State of seawater intrusion and its adaptive management countermeasures in Longkou City of China

Longkou City is a coastal area,and lacks water resources.The overexploitation of groundwater causes seawater intrusion.At present,seawater intrudes an area of 68 km2.With the decrease of groundwater extraction,the seawater intrusion area has generally declined.The paper expounds the development process of seawater intrusion as well as the corresponding prevention and control measures of using groundwater replenishment and groundwater throttling in Longkou City.In view of the seawater intrusion problem in Longkou City,some adaptive management countermeasures are put forward,which include:Adjusting industrial and agricultural structure,promoting economic and social development to match water resources;improving water usage structure,optimizing the utilization of water resources;advancing the construction of a water-saving society,using water resources efficiently;implementing inter-basin water transfer,using water resources rationally;developing and utilizing unconventional water sources,making full use of water resources;strengthening water infrastructure construction,increasing the development and utilization potential of water resources;carrying out ecological restoration,protecting water resources and ecological environment;improving the management informationalization level,strengthening the capabilities of groundwater monitoring and management;increasing publicity,improving public awareness of participation.

Saline Intrusion Response to Sea Level Rise and Its Implications on Water and Coastal Management: A Case Study in Brazil

Global temperature is predicted to increase in the end of the century and one of the primary consequences of this warming is the sea level rise. Considering the vulnerabilities on coastal systems and water resources, it is important to evaluate the potential effects of this rising in coastal areas, since the saline intrusion on rivers would be intensified, leading to problems related to water quality. In this context, the present work aimed to verify saline intrusion changes along an important river, S&#227o Francisco Canal, located in Rio de Janeiro State, Brazil. For this purpose, a hydrodynamic modeling was performed using SisBaHiA, considering different sea levels and tide conditions. According to the results, it was verified the intensification on saline intrusion and higher salinity values due to a sea level rise of 0.5 m. These results show that new licenses for water withdrawals must be carefully analyzed as the fluvial flow plays an important role to contain the saltwater intrusion on the studied river. Accordingly, it is recommended the evaluation of climate change effects in order to choose best strategies to reduce coastal vulnerability, and the use of this theme on environmental licensing and territorial planning, integrating water planning with coastal management.

A New Database Intrusion Detection Approach Based on Hybrid Meta-Heuristics

A new secured database management system architecture using intrusion detection systems(IDS)is proposed in this paper for organizations with no previous role mapping for users.A simple representation of Structured Query Language queries is proposed to easily permit the use of the worked clustering algorithm.A new clustering algorithm that uses a tube search with adaptive memory is applied to database log files to create users’profiles.Then,queries issued for each user are checked against the related user profile using a classifier to determine whether or not each query is malicious.The IDS will stop query execution or report the threat to the responsible person if the query is malicious.A simple classifier based on the Euclidean distance is used and the issued query is transformed to the proposed simple representation using a classifier,where the Euclidean distance between the centers and the profile’s issued query is calculated.A synthetic data set is used for our experimental evaluations.Normal user access behavior in relation to the database is modelled using the data set.The false negative(FN)and false positive(FP)rates are used to compare our proposed algorithm with other methods.The experimental results indicate that our proposed method results in very small FN and FP rates.

CNN Channel Attention Intrusion Detection SystemUsing NSL-KDD Dataset

Intrusion detection systems(IDS)are essential in the field of cybersecurity because they protect networks from a wide range of online threats.The goal of this research is to meet the urgent need for small-footprint,highly-adaptable Network Intrusion Detection Systems(NIDS)that can identify anomalies.The NSL-KDD dataset is used in the study;it is a sizable collection comprising 43 variables with the label’s“attack”and“level.”It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks(CNN).Furthermore,this dataset makes it easier to conduct a thorough assessment of the suggested intrusion detection strategy.Furthermore,maintaining operating efficiency while improving detection accuracy is the primary goal of this work.Moreover,typical NIDS examines both risky and typical behavior using a variety of techniques.On the NSL-KDD dataset,our CNN-based approach achieves an astounding 99.728%accuracy rate when paired with channel attention.Compared to previous approaches such as ensemble learning,CNN,RBM(Boltzmann machine),ANN,hybrid auto-encoders with CNN,MCNN,and ANN,and adaptive algorithms,our solution significantly improves intrusion detection performance.Moreover,the results highlight the effectiveness of our suggested method in improving intrusion detection precision,signifying a noteworthy advancement in this field.Subsequent efforts will focus on strengthening and expanding our approach in order to counteract growing cyberthreats and adjust to changing network circumstances.

AN INTRUSION DETECTION SYSTEM BASED ON EVIDENCE THEORY AND ROUGH SET THEORY

In this paper,we propose a novel Intrusion Detection System (IDS) architecture utilizing both the evidence theory and Rough Set Theory (RST). Evidence theory is an effective tool in dealing with uncertainty question. It relies on the expert knowledge to provide evidences,needing the evidences to be independent,and this make it difficult in application. To solve this problem,a hybrid system of rough sets and evidence theory is proposed. Firstly,simplification are made based on Variable Precision Rough Set (VPRS) conditional entropy. Thus,the Basic Belief Assignment (BBA) for all evidences can be calculated. Secondly,Dempster’s rule of combination is used,and a decision-making is given. In the proposed approach,the difficulties in acquiring the BBAs are solved,the correlativity among the evidences is reduced and the subjectivity of evidences is weakened. An illustrative example in an intrusion detection shows that the two theories combination is feasible and effective.

Effective data transmission through energy-efficient clustering and Fuzzy-Based IDS routing approach in WSNs

Wireless sensor networks(WSN)gather information and sense information samples in a certain region and communicate these readings to a base station(BS).Energy efficiency is considered a major design issue in the WSNs,and can be addressed using clustering and routing techniques.Information is sent from the source to the BS via routing procedures.However,these routing protocols must ensure that packets are delivered securely,guaranteeing that neither adversaries nor unauthentic individuals have access to the sent information.Secure data transfer is intended to protect the data from illegal access,damage,or disruption.Thus,in the proposed model,secure data transmission is developed in an energy-effective manner.A low-energy adaptive clustering hierarchy(LEACH)is developed to efficiently transfer the data.For the intrusion detection systems(IDS),Fuzzy logic and artificial neural networks(ANNs)are proposed.Initially,the nodes were randomly placed in the network and initialized to gather information.To ensure fair energy dissipation between the nodes,LEACH randomly chooses cluster heads(CHs)and allocates this role to the various nodes based on a round-robin management mechanism.The intrusion-detection procedure was then utilized to determine whether intruders were present in the network.Within the WSN,a Fuzzy interference rule was utilized to distinguish the malicious nodes from legal nodes.Subsequently,an ANN was employed to distinguish the harmful nodes from suspicious nodes.The effectiveness of the proposed approach was validated using metrics that attained 97%accuracy,97%specificity,and 97%sensitivity of 95%.Thus,it was proved that the LEACH and Fuzzy-based IDS approaches are the best choices for securing data transmission in an energy-efficient manner.

An Assessment of Saltwater Intrusion in Coastal Regions of Lagos, Nigeria

This paper explains various factors that contribute to saltwater intrusion, including overexploitation of freshwater resources and climate change as well as the different techniques essential for effective saltwater intrusion management. The impact of saltwater intrusion along coastal regions and its impact on the environment, hydrogeology and groundwater contamination. It suggests potential solutions to mitigate the impact of saltwater intrusion, including effective water management and techniques for managing SWI. The application of A.I (assessment index) serves as a guideline to correctly identify wells with SWI ranging from no intrusion, slight intrusion and strong intrusion. The challenges of saltwater intrusion in Lagos and the salinization of wells were investigated using the hydro-chemical parameters. The study identifies four wells (“AA”, “CMS”, “OBA” and “VIL”) as having high electric conductivities, indicating saline water intrusion, while other wells (“EBM”, “IKJ, and “IKO”) with lower electric conductivities, indicate little or no salt-water intrusion, and “AJ” well shows slight intrusion. The elevation of the wells also played a vital role in the SWI across coastal regions of Lagos. The study recommends continuous monitoring of coastal wells to help sustain and reduce saline intrusion. The findings of the study are important for policymakers, researchers, and practitioners who are interested in addressing the challenges of saltwater intrusion along coastal regions. We assessed the SWI across the eight (8) wells using the Assessment Index to identify wells with SWI. Wells in “CMS” and “VIL” has strong intrusions. A proposed classification system based on specific ion ratios categorizes water quality from good (+) to highly (-) contaminated (refer to Table 4). These findings underscore the need for attention and effective management strategies to address groundwater unsuitability for various purposes.

Hybrid Optimization of Support Vector Machine for Intrusion Detection

Support vector machine (SVM) technique has recently become a research focus in intrusion detection field for its better generalization performance when given less priori knowledge than other soft-computing techniques. But the randomicity of parameter selection in its implement often prevents it achieving expected performance. By utilizing genetic algorithm (GA) to optimize the parameters in data preprocessing and the training model of SVM simultaneously, a hybrid optimization algorithm is proposed in the paper to address this problem. The experimental results demonstrate that it’s an effective method and can improve the performance of SVM-based intrusion detection system further.

A Hybrid DNN-RBFNN Model for Intrusion Detection System

Intrusion Detection Systems (IDS) are pivotal in safeguarding computer networks from malicious activities. This study presents a novel approach by proposing a Hybrid Dense Neural Network-Radial Basis Function Neural Network (DNN-RBFNN) architecture to enhance the accuracy and efficiency of IDS. The hybrid model synergizes the strengths of both dense learning and radial basis function networks, aiming to address the limitations of traditional IDS techniques in classifying packets that could result in Remote-to-local (R2L), Denial of Service (Dos), and User-to-root (U2R) intrusions.

IDS入侵检测系统研究

在分布式计算环境中，信息系统首先需要考虑的就是保护数据和资源免遭未授权的非法访问、操作，甚至恶意入侵和破坏，因此安全管理日益成为人们关注的焦点。在诸多的新兴技术中，IDS（入侵检测系统）以它新颖的思路和广阔的应用前景而倍受青睐。介绍IDS的历史和现状，说明现有IDS的不足以及今后ID技术的发展趋势。

Optimal configuration of firewall, IDS and vulnerability scan by game theory

The integrated linkage control problem based on attack detection is solved with the analyses of the security model including firewall, intrusion detection system （IDS） and vulnerability scan by game theory. The Nash equilibrium for two portfolios of only deploying IDS and vulnerability scan and deploying all the technologies is investigated by backward induction. The results show that when the detection rates of IDS and vulnerability scan are low, the firm will not only inspect every user who raises an alarm, but also a fraction of users that do not raise an alarm; when the detection rates of IDS and vulnerability scan are sufficiently high, the firm will not inspect any user who does not raise an alarm, but only inspect a fraction of users that raise an alarm. Adding firewall into the information system impacts on the benefits of firms and hackers, but does not change the optimal strategies of hackers, and the optimal investigation strategies of IDS are only changed in certain cases. Moreover, the interactions between IDS ＆ vulnerability scan and firewall ＆ IDS are discussed in detail.

基于移动agent的分布式入侵检测系统MAIDS的设计与实现

阐述了基于移动agent技术的分布式入侵检测系统MAIDS的设计与实现。该系统利用了当前正在广泛研究的移动agent技术,主要由控制服务器和受检测主机两部分组成,它可同时对主机和网络进行检测,并具有一定的智能性和灵活性,克服了传统IDS的一些缺陷。

网络入侵检测系统RIDS的研究

入侵检测是网络安全的一个新方向 ,入侵检测的重点是如何有效地提取特征数据并准确地分析出非正常网络行为。本文在深入研究分析公共入侵检测框架 (CIDF)理论和现有入侵检测系统 (IDS)实现策略的基础上 ,提出了一种基于资源监视的入侵检测概念 ,给出了以资源监视为中心的主机监视和网络监视相结合的网络入侵检测系统RIDS的模型 。

入侵检测系统IDS的安全性研究

通过对IDS系统的安全性和脆弱性的分析,提出了IDS系统安全度、入侵度以及脆弱度等安全评价指标,并在总结目前存在的攻击和躲避IDS系统的工具及技术的基础上,设计了一个新型的IDS安全体系结构实用配置模型。

几种机器学习方法在IDS中的性能比较

入侵检测是一种保障网络安全的新技术,传统的入侵检测方法存在误报漏报及实时性差等缺点,将机器学习的技术引入到入侵监测系统之中以有效地提高系统性能具有十分重要的现实意义。将目前主要的基于机器学习的贝叶斯分类的方法、神经网络的方法、决策树方法与支持向量机的方法应用于入侵检测系统中,以kdd99公共数据集进行了仿真实验,仿真测试结果表明支持向量机方法(SVM)和神经网络方法具有较好的分类识别性能,适合用于入侵检测。

一种IDS报警可信性增强方案

提高IDS(入侵检测系统)报警的可信性是IDS的根本目标。从理论上分析了可信问题产生的原因,给出了其形式化描述,提出了一种多IDS协同工作提高检测可信度的方法,并证明了该方法可以应用于各种不同IDS的协同工作中(基于误用、异常及异常与误用相结合的IDS)。多检测系统结果融合时采用推进Bayesian分类方法,给出了其模型和具体算法。实验分析表明,该方法与其他同类算法相比,降低了系统的漏报率和误报率,增强了报警的可信度。

分布式网络攻击检测系统(DIDS)

介绍了网络攻击检测系统 (IDS)的运作机理 ,分析了IDS的优缺点。针对传统IDS的问题提出了分布式IDS(DIDS)的概念 ,比较了DIDS的设计目标与目前一些IDS产品的性能。最后从功能模块设计、攻击特征的获取和更新、提高攻击行为的检测和反应速度、攻击行为关联性分析和更加主动的反应策略五个方面详细阐述了DIDS的具体设计思路 。

MAIDS系统中的检测功能模块

阐述基于移动agent技术的入侵检测系统中检测功能模块的设计与实现。系统中检测功能主要是由两种移动agent来完成的，即主机检测agent和网络检测agent，它们共同工作，一起完成对网络及其中主机的入侵检测，使系统具有较好的性能和扩展性。

分类异常点检测算法及在IDS模型中的应用

在分析了各种异常点检测算法的基础上,提出了一种分类异常点检测算法,该方法能够对数据在各个方面表现出的异常情况进行全面检测,精确度高、时间消耗少。提出了一个入侵检测系统模型,包括异常检测层和误用检测层,在异常检测中应用了分类异常点检测方法,该模型可以明显减少系统的漏报率。

基于数据挖掘的IDS日志数据分析处理

日志数据分析处理是安全模型中的重要组成部分,对该问题的研究具有较高的应用价值。将数据挖掘的方法应用于IDS日志分析系统,并构造出了入侵模式提取与检测分类器建立的处理流程,从而解决了日志分析自动化的问题。