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Detection of advanced persistent threat: A genetic programming approach

APPLIED SOFT COMPUTING 2024年 167卷

作者： Al Mamun, Abdullah Al-Sahaf, Harith Welch, Ian Mansoori, Masood Camtepe, Seyit Victoria Univ Wellington Sch Engn & Comp Sci Wellington New Zealand Al Zahraa Univ Women Coll Informat Technol Engn Karbala Iraq CSIRO Data61 Eveleigh Australia

Advanced Persistent Threats (APTs) are an intimidating class of cyberattacks known for their persistence, sophistication, and targeted nature. These attacks, coordinated by highly motivated adversaries, pose a grave risk to organizations and individuals, often operating stealthily and evading detection. While existing research primarily focuses on applying Machine Learning (ML) methods to analyze network traffic data for APT detection, this article introduces a novel approach that utilizes Genetic Programming (GP). The proposed method not only detects APT attacks but also identifies their specific life cycle stages through the evolutionary capabilities of GP. Its effectiveness lies in its ability to excel in detecting intricate patterns, even within classes with a limited number of instances, a feat that is often challenging for traditional ML techniques. The method involves evolving and optimizing its models to effectively learn and adapt to complex APT behaviors. Experimentation with a publicly available dataset showcases the efficacy of the proposed method across diverse APT stages. The results demonstrate that the proposed method, GPC, achieves a 3.71% improvement in balanced accuracy compared to the best-performing model from related works. Moreover, a thorough analysis of the best-evolved GP model uncovers valuable insights about identified features and significant patterns. This research advances the APT detection paradigm by leveraging GP's capabilities, providing afresh and effective perspective on countering these persistent threats.

关键词： APT Advanced Persistent Threat evolutionary computation Genetic Programming Machine Learning CKC

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Knowledge Learning-Based Dimensionality Reduction for Solving Large-Scale Sparse Multiobjective Optimization Problems

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IEEE TRANSACTIONS ON CYBERNETICS 2025年 PP卷 PP页

作者： Shao, Shuai Tian, Ye Zhang, Yajie Zhang, Xingyi Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China

Large-scale sparse multiobjective optimization problems (LSMOPs) are of great significance in the context of practical applications, such as critical node detection, feature selection, and pattern mining. Since many LSMOPs are pursued based on large datasets, they involve a large number of decision variables, resulting in a huge search space that is challenging to explore efficiently. To rapidly approximate sparse Pareto optimal solutions, some evolutionary algorithms have been proposed to reduce the dimensionality of LSMOPs. However, their adaptability to different LSMOPs remains limited due to their reliance on fixed dimensionality reduction schemes, which can potentially lead to local optima and inefficient utilization of function evaluations. To address this issue, a knowledge learning-based dimensionality reduction approach is proposed in this article. First, in the early stages of evolution, the impact of different dimensionality reduction schemes on the sparse distribution of the population is evaluated. Then, the multilayer perceptron is employed to learn the accumulated knowledge from the evolutionary process, thereby constructing a mapping model between the sparse features of the evolutionary process and the candidate dimensionality reduction schemes. Finally, the model recommends the best dimensionality reduction scheme in each generation, achieving a good balance between exploration and exploitation. Experimental evaluations on both benchmark and real-world LSMOPs demonstrate that an evolutionary algorithm incorporating the proposed knowledge learning-based dimensionality reduction approach outperforms most existing evolutionary algorithms.

关键词： Dimensionality reduction Vectors Optimization Pareto optimization Convergence Heuristic algorithms Search problems Particle swarm optimization Encoding Benchmark testing evolutionary computation large-scale optimization multiobjective optimization neural networks

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Personalized Exercise Group Assembly Using a Two Archive evolutionary Algorithm

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IEEE TRANSACTIONS ON EMERGING TOPICS IN computationAL INTELLIGENCE 2024年第3期9卷 2572-2583页

作者： Sun, Yifei Cao, Yifei Wang, Ziang Hou, Sicheng Gao, Weifeng Zhan, Zhi-hui Shaanxi Normal Univ Sch Phys & Informat Technol Xian 710119 Peoples R China Xidian Univ Sch Math & Stat Xian 710126 Peoples R China Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China

Traditional exercise recommendation algorithms generate exercise groups according to the features of exercises for all students. However, as different students may have different knowledge proficiencies, this article focuses on the personalized exercises group assembly (PEGA) to select exercises for each student based on their knowledge proficiencies, which is formulated as a constrained multi-objective problem. In order to solve the constrained multi-objective PEGA problem efficiently, this paper proposes a two archives evolutionary algorithm (TAEA) with three novel designs. Firstly, as the number of exercises is very large in PEGA, the traditional binary-number encoding method will result in high consumption in both memory and computation. To this end, a new integer-number encoding (INE) method is designed for solution representation. It saves memory for exercise subset representation, speeds up evaluations, and generates solutions that satisfy some constraints. Secondly, based on the INE method, the TAEA adopts two archives name convergence-oriented archive (CA) and diversity-oriented archive (DA). The CA ensures the convergence, driving force, and feasibility of the solutions. The DA aims to provide as diverse a solution as possible, including exploration of infeasible regions. Thirdly, a classification-based offspring co-reproduction strategy is proposed to solve the issue of too much infeasible space exploration. Experimental results show that our INE method can help to reduce the running time and improve the optimization results. The effectiveness of the TAEA is demonstrated by comparing it with some recent existing algorithms.

关键词： Encoding Sun Recommender systems Maintenance engineering Convergence Standards Smoothing methods Mathematical models Assembly Vectors Convergence archive diversity archive evolutionary computation exercise recommendation multi-objective problem personalized exercise group assembly (PEGA)

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Regularity Evolution for Multiobjective Optimization

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IEEE TRANSACTIONS ON evolutionary computation 2024年第5期28卷 1470-1483页

作者： Wang, Shuai Zhou, Aimin East China Normal Univ Shanghai Inst AI Educ Sch Comp Sci & Technol Shanghai 200062 Peoples R China

Recent years have witnessed the repaid progress in developing and applying multiobjective evolutionary algorithms (MOEAs). However, as a major component of an MOEA, the offspring generator has been largely overlooked, lacking a principle to design generators. This article addresses this issue by introducing an offspring generation paradigm, called regularity evolution (RE), for MOEAs. RE assumes that a solution consists of two parts: 1) a structure vector and 2) a perturbation vector. The former represents the manifold structure learned from the population, in accordance with the regularity property of multiobjective optimization problems, while the latter represents the noise or uncertainty that can be embedded in the learned manifold structure. With the RE paradigm, we can explain and improve some existing generation operators, e.g., the regularity model-based generators, and furthermore, design new generators by proposing alternative ways to construct structure or perturbation vectors. The systematic studies with comparisons on popular generation operators and newly developed MOEAs indicate that the RE paradigm has significant superiority in offspring generation for multiobjective optimization.

关键词： Manifolds Perturbation methods Statistics Sociology Optimization Generators evolutionary computation evolutionary algorithm generation paradigm multiobjective optimization regularity evolution (RE) regularity property

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A Framework Based on Historical Evolution Learning for Dynamic Multiobjective Optimization

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IEEE TRANSACTIONS ON evolutionary computation 2024年第4期28卷 1127-1140页

作者： Yu, Kunjie Zhang, Dezheng Liang, Jing Qu, Boyang Liu, Mengnan Chen, Ke Yue, Caitong Wang, Ling Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China Henan Inst Technol Sch Elect Engn & Automat Xinxiang 453003 Peoples R China Zhongyuan Univ Technol Sch Elect & Informat Zhengzhou 450007 Peoples R China Chassis Transmiss Technol Inst State Key Lab Intelligent Agr Power Equipment Luoyang 471000 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Dynamic multiobjective optimization problems (DMOPs) are widely encountered in real-world applications and have received considerable attention in recent years. During the process of solving DMOPs, tracking the constantly changing Pareto optimal set (POS) quickly is the main task, and numerous methods have been proposed to achieve this task from different perspectives. However, how to improve the evolution of static optimizers via using historical information from past environments gets little attention. In fact, with adequate historical evolution information to learn, the trend of population evolution in the historical environments is promising to guide the future evolution and thus enhance the search ability. Therefore, in this article, a historical evolution learning-based framework is proposed to assist the static optimizers in fully using the historical evolution direction and POSs distribution. Specifically, two new models are designed with a purpose of generating offsprings and environmental selection, respectively. For enhancing the contribution of offsprings, a direction guidance model is developed to guide the offsprings according to the direction tendency of historical evolution. Furthermore, to improve the robustness of static optimizers and avoid the disturbance caused by misleading solutions, a manifold revise model is proposed to produce promising solutions via consulting historical POSs distribution. The proposed framework employs these two models collaboratively, and it is flexible and readily to be embedded into various dynamic response mechanisms and static optimizers in coping with DMOPs. The superiority of the framework has been comprehensively demonstrated by experimental comparison results on a variety of benchmark test problems.

关键词： Statistics Sociology Optimization Manifolds Task analysis Robustness evolutionary computation Dynamic multiobjective optimization evolutionary algorithm framework historical learning offspring guidance

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evolutionary Variational YOLOv8 Network for Fault Detection in Wind Turbines

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Computers, Materials & Continua 2024年第7期80卷 625-642页

作者： Hongjiang Wang Qingze Shen Qin Dai Yingcai Gao Jing Gao Tian Zhang College of Information Shenyang Institute of EngineeringShenyang110136China Graduate School Shenyang Institute of EngineeringShenyang110136China College of Software Northeastern UniversityShenyang110169China

Deep learning has emerged in many practical applications,such as image classification,fault diagnosis,and object *** recently,convolutional neural networks(CNNs),representative models of deep learning,have been used to solve fault ***,the current design of CNNs for fault detection of wind turbine blades is highly dependent on domain knowledge and requires a large amount of trial and *** this reason,an evolutionary YOLOv8 network has been developed to automatically find the network architecture for wind turbine blade-based fault ***8 is a CNN-backed object detection ***,to reduce the parameter count,we first design an improved FasterNet module based on the Partial Convolution(PConv)***,to enhance convergence performance,we improve the loss function based on the efficient complete intersection over the *** on this,a flexible variable-length encoding is proposed,and the corresponding reproduction operators are *** experimental results confirmthat the proposed approach can achieve better fault detection results and improve by 2.6%in mean precision at 50(mAP50)compared to the existing ***,compared to training with the YOLOv8n model,the YOLOBFE model reduces the training parameters by 933,937 and decreases the GFLOPS(Giga Floating Point Operations Per Second)by 1.1.

关键词： Neural architecture search YOLOv8 evolutionary computation fault detection

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Micro Multiobjective evolutionary Algorithm With Piecewise Strategy for Embedded-Processor-Based Industrial Optimization

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IEEE TRANSACTIONS ON CYBERNETICS 2024年第8期54卷 4763-4774页

作者： Peng, Hu Kong, Fanrong Zhang, Qingfu Jiujiang Univ Sch Comp & BigData Sci Jiujiang 332005 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

In some industrial applications, it is required to do off-line multiobjective optimization in embedded systems. Due to their limited computing and memory capability, embedded processor may not be able to run conventional multiobjective optimization evolutionary algorithms (MOEAs). This article proposes a micro MOEA with piecewise strategy $(\mu$ MOEA) for industrial optimization in embedded processor. $\mu$ MOEA introduces an improved piecewise strategy based on the MOEA/D framework, which serially optimizes subclusters to be compatible with embedded processor under limited computing power. For the purpose of further enhancing $\mu$ MOEA, a dynamic and flexible weight vector update trigger mechanism is proposed, so that the algorithm can save and utilize the computing resources of the embedded processor as much as possible. Abundant artificial test problems are carrying out to test the performance of $\mu$ MOEA. Through various experiments, it can be found that $\mu$ MOEA has outstanding performance in ZDT, DTLZ, SMOP, and MaF problems. Last and most importantly, $\mu$ MOEA is successfully applied to two specific application scenarios of industrial optimization on embedded processor for simulation, such as two different types of semi-autogenous grinding optimization problems and micro-grid energy optimization problem, which prove the feasibility of applying MOEA to embedded processor.

关键词： Optimization Statistics Sociology evolutionary computation Search problems Memory management Artificial intelligence Decomposition framework embedded-processor-based industrial optimization micro multiobjective evolutionary algorithm (MOEA) piecewise strategy

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Subset Selection for evolutionary Multiobjective Optimization

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IEEE TRANSACTIONS ON evolutionary computation 2024年第2期28卷 403-417页

作者： Gu, Yu-Ran Bian, Chao Li, Miqing Qian, Chao Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China Univ Birmingham Sch Comp Sci Birmingham B15 2TT England

Subset selection, which selects a subset of solutions according to certain criterion/indicator, is a topic closely related to evolutionary multiobjective optimization (EMO). The critical component of a multiobjective evolutionary algorithm (MOEA), environmental selection, is essentially a subset selection problem, i.e., selecting $N$ solutions as the next-generation population from usually $2N$ candidates (where $N$ denotes the size of the population). Another use of subset selection is the solution preprocessing procedure for decision-making, in which typically a few representatives are selected from the final population or a large-size archive, in order not to overwhelm the decision maker. Existing work for subset selection in EMO is focused on developing greedy algorithms, but may suffer from being trapped in local optima. In this article, we approach the problem by providing a multiobjective evolutionary algorithmic framework. We consider several popular quality indicators for subset evaluation and present accelerated variants of a well-studied MOEA in the theoretical study area, global simple evolutionary multiobjective optimizer (GSEMO), for each indicator. We conduct rigorous theoretical analyses of the acceleration procedure. Moreover, we prove that our algorithms can achieve the best-so-far approximation guarantee by the submodularity of the indicators. We finally empirically show the effectiveness and scalability of the proposed algorithms, in addition to the potentials to be further improved by introducing popular MOEAs (e.g., using NSGA-II and MOEA/D to replace GSEMO).

关键词： evolutionary computation evolutionary multiobjective optimization (EMO) global simple evolutionary multiobjective algorithm (GSEMO) multiobjective evolutionary optimizer (MOEAs) quality indicators submodularity subset selection

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Multiselection-Based Differential Evolution

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2024年第12期54卷 7318-7330页

作者： Cai, Zonghui Gao, Shangce Yang, Xiao Zhou, MengChu Univ Toyama Fac Engn Toyama 9308555 Japan Macau Univ Sci & Technol Macao Inst Syst Engn Macau Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

Differential evolution (DE) has been developed as a state-of-the-art population-based stochastic optimizer for continuous nonconvex search space in recent decades. It uses difference vectors among individuals to adaptively control the balance between exploration and exploitation for achieving an excellent search performance. Specifically, one-to-one selection in DE guarantees that difference vectors vary from large for exploration to small for exploitation. However, the selection may generate inappropriate scale of difference vectors, which causes insufficient exploitation when facing complex problems with, e.g., multimodal with a huge number of local optima or plain regions. In this article, we propose a multiselection-based DE (MSDE) to address such limitation. Specifically, three different types of selection strategies are adopted to deal with the different situation during a search process. The proposed MSDE is evaluated on the 2013, 2014, and 2017 IEEE congress on evolutionary computation real parameter optimization competitions. Experimental results demonstrate that the proposed MSDE significantly outperforms the winners of those competitions. We further show that, when integrating the proposed multiselection-based strategy with the original DE or other advanced DE variants, it can also improve their search performance on most of test cases. This work makes a significant contribution to advance the state of the art in the area of population-based stochastic optimizers.

关键词： Vectors Optimization Next generation networking Convergence evolutionary computation Search problems Indexes Continuous optimization differential evolution (DE) selection

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Fragment-Based Knowledge Transfer for Multi-Task Capacitated Vehicle Routing

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Liu, Xiao-Fang Dai, Yang-Tao Fang, Yongchun Yu, Xue Zhan, Zhi-Hui Zhang, Jun Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China Nankai Univ Inst Intelligence Technol & Robot Syst Shenzhen Res Inst Shenzhen 518083 Peoples R China JD Com Beijing 100176 Peoples R China Nankai Univ Tianjin 300350 Peoples R China Hanyang Univ ERICA Ansan 15588 South Korea Zhejiang Normal Univ Jinhua 321017 Peoples R China Chaoyang Univ Technol Taichung 413310 Taiwan

In a capacitated vehicle routing problem (CVRP), multiple vehicles are planned to travel for serving customers so as to reduce transportation costs in logistics. Taking each CVRP as a task, multiple CVRPs can form a multi-task optimization problem. Based on the similarity between tasks, knowledge transfer methods are developed to improve optimization performance by utilizing the search experience of related tasks. However, existing methods tend to use one task only for knowledge transfer. Indeed, in a target task, the different parts of customer distributions are similar to that of multiple related tasks. The information of multiple source tasks can be fused to assist the optimization of target tasks. Thus, this paper proposes a genetic algorithm with fragment-based knowledge transfer (FKT-GA), which fuses route fragments from multiple related tasks to assist the optimization of target tasks. In FKT-GA, multiple source tasks are selected for each target task based on distribution features that are invariant to rotation, shift, and scaling. Solutions of source tasks are aligned to target spaces for sampling route fragments, which are integrated to construct high-quality solutions for target tasks. In addition, mutation and crossover operators are developed to enhance solution diversity. Experimental results on fifteen 6-task instances show that FKT-GA outperforms state-of-the-art algorithms in terms of solution optimality. The proposed FKT can improve algorithm performance.

关键词： Optimization Knowledge transfer Multitasking Genetic algorithms Vehicle routing Routing Kernel Fuses Costs Logistics Capacitated vehicle routing problem knowledge fragment knowledge transfer evolutionary computation multi-task optimization

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