Search

Network security has been one of the most important problems in Computer Network Management and Intrusion is the most publicized threats to security. In recent years, intrusion detection has emerged as an important field for network security. IDSs obtain better results when each class ofattacks is treated as a separate problem and handled by specialized algorithms. Now in days various model and method are available for intrusion detection. In this paper, we present a study of intrusion detection. Detection method to improve the detection rate & helping the users to develop secure information systems.

Host-based intrusion detection system (HIDS) is a necessary component for network security, especially when more and more data are encrypted which makes network-based intrusion detection system lose its functionality of packet content inspection. After many years of research, it is widely acknowledged that system calls are the preferred data source for HIDS. In a recent paper, a novel semantic analysis approach was proposed and shown to achieve the best performance, as compared with various previous syntactic analysis schemes. The performance difference is profound for modern attacks. However, the semantic analysis approach requires considerable computational complexity. In this paper, we present a deep learning architecture which requires no data pre-processing and is easy to train. Experimental results show that our design has a better performance than the semantic analysis approach.

Today, the world is progressively associated with the Internet so that the attackers and hackers are having a high opportunity to enter PCs and networks. In today’s world hackers are using different types of attacks for receiving valuable information. It is important to recognize these attacks ahead of time to secure end clients and the system effects. Intrusion detection system (IDS) has been generally conveyed in PCs and systems to recognize the variety of attacks. In this paper, the basic observation is on log monitoring in host-based intrusion detection systems. In this paper, host-based intrusion detection is achieved using OSSEC tool. By using the OSSEC, the system is capable of detecting the malicious logs which run in the background from the system.

In the present scenario, the demand for extended coverage, low latency and increased data rate is the need of the hour. Many emerging 5G technologies like massive Multiple Input Multiple Output and device to device communication are introduced in order to meet these increasing demands. For extending the coverage region, new wireless components are introduced in the network like relays, Small Cell Access Point and hotspots. But these components are highly vulnerable to security breaches and thus provides an entry point for the intruder to enter into the network. In this paper, a general 5G wireless communication network with an incorporated relay is proposed. This paper focuses on the implementation of Intrusion Detection System using Adaptive Neuro-Fuzzy Inference System using KDD cup 99 data set for detecting an attack on the relay. The effect of varying the membership function and learning algorithms have also been analyzed.

Spoofed ARP packets are used by malicious users to redirect network’s traffic to their hosts. The potential damage to a network from an attack of this nature can be very important. This paper discusses first how malicious users redirect network traffic using spoofed ARP packets. Then, the paper proposes a practical and efficient mechanism for detecting malicious hosts that are performing traffic redirection attack against other hosts in switched LAN networks. The proposed mechanism consists of sending first spoofed packets to the network’s hosts. Then, by collecting and analyzing the responses packets, it is shown how hosts performing traffic redirection attack can be identified efficiently and accurately. The affect of the proposed mechanism on the performance of the network is discussed and shown to be minimal. The limits of current IDSs regarding their ability to detect malicious traffic redirection attack, based on spoofed ARP packets, in switched LAN networks are discussed. Our work is concerned with the detection of malicious network traffic redirection attack, at the Data Link layer. Other works proposed protection mechanisms against this attack, but at the Application layer, using cryptographic techniques and protocols.

Many intrusion behaviors can be characterized as the execution of a sequence of crucial commands that results in an unauthorized access. Lots of attack sequences can be derived by either exchanging properly orders of crucial commands or replacing crucial commands with the functionally similar commands, which have the same performance. Therefore, it is very difficult to detect such attacks. In this paper, we propose a cooperative intrusion detection model based on state transition analysis, in which the topological order and isomorphic transformation are adopted. For a given sequence of crucial commands of an intrusion, all the possible derived sequences as an intrusion scenario can be generated by means of the model. We may also use the model to detect the attacks from different cooperating attackers and the attacks from one attacker in different login sessions. Furthermore, a derived intrusion can be seen as an unknown intrusion, in this sense that the technique presented in this paper can detect some unknown intrusions.

Optimization algorithms are search methods to find an optimal solution to a problem with a set of constraints. Bio-Inspired Algorithms (BIAs) are based on biological behavior to solve a real world problem. BIA with optimization technique is to improve the overall performance of BIA. The aim of this paper is to introduce a novel optimization algorithm which is inspired by natural stinging behavior of honey bee to find the optimal solution. This algorithm performs both monitor and sting if any occurrence of predators. By applying a novel optimization algorithm based on stinging behavior of bee, used to solve the intrusion detection problems. In this paper, a new host intrusion detection system based on novel optimization algorithm has been proposed and implemented. The performance of the proposed Anomaly-based Host Intrusion Detection System (A-HIDS) using a novel optimization algorithm based on stinging behavior of bee has been tested. In this paper, after an explanation of the natural stinging behavior of honey bee, a novel optimization algorithm and A-HIDS are described and implemented. The results show that the novel optimization algorithm offers some advantage according to the nature of the problem.

In the era of blossoming computer sciences and internet technology, people cannot abolish network in our lives. However, the large number of users, website services will make itself became the most favorite targets for hackers. Although these malicious behaviors can be detected by network intrusion detection system, it is difficult to generate accuracy result owing to the shortage of data. This paper proposed a solution using host intrusion detection system that focus on the host log detection of webserver. Besides using port monitoring to monitor network environment, this paper also collected signatures of web attack and malicious activities by using signature-based approach. Furthermore, this research will find out the source of the malicious files with file monitoring function, and take appropriate action to protect web services. By using the proposed mechanism of host-based intrusion detection methods, it can provide a high accuracy to bring safety for managers and users.

Intrusions and intrusive behaviour can be aimed at different parts of the system, ranging from lower-level network attacks intended to disrupt the flow of data in general, to higher-level attacks targeted against specific applications or services. Due to the constant growth of network traffic and the need to inspect the traffic thoroughly, intrusion detection and prevention are becoming increasingly complex and require significant computational resources. This paper presents a distributed, scalable solution for the detection of lower-level Denial-of-Service (DoS) attacks which are executed by transmitting overwhelming amounts of data with the intention of disrupting regular network service. Scalability is achieved by active traffic balancing among multiple traffic processors, exploiting the flexibility and network programmability that Software Defined Networking paradigm brings and packet processing based on device polling. Traffic processors can be elastically added into the pool depending on the traffic volume. The whole system is completely transparent to the external observers. The paper shows that the implemented balancing algorithm further improves the reliability of the intrusion detection.

Cloud computing delivers on-demand resources over the Internet on a pay-for-use basis, intruders may exploit clouds for their advantage. This paper presents Autonomous Cloud Intrusion Response System (ACIRS), a proper defense strategy for cloud systems. ACIRS continuously monitors and analyzes system events and computes security and risk parameters to provide risk assessment and mitigation capabilities with a scalable and elastic architecture with no central coordinator. It detects masquerade, host based and network based attacks and selects the appropriate response to mitigate these attacks. ACIRS is superior to NICE (Network Intrusion Detection and Countermeasure Selection system) in reducing the risk by 38 %. This paper describes the components, architecture, and advantages of ACIRS. Scopus

means illegal entry or unwelcome addition of the system. So, Intrusion detection system is used to find out the signatures of an intrusion. The goal of the system is to protect system for various network attacks like Dos, U2R, R2L, Probing etc. Intrusion detection system (IDS) collects information from various parts of network and system. This paper introduces the Anomaly Intrusion Detection System that can detect various network attacks.The aim of this work is to identify those attacks with the support of supervised neural network, i.e. back propagation artificial neural network algorithm and make complete data safe. In this paper, system comprises experimenting neural networks that use only the (17 of 41) most significant features of the KDD 99 dataset. The proposed IDS use a supervised neural network to study system's performance.

This paper aimed at the actual situation of the difficult of getting a lot of the training sample of the security computer network system in the distributed intrusion detection. In this paper, we studied how to increase the intrusion detection accuracy in the case of small samples, so that processing, maintenance and deal with the invasion of the network timely. In this paper, we proposed a new intrusion detection method based on improved SVM Co training. The specific implementation process of the algorithm is presented. Through the simulation experiments based on the actual data showed that the method is effective. Apply this method to a classified computer network system, effectively realized the detection to outside intruders and internal intruder.

Security and reliability are the major concern of our daily life usage of any network. But with the swift advancements in network technology, attacks are becoming more sophisticated than defenses. Although firewalls and router-based packet filtering are essential elements of an overall network security topology, they are not enough on their own. So, to brace the network from unauthorized access the idea of Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) is attracting security experts. This paper briefs various trends in Intrusion Detection & Prevention. To understand various techniques in IDS, this paper analyses various approaches proposed by security researchers specifically using popular open source software Snort as their IDS tool. Being an open source IDS, Snort can be easily configured and deployed in any environment. To assess the efficiency, these research papers are analyzed in various performance aspects like Detection Accuracy, Scalability and Capability of detecting unknown attacks. To overcome various challenges like low detection rate, incapable of handling huge traffic, unsupported automated tuning, etc. that are identified during literature review, this paper proposes a level based architecture. All the levels are designed as incremental i.e. capable of providing the desired functionality and also its lower levels. To prove the efficiency of the proposed architecture, it can be integrated into Snort Tool using Code Refactoring. Also proposed an environment setup to evaluate the modified Snort Tool performance in future.

The paper takes Web service composition document as the research object, through the analysis of the documents, the port and address on the Web server, to create a Web intrusion detection model. The core of the model will monitor the Web server host resources, and finally discusses in detail the design and implementation of resource monitoring system. Intrusion detection model proposed can effectively regulate the behavior of users in this paper, allowing users follow a pre-standard service to call service providers, largely to protect the security of Web services.

Ever increasing demand of good quality communication relies heavily on Network Intrusion Detection System (NIDS). Intrusion detection for network security demands high performance. This paper gives a description of the available approaches for a network intrusion detection system in both software and hardware implementation. This paper gives a description of the structure of Snort rule set which is a very popular software signature and anomaly based Intrusion Detection and prevention system. This paper also discusses the merit of FPGA devices to be used in network intrusion detection system implementation and the approaches used in hardware implementation of NIDS.

Due to extensive growth of the Internet and increasing availability of tools and methods for intruding and attacking networks, intrusion detection has become a critical component of network security parameters. TCP/IP protocol suite is the defacto standard for communication on the Internet. The underlying vulnerabilities in the protocols is the root cause of intrusions. Therefor Intrusion detection system becomes an important element in network security that controls real time data and leads to huge dimensional problem. Processing large number of packets and data in real time is very difficult and costly. Therefor data pre- processing is necessary to remove redundant and unwanted information from packets and clean network data. Here, we are focusing on two important aspects of intrusion detection; one is accuracy and other is performance. The layered approach of TCP/IP model can be applied to packet pre-processing to achieve early and faster intrusion detection. Motivation for the paper comes from the large impact data preprocessing has on the accuracy and capability of anomaly-based NIPS. In this paper it is demonstrated that high attack detection accuracy can be achieved by using layered approach for data preprocessing in Internet. To reduce false positive rate and to increase efficiency of detection, the paper proposed framework for preprocessing in intrusion prevention system. We experimented with real time network traffic as well as he KDDcup99 dataset for our research.

Objectives: To prevent malicious software and hacking in near field communication-based mobile payments, this paper suggests a new intrusion tolerant system. Methods/Statistical analysis: As Cuckoo Sandbox restricts the access to the system resources of the applet transmitted through the network, this new intrusion tolerant system adopted Cuckoo Sandbox for the intrusion detection and the safe normal service of the operation system from various malicious attacks. To enhance the intrusion tolerant level, the scalable intrusion-tolerant architecture for distributed service is adopted to analyze the status of the system and the risk in the current environment through the intrusion detection, which optimizes the security level of a system. Findings: The intrusion tolerant system suggested in this paper may cover various malicious applications such as network attacks to the weak service, data driven attack to applications, privilege escalation or intruder login, main file access from intruders, and host-based attacks. This system prevents the access to the server cluster and blocks direct attacks by forming a barrier layer with a firewall and the proxy. It can detect various attacks through checking the file integrity by the operation of challenge/response protocol. This system also finds the infected server by checking the responses from each server. In addition, this system has much more active corresponding level than the existing scalable intrusion-tolerant architecture. In this intrusion detection system, both the data analysis and detection proceed simultaneously through the migration of the system, which maintains the operation of the system as it was. This virtualization technique is good for the implementation of the intrusion tolerance system and the migration of the mobile payment system in active state to another system. Especially, the live migration method is very effective to minimize the loss of time and data. Application/Improvements: Especially, this intrusion tolerant system makes the operating system safe from mobile payments intrusion and malicious software.

With the development of computer network, the network security is increasingly important. Recently intrusion detection becomes a novel network security strategy in order to achieve the purpose for network system security dynamic detection and surveillance. This paper introduces the structure and detection of the intrusion detection system and proposes an integrated intrusion detection system based on multiple detection methods and techniques. This paper also introduces the knowledge base of the intrusion behavior rules and achieves the optimal goals by data analysis and matching to ensure the satisfactory performance of network system security.

Intrusion detection systems play a key role in detecting such malicious activities and enable administrators in securing network systems. Intrusion detection can detect malicious attacks that have penetrated preventative mechanisms such as firewalls, which can help provide damage assessment, response and prosecution support. This paper describes a novel approach using Hidden Markov Models (HMM) to detect Internet attacks. In this paper we describe an intrusion detection system for detection of signature based attack. These attack signatures encompass specific traffic or activity that is based on known intrusive activity. We performed single and multiple HMM model for source separation both on IP and port information of source and destination. This approach reduced the false positive rate and we made this type of source separation as our basic step for building HMM.

As the usage of smart devices is increasing, malware affecting such devices is rapidly evolving as well. Security risks affecting the confidentiality, integrity, and privacy of smart devices are rapidly emerging. Mobile security suites exist to defend the device against malware and other intrusions. However, they require extensive resources which is a constraint of the device itself. In this paper, we address the problem of intrusion detection for smart devices taking into account the devices' limited resources such as energy, CPU usage, and internet connectivity. We provide an ad-hoc mobile cloud based intrusion detection framework that takes advantage of Wi-Fi Direct as means of achieving connectivity and sharing resources and services for providing security. The proposed framework allows exchange of data with or without the availability of internet connection. The paper also provides experiments, carried out using real devices, showing various improvements of using our approach. Up to 61% and 40% enhancement in energy consumption and response time respectively is reached compared to local execution.

The goal of an intrusion detection system (IDS) is to monitor activities to detect breaches in security policies of a computer system or a network. This paper focuses on anomaly detection paradigm of IDS. The goal of anomaly-based IDS is to classify intrusion based on system and network activities outside of a normal region. In this paper we employ a multipledetector set artificial immune system, a variation of artificial immune system, to classify intrusion based on features of application layer protocols (e.g., http, ftp, smtp, etc.) in network data flows. Our result shows the multiple-detector set artificial immune system achieved a Detection Rate of 53.34% and a False Positive Rate of 0.20%. The mAIS achieved an accuracy of 76.57%.

Data mining for intrusion detection is one of the most cutting-edge researches which focus on network security, database, and information decision-making. Due to the emergence of new forms of attacks and intrusion on the network, we need a new intrusion detection system which would be able to detect new and unknown attacks. In the paper, by studying the characteristics of network data intrusion, we put forward a intrusion detection system based on Rough set theory, and detect anomaly action in network. This method can extract detection rule model of the network connection data, dealing with incomplete data and basic discrete data exit in data mining effectively. The experiments results show that, models, methods and generation framework proposed in this paper can effectively detect network intrusion.

In today's era security is one of the main concern in every field also in wireless sensor networks. Resource limitation is main concern of sensor nodes in wireless sensor networks. There are many security threats which are affecting the, functionality, security and network life time of and wireless sensor networks. In this paper security threats, security goals, various attacks, classification of these attacks are presented along with the comparison of different intrusion detection systems in wireless sensor networks. Detailed information about intrusion detection systems is provided then, intrusion detection methodologies are compared based on different schemes. Intrusion detection schemes are categorized based on techniques used in the scheme: Specification based scheme, computational intelligence and data mining based scheme, game theory approach based intrusion detection scheme, probability distribution based detection scheme. At the end of paper advantages and disadvantages of each scheme is also presented.

The increasing amount of network traffic threat may originates from various sources, that can led to a higher probability for an organization to be exposed to intruder. Security mechanism such as Intrusion Detection System (IDS) is significant to alleviate such issue. Despite the ability of IDS to detect, some of the anomaly traffic may not be effectively detected. As such, it is vital the IDS algorithm to be reliable and can provide high detection accuracy, reducing as much as possible threats from the network. Nonetheless, every security mechanism has its weaknesses that can be exploited by intruders. Many research works exists, that attempts to address the issue using various methods. This paper discusses a hybrid approach to network IDS, which can minimize the malicious traffic in the network by using machine learning. The paper also provides a review of the available methods to further improve Anomaly-based Network Intrusion Detection System.

ssecurity is a major concern. Cloud computing and Intrusion Detection and Prevention Systems are one such measure to mitigate these attacks. Different researchers have proposed different IDSs time to time some of these IDS's combine features of two or more IDSs which are called as Hybrid Intrusion Detection Systems. Most of the researchers combine the features of Signature based detection methodology and Anomaly based detection methodology. For a signature based IDS if an attacker attacks slowly and organized, the attack may go undetected through the IDS, as signatures include factors which are based on duration of the events and the actions of attacker do not match. Sometimes, for an unknown attack there is no signature updated or an attacker attack in the mean time when the database is updating. Thus, signature-based IDS fail to detect unknown attacks. Anomaly based IDS suffer from many false-positive readings. Thus there is a need to hybridize those IDS which can overcome the shortcomings of each other. In this paper we proposed a new approach to IDS (Intrusion Detection System) which is more efficient than the traditional IDS (Intrusion Detection System). The IDS is based on Honeypot Technology and Anomaly based Detection Methodology. We have designed Architecture for the IDS in a packet tracer and then implemented it in real time. We have discussed experimental results performed both the Honeypot and Anomaly based IDS have some shortcomings but if we hybridized these two technologies, the newly proposed HIDS is capable enough to overcome these shortcomings with much enhanced performance. In this paper, we present a modified Hybrid Intrusion Detection System (HIDS) that combines the positive features of two different detection methodologies - Honeypot methodology and anomaly based intrusion detection methodology. In the experiment we run both the Intrusion Detection System individually first and then together and record the data from time to time. From the data we can conclude that the resulting IDS is much better in detecting intrusions from the existing IDSs.

The main objective of this paper is to design a more complete intrusion detection system solution. The paper presents an efficient approach for reducing the rate of alerts using divided two-part adaptive intrusion detection system (DTPAIDS). The proposed DTPAIDS has a high degree of autonomy in tracking suspicious activity and detecting positive intrusions. The proposed DTPAIDS is designed with the aim of reducing the rate of detected false positive intrusion through two achievements. The first achievement is done by implementing adaptive self-learning neural network in the proposed DTPAIDS to gives it the ability to be automatic adaptively system based on Radial Basis Functions (RBF) neural network. The second achievement is done through dividing the proposed intrusion detection system IDS into two parts. The first part is IDS1, which is installed in the front of firewall and responsible for checking each entry user's packet and deciding if the packet considered is an attack or not. The second is IDS2, which is installed behind the firewall and responsible for detecting only the attacks which passed the firewall. This proposed approach for IDS exhibits a lower false alarm rate when detects novel attacks. The simulation tests are conducted using DARPA 1998 dataset. The experimental results show that the proposed DTPAIDS [1] reduce false positive rate, [2] detects intrusion occurrence sensitively and precisely, [3] accurately self---adapts diagnoser model, thus improving its detection accuracy.

Wireless mesh networks are very common both for organizations and individuals. Many laptops, computers have wireless cards pre-installed for buyer. However a wireless networking has many security issues. An intrusions detection system aim to detect the different attacks against network and system. An intrusion detection system should be capable for detecting the misuse of the network whether it will be by the authenticated user or by an attacker. They detect attempts and active misuse either by legitimate users of the information systems or by external. The present paper deals with cross layer based intrusion detection system for wireless domain—a critical anlaysis. The present paper deals with role of cross layer based intrusion detection system for wireless domain.

Network threats are a security risk which can be met on a daily basis. Because of this, it is important today to consider more complex security options than just ordinary firewall systems. This paper deals about the advantages and disadvantages of the solution called Intrusion Detection System (IDS) and Intrusion Prevention System (IPS). This paper describes the behavior and principles of IDS/IPS systems and presents the comparison of commercial and open-sourced systems.

This paper highlights different security threats and vulnerabilities that is being challenged in smart-grid utilizing Distributed Network Protocol (DNP3) as a real time communication protocol. Experimentally, we will demonstrate two scenarios of attacks, unsolicited message attack and data set injection. The experiments were run on a computer virtual environment and then simulated in DETER testbed platform. The use of intrusion detection system will be necessary to identify attackers targeting different part of the smart grid infrastructure. Therefore, mitigation techniques will be used to ensure a healthy check of the network and we will propose the use of host-based intrusion detection agent at each Intelligent Electronic Device (IED) for the purpose of detecting the intrusion and mitigating it. Performing attacks, attack detection, prevention and counter measures will be our primary goal to achieve in this research paper.

This paper is a survey of the work, done for making an IDS fault tolerant.Architecture of IDS that uses mobile Agent provides higher scalability. Mobile Agent uses Platform for detecting Intrusions using filter Agent, co-relater agent, Interpreter agent and rule database. When server (IDS Monitor) goes down, other hosts based on priority takes Ownership. This architecture uses decentralized collection and analysis for identifying Intrusion. Rule sets are fed based on user-behaviour or applicationbehaviour.This paper suggests that intrusion detection system (IDS) must be fault tolerant; otherwise, the intruder may first subvert the IDS then attack the target system at will.

As virtualization technology gains in popularity, so do attempts to compromise the security and integrity of virtualized computing resources. Anti-virus software and firewall programs are typically deployed in the guest virtual machine to detect malicious software. These security measures are effective in detecting known malware, but do little to protect against new variants of intrusions. Intrusion detection systems (IDSs) can be used to detect malicious behavior. Most intrusion detection systems for virtual execution environments track behavior at the application or operating system level, using virtualization as a means to isolate themselves from a compromised virtual machine. In this paper, we present a novel approach to intrusion detection of virtual server environments which utilizes only information available from the perspective of the virtual machine monitor (VMM). Such an IDS can harness the ability of the VMM to isolate and manage several virtual machines (VMs), making it possible to provide monitoring of intrusions at a common level across VMs. It also offers unique advantages over recent advances in intrusion detection for virtual machine environments. By working purely at the VMM-level, the IDS does not depend on structures or abstractions visible to the OS (e.g., file systems), which are susceptible to attacks and can be modified by malware to contain corrupted information (e.g., the Windows registry). In addition, being situated within the VMM provides ease of deployment as the IDS is not tied to a specific OS and can be deployed transparently below different operating systems. Due to the semantic gap between the information available to the VMM and the actual application behavior, we employ the power of data mining techniques to extract useful nuggets of knowledge from the raw, low-level architectural data. We show in this paper that by working entirely at the VMM-level, we are able to capture enough information to characterize normal executions and identify the presence of abnormal malicious behavior. Our experiments on over 300 real-world malware and exploits illustrate that there is sufficient information embedded within the VMM-level data to allow accurate detection of malicious attacks, with an acceptable false alarm rate.

This paper proposes a Network Intrusion Detection System (NIDS) embedded in a smart-sensor-inspired device under a service-oriented architecture (SOA) approach which is able to operate independently as an anomaly-based NIDS, or integrated transparently in a Distributed Intrusion Detection System (DIDS). The proposal is innovative because it combines the advantages of the smart sensor approach and the subsequent offering of the NIDS functionality as a service with the SOA use to achieve their integration with other DIDS components. The main goal of this paper is to reduce the huge volume of management tasks inherent to this type of network services, as well as facilitating the design of DIDS whose managing complexity could be restricted within well-defined margins. This paper also addresses the construction of a physical sensor prototype. This prototype was used to carry out the tests that has demonstrated the proposal's validity, providing detection and performance ratios similar to those of existing intrusion detection systems (IDS), but with the advantage of a zero-maintenance approach.

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.

This paper firstly introduces the status of network safety, network safety aim, traditional network safety technology and necessity of network intrusion detection system (NIDS). Secondly, this paper discusses the concepts and definitions of NIDS. Thirdly, this paper analyzes structure and key technologies of NIDS. Finally, introduces an example software of NIDS –snort.

This paper proposes the use of an intrusion detection system (IDS) tailored to counter the threats to an IEC61850-automated substation based upon simulated attacks on intelligent electronic devices (IEDs). Intrusion detection (ID) is the process of detecting a malicious attacker. It is an effective and mature security mechanism. However, it is not harnessed when securing IEC61850-automated substations. The IDS of this paper is developed by using data collected by launching simulated attacks on IEDs and launching packet sniffing attacks using forged address resolution protocol (ARP) packets. The detection capability of the system is then tested by simulating attacks and through genuine user activity. A new method for evaluating the temporal risk of an intrusion for an electric substation based upon the statistical analysis of known attacks is also proposed.

Intrusion detection is an important area of research. Traditionally, the approach taken to find attacks is to inspect the contents of every packet. However, packet inspection cannot easily be performed at high-speeds. Therefore, researchers and operators started investigating alternative approaches, such as flow-based intrusion detection. In that approach the flow of data through the network is analyzed, instead of the contents of each individual packet. The goal of this paper is to provide a survey of current research in the area of flow-based intrusion detection. The survey starts with a motivation why flow-based intrusion detection is needed. The concept of flows is explained, and relevant standards are identified. The paper provides a classification of attacks and defense techniques and shows how flow-based techniques can be used to detect scans, worms, Botnets and (DoS) attacks.

The IP-based Ubiquitous Sensor Network (IP-USN) is an effort to build the “Internet of things”. By utilizing IP for low power networks, we can benefit from existing well established tools and technologies of IP networks. Along with many other unresolved issues, securing IP-USN is of great concern for researchers so that future market satisfaction and demands can be met. Without proper security measures, both reactive and proactive, it is hard to envisage an IP-USN realm. In this paper we present a design of an IDS (Intrusion Detection System) called RIDES (Robust Intrusion DEtection System) for IP-USN. RIDES is a hybrid intrusion detection system, which incorporates both Signature and Anomaly based intrusion detection components. For signature based intrusion detection this paper only discusses the implementation of distributed pattern matching algorithm with the help of signature-code, a dynamically created attack-signature identifier. Other aspects, such as creation of rules are not discussed. On the other hand, for anomaly based detection we propose a scoring classifier based on the SPC (Statistical Process Control) technique called CUSUM charts. We also investigate the settings and their effects on the performance of related parameters for both of the components.

Introduction An intrusion is defined as any set of actions that attempt to compromise the integrity, confidentiality or availability of a resource. An intrusion takes place when an attacker or group of attackers exploit security vulnerabilities and thus violate the CIA guarantees of a system. Intrusion detection is therefore required as an additional wall for protecting systems. Intrusion detection is simply an act of detecting intrusions. Intrusion Detection System (IDS) is an authorized way of identifying illegitimate users, attacks and vulnerabilities that could affect the proper functioning of computer systems. IDSs detect some set of intrusions and execute some predetermined actions when an intrusion is detected (W. Wang et al., 2006). However, the initial designs of IDS are faced with some shortcomings listed as follows: (i) Delay of time. (ii) A single point of failure. (iii) Limited scalability. (iv) Hard to communicate mutually between different IDSs In order to solve the aforementioned shortcomings, mobile agent technology is currently applied to IDS. Mobile agent is a particular type of software agents which has the capability of moving from one host to another. It is an autonomous program situated within an environment, which senses the environment and acts upon it using its knowledge base to achieve its goals. Mobile agent is of the features of reducing network overload, overcoming network latency, synchronous and autonomous execution, robustness and fault-tolerance, system scalability and operating in heterogeneous environments. To this end, MA technology is very suitable to solve intrusion detection in a distributed environment (Chan & Wei, 2002), hence the advent of Mobile Agent based IDS (MA-IDS). MA-IDSs are also faced with some shortcomings such as: a. High time to detection: MA solutions may not be fast enough to meet the needs of IDS. One of the major challenging problems facing MA-IDS is improving the speed with which they can identify malicious activities. b. Performance: though MA technology has improved greatly on detection performance, but effective detection of autonomous attacks is still very low. Also, agents are often written in scripting or interpreted languages, which are easily ported between different platforms. Their mode of execution is still very low compared to native codes (Kruegel and Toth, 2002). c. Security: Another major problem is protecting the protector (MA-IDS) from attacks. Hence, the thrust of this paper is to critically examine the existing and most referenced MA-IDSs. The paper is organized as follows: the second section discusses the proposed classification of Intrusion detection system; the third section examines the existing literature on MA-IDSs; in the fourth section, the proposed architecture is discussed considering the shortcomings of current design; the fifth section concludes the work. Classification of Intrusion Detection System It is a well-known fact that the research in a field greatly benefits from a good taxonomy and hence a good classification. There have been several defined taxonomies, classifications and subsequent surveys for intrusion detection. The goals of the efforts in several classifications have also been quite diverse; some only try to survey the field and find it easier with labels on the systems, while others try to use the taxonomies for a deeper understanding or to guide future research efforts. Despite these previous efforts, intrusion detection still lacks a widely applicable and accepted taxonomy. This may in part be because of it being a young research field, part of it being fast-paced and maybe part of it owing to its inherent complexity (Almgren et al., 2003). This paper aims to broadly classify IDS based on its necessary features. Figure 1 shows our proposed classification of a typical intrusion detection system and its description follows. …

In the world of communication, security is a big concern. Most of our crucial data is stored in a computer system and in most cases we exchange it over a network. But it's not just our data transmitting over the network but different types of attacks too. These attacks can harm our stored data. Monitoring computer system and its logs (administration logs, security logs, system logs, network logs) and protecting our crucial data is necessary. For these necessities we use intrusion detection system. An intrusion detection system is an application that provides protection from malicious activities or policy violations and generates various rules to defend computer security. Intrusion detection system can be designed and developed on any platform but for its better functionality we are using data mining technique. In past years, many techniques have been introduced to improvise the detection rate. Earlier, in the initial stages of its designing, hardware had to be installed to detect and monitor the system. But, with the help of data mining it has become easier to work with software and algorithm development. In the recent trends, many new algorithms have been introduced to increase its efficiency. They are categorized under machine learning algorithms: supervised, unsupervised and hybrid. Though hybrid has not yet been categorized finely but various authors have used it by merging different machine learning algorithms. Machine learning algorithms provide a process of detecting intrusion and generating rules for its detection and prevention. Rule generation is defined by association rule mining and apriori algorithm. An intrusion detection system is not a new application but developing a prototype which will work for the saved logs (administration logs, security logs and system logs) and monitor network logs on host system as well as on client system, so that the Intrusion detection system can alert the user on regular basis, is. In this paper we are using a hybrid machine learning algorithm following with rule generation algorithm to detect the intrusion in the network logs by training KDD dataset. Training and Testing KDD data set provides the way of analyzing the actual behavior and predicted behavior of the network logs. This paper shows the methodology and results in Netbeans IDE 8.1(Java platform using Weka library). Through the obtained results, it can be interpreted that optimal performance is obtained against most attacks after detection.

Wireless sensor network (WSN) consists of a small size sensor nodes depends on battery power. Applications of sensor networks are street lighting, home automation, industrial plant, environmental parameters, health care and military. Security is becoming a major concept in the design of wireless sensor networks. Attacks on sensor networks include attack on confidentiality, integrity and availability. Lack of confidentiality means that the secret information is revealed to attackers. Lack of integrity means that the attacker modifies the contents (data) communicated in the sensor network. Lack of availability means the resources in the network is not available to authorized users. So to avoid these problems we need better security measures. In this paper we are dealing with intrusion detection. Intrusion detection plays an important role in the design of wireless sensor networks. An intruder can perform various attacks like replay, interception, worm hole, sink hole attacks. So our aim in this paper is to find such intrusions as early as possible, when they occurred and to enhance the life tome of the sensor network. This is because sensors nodes depend only on battery power. Our intrusion detection is based on clustering of the sensor network. This makes routing process, medium control and intrusion detection simple. DOI: http://dx.doi.org/10.11591/telkomnika.v15i1.8072

The next generation protocol IPv6 brings the new challenges to the information security. This paper presents the design and implementation of a network-based intrusion detection system that support both IPv6 protocol and IPv6 protocol. This system's architecture is focused on performance, simplicity, and scalability. There are four primary subsystems that make it up: the packet capture, the packet decoder, the detection engine, and the logging and alerting subsystem. This paper further describes a new approach to packet capture whose goal is to improve the performance of the capture process at high speeds. The evaluation shows that the system has a good performance to detect IPv6 attacks and IPv4 attacks, and achieves 61% correct detection rate with 20% false detection rate at the speed of 100 Mb·s−1.

In computer and network security, standard approaches to intrusion detection and response attempt to detect and prevent individual attacks. Intrusion Detection System (IDS) and intrusion prevention systems (IPS) are real-time software for risk assessment by monitoring for suspicious activity at the network and system layer. Software scanner allows network administrator to audit the network for vulnerabilities and thus securing potential holes before attackers take advantage of them. In this paper we try to define the intruder, types of intruders, detection behaviors, detection approaches and detection techniques. This paper presents a structural approach to the IDS by introducing a classification of IDS. It presents important features, advantages and disadvantages of each detection approach and the corresponding detection techniques. Furthermore, this paper introduces the wireless intrusion protection systems. The goal of this paper is to place some characteristics of good IDS and examine the positioning of intrusion prevention as part of an overall layered security strategy and a review of evaluation criteria for identifying and selecting IDS and IPS. With this, we hope to introduce a good characteristic in order to improve the capabilities for early detection of distributed attacks in the preliminary phases against infrastructure and take a full spectrum of manual and automatic response actions against the source of attacks.

Intrusion Detection System or IDS is a software or hardware tool that repeatedly scans and monitors events that took place in a computer or a network. A set of rules are used by Signature based Network Intrusion Detection Systems or NIDS to detect hostile traffic in network segments or packets, which are so important in detecting malicious and anomalous behaviour over the network like known attacks that hackers look for new techniques to go unseen. Sometime, a single failure at any layer will cause the NIDS to miss that attack. To overcome this problem, a technique is used that will trigger a failure in that layer. Such technique is known as Evasive technique. An Evasion can be defined as any technique that modifies a visible attack into any other form in order to stay away from being detect. The proposed system is used for detecting attacks which are going on the network and also gives actual categorization of attacks. The proposed system has advantage of getting low false alarm rate and high detection rate. So that leads into decrease in complexity and overhead on the system. The paper presents the Evasion technique for customized apriori algorithm. The paper aims to make a new functional structure to evade NIDS. This framework can be used to audit NIDS. This framework shows that a proof of concept showing how to evade a self built NIDS considering two publicly available datasets., Comment: 5 pages, 3 figures

Intrusion detection is an important aspect of modern cyber-enabled infrastructure in identifying threats to digital assets. Intrusion detection encompasses tools, techniques and strategies to recognize evolving threats thereby contributing to a secure and trustworthy computing framework. There are two primary intrusion detection paradigms, signature pattern matching and anomaly detection. The paradigm of signature pattern matching encompasses the identification of known threat sequences of causal events and matching it to incoming events. If the pattern of incoming events matches the signature of an attack there is a positive match which can be labeled for further processing of countermeasures. The paradigm of anomaly detection is based on the premise that an attack signature is unknown. Events can deviate from normal digital behavior or can inadvertently give out information in normal event processing. These stochastic events have to be evaluated by variety of techniques such as artificial intelligence, prediction models etc. before identifying potential threats to the digital assets in a cyber-enabled system. Once a pattern is identified in the evaluation process after excluding false positives and negative this pattern can be classified as a signature pattern. This paper highlights a setup in an educational environment to effectively flag threats to the digital assets in the system using an intrusion detection framework. Intrusion detection framework comes in two primary formats a network intrusion detection system and a host intrusion detection system. In this paper we identify different publicly available tools of intrusion detection and their effectiveness in a test environment. This paper also looks at the mix of tools that can be deployed to effectively flag threats as they evolve. The effect of encryption in such setup and threat identification with encryption is also studied.

In today's era of drastically improved technology and increasing scalability for large number of users, need for a secure system has grown immensely. To protect networks from unauthorized entries and hackers, intrusion detection systems provides improved monitoring of malicious entries and provides integrity to the system. This growing problem has motivated widespread research in particular field. Till now, many approaches has been defined by various researchers to improve the detection rate of intrusions over networks. This paper provides a brief study on significance of evolutionary approach genetic algorithm for intrusion detection to classify and detect intrusions. Methodology of genetic algorithm based intrusion detection systems and review on different approaches introduced in the field has been illustrated with the help of flowcharts for easy interpretation. This paper will provide a helpful review to the promising researchers on the relevant topic.

It is important for critical applications to provide critical services without any integrity or availability degradation in the presence of intrusions. This requirement can be satisfied by intrusion masking techniques under some situations. Compared with intrusion tolerance techniques, where some integrity or availability degradations are usually caused, intrusion masking techniques use substantial replications to avoid such degradations. Existing intrusion masking techniques, such as the state machine approach, can effectively mask intrusions when processing requests from a client using a server replica group, but they are fairly limited in processing a (multi-stage) distributed operation across multiple server replica groups. As more and more applications (e.g., supply chain management, distributed banking) need to process distributed operations in an intrusion-masking fashion, it is in urgent need to overcome the limitations of existing intrusion masking techniques. In this paper, we specify and compose two intrusion-masking models for inter-replica-group distributed computing. Using these two models, a variety of applications can mask (numerous kinds of) intrusions. Our intrusion masking models overcome the limitations of existing intrusion masking techniques. The survivability of our intrusion-masking models is quantitatively analyzed. A simple yet practical implementation method of our intrusion-masking models is proposed and applied to build two intrusion-masking two-phase-commit (2PC) protocols, and the corresponding efficiency is analyzed. The two intrusion-masking 2PC protocols and the analysis results show that the proposed intrusion-masking models have good utility, practicality, and survivability. Finally, the composition methodology developed in this paper can also be used to develop other intrusion-masking distributed computing models.