A High-level Architecture for Intrusion Detection on Heterogeneous Wireless Sensor Networks: Hierarchical, Scalable and Dynamic Reconfigurable

Networks protection against different types of attacks is one of most important posed issue into the network and information security domains. This problem on Wireless Sensor Networks (WSNs), in attention to their special properties, has more importance. Now, there are some of proposed solutions to protect Wireless Sensor Networks (WSNs) against different types of intrusions;but no one of them has a comprehensive view to this problem and they are usually designed in single-purpose;but, the proposed design in this paper has been a comprehensive view to this issue by presenting a complete Intrusion Detection Architecture (IDA). The main contribution of this architecture is its hierarchical structure;i.e. it is designed and applicable, in one, two or three levels, consistent to the application domain and its required security level. Focus of this paper is on the clustering WSNs, designing and deploying Sensor-based Intrusion Detection System (SIDS) on sensor nodes, Cluster-based Intrusion Detection System (CIDS) on cluster-heads and Wireless Sensor Network wide level Intrusion Detection System (WSNIDS) on the central server. Suppositions of the WSN and Intrusion Detection Architecture (IDA) are: static and heterogeneous network, hierarchical, distributed and clustering structure along with clusters' overlapping. Finally, this paper has been designed a questionnaire to verify the proposed idea;then it analyzed and evaluated the acquired results from the questionnaires.

Cyber-Security of Smart Grids: Attacks, Detection, Countermeasure Techniques, and Future Directions

One of the significant challenges that smart grid networks face is cyber-security. Several studies have been conducted to highlight those security challenges. However, the majority of these surveys classify attacks based on the security requirements, confidentiality, integrity, and availability, without taking into consideration the accountability requirement. In this survey paper, we provide a classification of attacks based on the OSI model and discuss in more detail the cyber-attacks that can target the different layers of smart grid networks communication. We also propose new classifications for the detection and countermeasure techniques and describe existing techniques under each category. Finally, we discuss challenges and future research directions.

A Hybrid Approach for Network Intrusion Detection

Due to the widespread use of the internet and smart devices,various attacks like intrusion,zero-day,Malware,and security breaches are a constant threat to any organization’s network infrastructure.Thus,a Network Intrusion Detection System(NIDS)is required to detect attacks in network traffic.This paper proposes a new hybrid method for intrusion detection and attack categorization.The proposed approach comprises three steps to address high false and low false-negative rates for intrusion detection and attack categorization.In the first step,the dataset is preprocessed through the data transformation technique and min-max method.Secondly,the random forest recursive feature elimination method is applied to identify optimal features that positively impact the model’s performance.Next,we use various Support Vector Machine(SVM)types to detect intrusion and the Adaptive Neuro-Fuzzy System(ANFIS)to categorize probe,U2R,R2U,and DDOS attacks.The validation of the proposed method is calculated through Fine Gaussian SVM(FGSVM),which is 99.3%for the binary class.Mean Square Error(MSE)is reported as 0.084964 for training data,0.0855203 for testing,and 0.084964 to validate multiclass categorization.

Designing an Agent-Based Intrusion Detection System for Heterogeneous Wireless Sensor Networks: Robust, Fault Tolerant and Dynamic Reconfigurable

Protecting networks against different types of attacks is one of most important posed issue into the network and information security domains. This problem on Wireless Sensor Networks (WSNs), in attention to their special properties, has more importance. Now, there are some of proposed solutions to protect Wireless Sensor Networks (WSNs) against different types of intrusions;but no one of them has a comprehensive view to this problem and they are usually designed in single-purpose;but, the proposed design in this paper has been a comprehensive view to this issue by presenting a complete architecture of Intrusion Detection System (IDS). The main contribution of this architecture is its modularity and flexibility;i.e. it is designed and applicable, in four steps on intrusion detection process, consistent to the application domain and its required security level. Focus of this paper is on the heterogeneous WSNs and network-based IDS, by designing and deploying the Wireless Sensor Network wide level Intrusion Detection System (WSNIDS) on the base station (sink). Finally, this paper has been designed a questionnaire to verify its idea, by using the acquired results from analyzing the questionnaires.

General Study of Mobile Agent Based Intrusion Detection System (IDS)

The extensive access of network interaction has made present networks more responsive to earlier intrusions. In distributed network intrusions, there are many computing nodes that are assisted by intruders. The evidence of intrusions is to be associated from all the held up nodes. From the last few years, mobile agent based technique in intrusion detection system (IDS) has been widely used to detect intrusion over distributed network. This paper presented survey of several existing mobile agent based intrusion detection system and comparative analysis report between them. Furthermore we have focused on each attribute of analysis, for example technique (NIDS, HIDS or Hybrid), behavior layer, detection techniques for analysis, uses of mobile agent and technology used by existing IDS, strength and issues. Their strengths and issues are situational wherever appropriate. We have observed that some of the existing techniques are used in IDS which causes low detection rate, behavior layers like TCP connection for packet capturing which is most important activity in NIDS and response time (technology execution time) with memory consumption by mobile agent as major issues.

FEW-NNN: A Fuzzy Entropy Weighted Natural Nearest Neighbor Method for Flow-Based Network Traffic Attack Detection

Attacks such as APT usually hide communication data in massive legitimate network traffic, and mining structurally complex and latent relationships among flow-based network traffic to detect attacks has become the focus of many initiatives. Effectively analyzing massive network security data with high dimensions for suspicious flow diagnosis is a huge challenge. In addition, the uneven distribution of network traffic does not fully reflect the differences of class sample features, resulting in the low accuracy of attack detection. To solve these problems, a novel approach called the fuzzy entropy weighted natural nearest neighbor(FEW-NNN) method is proposed to enhance the accuracy and efficiency of flowbased network traffic attack detection. First, the FEW-NNN method uses the Fisher score and deep graph feature learning algorithm to remove unimportant features and reduce the data dimension. Then, according to the proposed natural nearest neighbor searching algorithm(NNN_Searching), the density of data points, each class center and the smallest enclosing sphere radius are determined correspondingly. Finally, a fuzzy entropy weighted KNN classification method based on affinity is proposed, which mainly includes the following three steps: 1、 the feature weights of samples are calculated based on fuzzy entropy values, 2、 the fuzzy memberships of samples are determined based on affinity among samples, and 3、 K-neighbors are selected according to the class-conditional weighted Euclidean distance, the fuzzy membership value of the testing sample is calculated based on the membership of k-neighbors, and then all testing samples are classified according to the fuzzy membership value of the samples belonging to each class;that is, the attack type is determined. The method has been applied to the problem of attack detection and validated based on the famous KDD99 and CICIDS-2017 datasets. From the experimental results shown in this paper, it is observed that the FEW-NNN method improves the accuracy and efficiency of flow-based network traffic attack detection.

A Comprehensive Survey on Advanced Persistent Threat (APT) Detection Techniques

The increase in number of people using the Internet leads to increased cyberattack opportunities.Advanced Persistent Threats,or APTs,are among the most dangerous targeted cyberattacks.APT attacks utilize various advanced tools and techniques for attacking targets with specific goals.Even countries with advanced technologies,like the US,Russia,the UK,and India,are susceptible to this targeted attack.APT is a sophisticated attack that involves multiple stages and specific strategies.Besides,TTP(Tools,Techniques,and Procedures)involved in the APT attack are commonly new and developed by an attacker to evade the security system.However,APTs are generally implemented in multiple stages.If one of the stages is detected,we may apply a defense mechanism for subsequent stages,leading to the entire APT attack failure.The detection at the early stage of APT and the prediction of the next step in the APT kill chain are ongoing challenges.This survey paper will provide knowledge about APT attacks and their essential steps.This follows the case study of known APT attacks,which will give clear information about the APT attack process—in later sections,highlighting the various detection methods defined by different researchers along with the limitations of the work.Data used in this article comes from the various annual reports published by security experts and blogs and information released by the enterprise networks targeted by the attack.

A Method for Detecting Intrusion on Networks in Real-time Based on IP Weight

A new rule to detect intrusion based on IP weight, which is also well implemented in the rule base of author’s NMS, is presented. Compared with traditional ones, intrusion detecting based on IP weight enhanced analysis to packet content. The method also provides a real-time efficient way to analyze traffic on high-speed network and can help to increase valid usage rates of network resources. Practical implementation as a rule in the rule base of our NMS has verified that the rule can detect not only attacks on network, but also other unusual behaviors.

FMSA:a meta-learning framework-based fast model stealing attack technique against intelligent network intrusion detection systems

Intrusion detection systems are increasingly using machine learning.While machine learning has shown excellent performance in identifying malicious traffic,it may increase the risk of privacy leakage.This paper focuses on imple-menting a model stealing attack on intrusion detection systems.Existing model stealing attacks are hard to imple-ment in practical network environments,as they either need private data of the victim dataset or frequent access to the victim model.In this paper,we propose a novel solution called Fast Model Stealing Attack(FMSA)to address the problem in the field of model stealing attacks.We also highlight the risks of using ML-NIDS in network security.First,meta-learning frameworks are introduced into the model stealing algorithm to clone the victim model in a black-box state.Then,the number of accesses to the target model is used as an optimization term,resulting in minimal queries to achieve model stealing.Finally,adversarial training is used to simulate the data distribution of the target model and achieve the recovery of privacy data.Through experiments on multiple public datasets,compared to existing state-of-the-art algorithms,FMSA reduces the number of accesses to the target model and improves the accuracy of the clone model on the test dataset to 88.9%and the similarity with the target model to 90.1%.We can demonstrate the successful execution of model stealing attacks on the ML-NIDS system even with protective measures in place to limit the number of anomalous queries.

A survey of edge computing-based designs for IoT security

Pervasive IoT applications enable us to perceive,analyze,control,and optimize the traditional physical systems.Recently,security breaches in many IoT applications have indicated that IoT applications may put the physical systems at risk.Severe resource constraints and insufficient security design are two major causes of many security problems in IoT applications.As an extension of the cloud,the emerging edge computing with rich resources provides us a new venue to design and deploy novel security solutions for IoT applications.Although there are some research efforts in this area,edge-based security designs for IoT applications are still in its infancy.This paper aims to present a comprehensive survey of existing IoT security solutions at the edge layer as well as to inspire more edge-based IoT security designs.We first present an edge-centric IoT architecture.Then,we extensively review the edge-based IoT security research efforts in the context of security architecture designs,firewalls,intrusion detection systems,authentication and authorization protocols,and privacy-preserving mechanisms.Finally,we propose our insight into future research directions and open research issues.

Detecting Man-in-the-Middle Attack in Fog Computing for Social Media

Fog computing(FC)is a networking paradigm where wireless devices known as fog nodes are placed at the edge of the network(close to the Internet of Things(IoT)devices).Fog nodes provide services in lieu of the cloud.Thus,improving the performance of the network and making it attractive to social media-based systems.Security issues are one of the most challenges encountered in FC.In this paper,we propose an anomalybased Intrusion Detection and Prevention System(IDPS)against Man-in-theMiddle(MITM)attack in the fog layer.The system uses special nodes known as Intrusion Detection System(IDS)nodes to detect intrusion in the network.They periodically monitor the behavior of the fog nodes in the network.Any deviation from normal network activity is categorized as malicious,and the suspected node is isolated.ExponentiallyWeighted Moving Average(EWMA)is added to the system to smooth out the noise that is typically found in social media communications.Our results(with 95%confidence)show that the accuracy of the proposed system increases from 80%to 95%after EWMA is added.Also,with EWMA,the proposed system can detect the intrusion from 0.25–0.5 s seconds faster than that without EWMA.However,it affects the latency of services provided by the fog nodes by at least 0.75–1.3 s.Finally,EWMA has not increased the energy overhead of the system,due to its lightweight.

A Case Study on Security Recommendations for a Global Organization

In today’s world, computer networks form an essential part of any organization. They are used not only to communicate information amongst the various parties involved but also to process data and store critical information which is accessible to approved subscribers. Protecting critical data, ensuring confidentiality, and thwarting illegal access are primary concerns for such organizations. This case study presents security recommendations for any such organization, to assist them in defining security policies at various levels of the network infrastructure.

A Novel IoT Architecture, Assessment of Threats and Their Classification withMachine Learning Solutions

The Internet of Things(IoT)will significantly impact our social and economic lives in the near future.Many Internet of Things(IoT)applications aim to automate multiple tasks so inactive physical objects can behave independently of others.IoT devices,however,are also vulnerable,mostly because they lack the essential built-in security to thwart attackers.It is essential to perform the necessary adjustments in the structure of the IoT systems in order to create an end-to-end secure IoT environment.As a result,the IoT designs that are now in use do not completely support all of the advancements that have been made to include sophisticated features in IoT,such as Cloud computing,machine learning techniques,and lightweight encryption techniques.This paper presents a detailed analysis of the security requirements,attack surfaces,and security solutions available for IoT networks and suggests an innovative IoT architecture.The Seven-Layer Architecture in IoT provides decent attack detection accuracy.According to the level of risk they pose,the security threats in each of these layers have been properly categorized,and the essential evaluation criteria have been developed to evaluate the various threats.Also,Machine Learning algorithms like Random Forest and Support Vector Machines,etc.,and Deep Learning algorithms like Artificial Neural Networks,Q Learning models,etc.,are implemented to overcome the most damaging threats posing security breaches to the different IoT architecture layers.

A critical review of intrusion detection systems in the internet of things:techniques,deployment strategy,validation strategy,attacks,public datasets and challenges

The Internet of Things(IoT)has been rapidly evolving towards making a greater impact on everyday life to large industrial systems.Unfortunately,this has attracted the attention of cybercriminals who made IoT a target of malicious activities,opening the door to a possible attack on the end nodes.To this end,Numerous IoT intrusion detection Systems(IDS)have been proposed in the literature to tackle attacks on the IoT ecosystem,which can be broadly classified based on detection technique,validation strategy,and deployment strategy.This survey paper presents a comprehensive review of contemporary IoT IDS and an overview of techniques,deployment Strategy,validation strategy and datasets that are commonly applied for building IDS.We also review how existing IoT IDS detect intrusive attacks and secure communications on the IoT.It also presents the classification of IoT attacks and discusses future research challenges to counter such IoT attacks to make IoT more secure.These purposes help IoT security researchers by uniting,contrasting,and compiling scattered research efforts.Consequently,we provide a unique IoT IDS taxonomy,which sheds light on IoT IDS techniques,their advantages and disadvantages,IoT attacks that exploit IoT communication systems,corresponding advanced IDS and detection capabilities to detect IoT attacks.

A Cyber Kill Chain Approach for Detecting Advanced Persistent Threats

The number of cybersecurity incidents is on the rise despite significant investment in security measures.The existing conventional security approaches have demonstrated limited success against some of the more complex cyber-attacks.This is primarily due to the sophistication of the attacks and the availability of powerful tools.Interconnected devices such as the Internet of Things(IoT)are also increasing attack exposures due to the increase in vulnerabilities.Over the last few years,we have seen a trend moving towards embracing edge technologies to harness the power of IoT devices and 5G networks.Edge technology brings processing power closer to the network and brings many advantages,including reduced latency,while it can also introduce vulnerabilities that could be exploited.Smart cities are also dependent on technologies where everything is interconnected.This interconnectivity makes them highly vulnerable to cyber-attacks,especially by the Advanced Persistent Threat(APT),as these vulnerabilities are amplified by the need to integrate new technologies with legacy systems.Cybercriminals behind APT attacks have recently been targeting the IoT ecosystems,prevalent in many of these cities.In this paper,we used a publicly available dataset on Advanced Persistent Threats(APT)and developed a data-driven approach for detecting APT stages using the Cyber Kill Chain.APTs are highly sophisticated and targeted forms of attacks that can evade intrusion detection systems,resulting in one of the greatest current challenges facing security professionals.In this experiment,we used multiple machine learning classifiers,such as Naïve Bayes,Bayes Net,KNN,Random Forest and Support Vector Machine(SVM).We used Weka performance metrics to show the numeric results.The best performance result of 91.1%was obtained with the Naïve Bayes classifier.We hope our proposed solution will help security professionals to deal with APTs in a timely and effective manner.

Machine Learning Approaches to Detect DoS and Their Effect on WSNs Lifetime

Energy and security remain the main two challenges in Wireless Sensor Networks(WSNs).Therefore,protecting these WSN networks from Denial of Service(DoS)and Distributed DoS(DDoS)is one of the WSN networks security tasks.Traditional packet deep scan systems that rely on open field inspection in transport layer security packets and the open field encryption trend are making machine learning-based systems the only viable choice for these types of attacks.This paper contributes to the evaluation of the use machine learning algorithms in WSN nodes traffic and their effect on WSN network life time.We examined the performance metrics of different machine learning classification categories such asK-Nearest Neighbour(KNN),Logistic Regression(LR),Support Vector Machine(SVM),Gboost,Decision Tree(DT),Na飗e Bayes,Long Short Term Memory(LSTM),and Multi-Layer Perceptron(MLP)on aWSN-dataset in different sizes.The test results proved that the statistical and logical classification categories performed the best on numeric statistical datasets,and the Gboost algorithm showed the best performance compared to different algorithms on average of all performance metrics.The performance metrics used in these validations were accuracy,F1-score,False Positive Ratio(FPR),False Negative Ratio(FNR),and the training execution time.Moreover,the test results showed the Gboost algorithm got 99.6%,98.8%,0.4%0.13%in accuracy,F1-score,FPR,and FNR,respectively.At training execution time,it obtained 1.41 s for the average of all training time execution datasets.In addition,this paper demonstrated that for the numeric statistical data type,the best results are in the size of the dataset ranging from3000 to 6000 records and the percentage between categories is not less than 50%for each category with the other categories.Furthermore,this paper investigated the effect of Gboost on the WSN lifetime,which resulted in a 32%reduction compared to other Gboost-free scenarios.

A graph based system for multi-stage attacks recognition

Building attack scenario is one of the most important aspects in network security.This paper pro-posed a system which collects intrusion alerts,clusters them as sub-attacks using alerts abstraction,ag-gregates the similar sub-attacks,and then correlates and generates correlation graphs.The scenarios wererepresented by alert classes instead of alerts themselves so as to reduce the required rules and have the a-bility of detecting new variations of attacks.The proposed system is capable of passing some of the missedattacks.To evaluate system effectiveness,it was tested with different datasets which contain multi-stepattacks.Compressed and easily understandable Correlation graphs which reflect attack scenarios were gen-erated.The proposed system can correlate related alerts,uncover the attack strategies,and detect newvariations of attacks.

A Compact Trust Computation and Management Approach for Defending against Derailed Attacks for Wireless Sensor Networks and Its Applications

One of the most effective measurements of intercommunication and collaboration in wireless sensor networks which leads to provide security is Trust Management. Most popular decision making systems used to collaborate with a stranger are tackled by two different existing trust management systems: one is a policy-based approach which verifies the decision built on logical properties and functionalities;the other approach is reputation-based approach which verifies the decision built on physical properties and functionalities of WSN. Proofless authorization, unavailability, vagueness and more complexity cause decreased detection rate and spoil the efficacy of the WSN in existing approaches. Some of the integrated approaches are utilized to improve the significance of the trust management strategies. In this paper, a Compact Trust Computation and Management (CTCM) approach is proposed to overcome the limitations of the existing approaches, also it provides a strong objective security with the calculability and the available security implications. Finally, the CTCM approach incorporates the optimum trust score for logical and physical investigation of the network resources. The simulation based experiment results show that the CTCM compact trust computation and management approach can provide an efficient defending mechanism against derailing attacks in WSN.

面向工艺数据分析的流程工业入侵检测及攻击定位

为解决攻击者利用流程工业生产中深度耦合的工序参数进行生产过程攻击的问题,提出一种基于SSA-LSTM的深度学习算法,对工艺数据进行异常检测。通过麻雀优化算法优化LSTM神经网络的迭代次数、学习率和隐藏层节点数三个超参数,实现对工艺数据的准确预测。将预测数据与真实数据进行对比,超出阈值的点定义为异常点,再运用Petri网理论对生产工艺参数间的耦合关系进行建模,确定异常点与入侵点之间的因果关系,为预测结果提供理论支撑。将SWAT水处理系统数据集用于验证算法效率,证明了所提出的模型在检测精度和攻击定位准确性方面优于其他算法模型。实验结果表明,所提出的算法模型可有效检测出通过暴力篡改传感器数据对工业生产造成重大影响的入侵行为。