evade errors in local optimums and to obtain appropriate responses. The introduced Shark Smell Optimizing method is examined on five commercial proton exchange membrane fuel cells stack concerning empirical data. Many types of research and operation examination are executed to verify the effectuality of the proposed scheme. Additionally, for more authentication, Proton Exchange Membrane Fuel Cells modeling with Shark Smell Optimizer obtained data is compared with other optimization algorithm results. A statistical analysis also carried out for proposed method, the obtained show the reliability of Shark Smell Optimizer in comparison to other utilized techniques.

Traditional method of hunting edible termite, Odontotermes formosanus Shiraki by the Irulars of Gingee hills, South India

Background : Insects, especially termites are considered as one of the nutritious foods widely used by various ethnic communities throughout the world. Present study was undertaken among rural people in Gingee hills of Eastern Ghats, India to know about traditional hunting method for edible termites ( Odontotermes formosanus Shiraki) practiced by them to fulfill their protein requirement and as a source of revenue. Methods : Traditional method of hunting edible termites by Irulars of Periyamalai was observed for a couple of years. Preparation of herbal mixture, trapping method and other information were documented through direct observation in the field and by interviewing the individuals involved in termite hunting. Results : A total of 11 plant species were found to be used in termite hunting process and to treat diseases or allergies occurred (if any) after consumption of termites. Powdered roots of Aristolochia indica L., seeds of Canavalia cathartica Thouars and leaves of Glycosmis mauritiana (Lam.) Tanaka, are used to harvest termites from their mounds and to treat poisonous bites. Acalypha fruticosa Forssk ., Leucas aspera (Willd.) Link., Madhuca longifolia (J. Koenig ex L.) J.F. Macbr. Pavetta indica L. and Premna tomentosa Willd. are used by Irulars in treating skin allergic conditions after consuming termites. Conclusions : Traditional method of hunting edible termites with medicinal plants shows the rich traditional knowledge of the Irulars. There should be initiatives to identify and quantify the nutritional and nutraceutical value of termite species used by ethnic people for value addition, which in turn improve their livelihood. Also, it is essential to study the plants employed in termite hunting for their wound healing potential.

A hybrid grey wolf optimizer algorithm with evolutionary operators for optimal QoS-aware service composition and optimal selection in cloud manufacturing

Cloud manufacturing (CMfg), as a new service-oriented technology, is aiming towards delivering on-demand manufacturing services over the internet by facilitating collaboration among different producers with distributed manufacturing resources and capabilities. To this end, addressing service composition and optimal selection (SCOS) problem has been the pivotal challenge. This NP-hard combinatorial problem deals with selecting and combining the available resources into a composite service to meet the user’s requirements while keeping up the optimal quality of service. This study proposes a new hybrid approach based on the recently developed grey wolf optimizer (GWO) algorithm and evolutionary operators of the genetic algorithm. The embedded crossover and mutation operators carry out a twofold functionality: (1) they make it possible to adapt the continuous structure of GWO to a combinatorial problem such as SCOS, and (2) they help to avoid the local optimal stagnation at the hunting process by providing more exploration strength. A series of experiments were designed and conducted to prove the effectiveness of the proposed algorithm, and the experimental results demonstrated that the proposed algorithm delivers superior performance compared with that of both existing discrete variations of GWO and genetic algorithm, especially in large-scale SCOS problems.

Analyzing risk factors for shrinkage and transformation of East Kolkata Wetland, India

The East Kolkata Wetland, called the ‘kidney’ of city Kolkata has now become a significant issue for its gradual loss and modification of anthropogenic activities. A socioeconomic elucidation had been used in this article through the analysis using GIS and field observation. Changes of wetland area have been determined from 1991 to 2017 with Landsat images and accuracy assessment through ground truth verification. It is seen that loss of wetland from 1991 to 2001 was 23.55% where it was 7.34% in 2011 to 2017, mainly due to the result of land transformation for aquaculture activity. Hence, three phases of degradation have been observed in this study, these are: the phase of rapid degradation, the phase of controlled degradation and phase of transforming degraded. At the same time, it’s highlighted the loophole of management actions. A multi-criteria analysis technique with the analytic hierarchy process has been applied to determine the risk factors of wetland degradation and it is seen that expansion of built-up area, encroachment of cropland, the transformation of wetland into fishing ponds are responsible for wetland degradation. In the conclusion, it highlights the approaches of land developers and real estate agents in their wetland hunting process and emergence consequences on the environment as well as conserving approaches for sustainable management.

Cheetah chase algorithm (CCA): a nature-inspired metaheuristic algorithm

In recent years, appreciable attention among analysts to take care of the extraordinary enhancement issues utilizing metaheuristic algorithms in the domain area of Swarm Intelligence. Many metaheuristic algorithms have been developed by inspiring various nature phenomena’s. Exploration and exploitation are distinctive capacities and confine each other, along these lines, customary calculations require numerous parameters and bunches of expenses to accomplish the adjust, and furthermore need to modify parameters for various enhancement issues. In this paper, another populace based algorithm, the Cheetah Chase Algorithm (CCA), is presented. Distinctive features of Cheetah and their characteristics has been the essential motivation for advancement of this optimization algorithm. Cheetah Chase Algorithm (CCA) has awesome capacities both in exploitation and exploration, is proposed to address these issues. To start with, CCA endeavours to locate the optimal solution in the assigned hunt territory. It at that point utilizes history data to pursue its prey. CCA can, hence, decide the situation of the worldwide ideal. CCA accomplishes solid exploitation and exploration with these highlights. Additionally, as indicated by various issues, CCA executes versatile parameter change. The self-examination and analysis of this exploration show that each CCA capacity can have different beneficial outcomes, while the execution correlation exhibits CCAs predominance over conventional metaheuristic algorithms. The proposed Cheetah Chase Algorithm is developed by the process of hunting and chasing of Cheetah to capture its prey with the parameters of high speed, velocity and greater accelerations.

Variance-mediated multifractal analysis of group participation in chasing a single dangerous prey

Complexity of group participation in chasing a single dangerous prey is usually influenced by many factors such as the group size, the physiological fitness of predator, the hunting tactics and the running speed of prey. The group members may take diversified roles and switch their roles in the process of group hunting. In this paper, we study group participation in lions’ pride hunting by using a minimal cheating–pursuing model formulated in discrete-time random walks. Predator’s switching between cheating state and pursuing state is determined by a preset probability transition matrix which is regarded to be a product of the combination of various factors that influence group participation. The physiological constraints of lions require that the measurement of complexity of group participation must be built for short time series. On the basis of 300 simulated time steps, numerical results show that our model reasonably reflects the common expectation that the pride groups tightly in chasing a single slow prey while in chasing a single fast prey it groups loosely. We compute the distance between the group center and the center of the concerted pursuing for the quantification of group participation. Complexity embedded in short time series of these quantities is measured via a new method named variance-mediated multifractal analysis. This method is based on the partition function which is the weighted sum of probability measures of qth power. The short time series is divided into non-overlapping windows where the sample variance of each window is used to build the normalized probability measure. A strategy, “one-point indent for local past”, is designed to weigh the probability measures. The singularity spectrum exhibits more multifractality in the simulated group participation in chasing a fast prey and far less such in chasing a slow one, showing the potential applications of this method in studying short process of group hunting.