A network attack discovery algorithm based on unbalanced sampling vehicle evolution strategy for intrusion detection

2017 ◽  
Vol 42 (1) ◽  
pp. 84-92
Author(s):  
Zhang Yong-xiong ◽  
Wang Liang-ming ◽  
Yi Lu-xia
Keyword(s):  
Intrusion Detection ◽  
Evolution Strategy ◽  
Network Attack ◽  
Unbalanced Sampling

scholarly journals Research on Network Intrusion Detection Based on Incremental Extreme Learning Machine and Adaptive Principal Component Analysis

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1223 ◽  
Author(s):  
Jianlei Gao ◽  
Senchun Chai ◽  
Baihai Zhang ◽  
Yuanqing Xia
Keyword(s):  
Intrusion Detection ◽  
Extreme Learning Machine ◽  
Detection System ◽  
Principal Component ◽  
Detection Accuracy ◽  
Network Attack ◽  
Network Intrusion ◽  
Learning Machine ◽  
Malicious Behaviors ◽  
Adaptive Principal Component Analysis

Recently, network attacks launched by malicious attackers have seriously affected modern life and enterprise production, and these network attack samples have the characteristic of type imbalance, which undoubtedly increases the difficulty of intrusion detection. In response to this problem, it would naturally be very meaningful to design an intrusion detection system (IDS) to effectively and quickly identify and detect malicious behaviors. In our work, we have proposed a method for an IDS-combined incremental extreme learning machine (I-ELM) with an adaptive principal component (A-PCA). In this method, the relevant features of network traffic are adaptively selected, where the best detection accuracy can then be obtained by I-ELM. We have used the NSL-KDD standard dataset and UNSW-NB15 standard dataset to evaluate the performance of our proposed method. Through analysis of the experimental results, we can see that our proposed method has better computation capacity, stronger generalization ability, and higher accuracy.

scholarly journals Fusion of Heterogeneous Intrusion Detection Systems for Network Attack Detection

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Jayakumar Kaliappan ◽  
Revathi Thiagarajan ◽  
Karpagam Sundararajan
Keyword(s):  
Intrusion Detection ◽  
Detection Rate ◽  
Detection System ◽  
Attack Detection ◽  
Majority Voting ◽  
Final Decision ◽  
Network Attack ◽  
Voting Rule ◽  
Specific Category ◽  
Local Decision

An intrusion detection system (IDS) helps to identify different types of attacks in general, and the detection rate will be higher for some specific category of attacks. This paper is designed on the idea that each IDS is efficient in detecting a specific type of attack. In proposed Multiple IDS Unit (MIU), there are five IDS units, and each IDS follows a unique algorithm to detect attacks. The feature selection is done with the help of genetic algorithm. The selected features of the input traffic are passed on to the MIU for processing. The decision from each IDS is termed as local decision. The fusion unit inside the MIU processes all the local decisions with the help of majority voting rule and makes the final decision. The proposed system shows a very good improvement in detection rate and reduces the false alarm rate.

scholarly journals DEVELOPMENT OF AN INTRUSION DETECTION SYSTEM IN A COMPUTER NETWORK

2014 ◽  
Vol 12 (5) ◽  
pp. 3479-3485
Author(s):  
Babatunde R.S ◽  
Adewole K.S ◽  
Abdulsalam S.O ◽  
Isiaka R.M
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Preventive Measures ◽  
Computer Network ◽  
Detection System ◽  
Denial Of Service ◽  
Network System ◽  
Network Attack ◽  
Network Technologies

The development of network technologies and application has promoted network attack both in number and severity.  The last few years have seen a dramatic increase in the number of attacks, hence, intrusion detection has become the mainstream of information assurance. A computer network system should provide confidentiality, integrity and assurance against denial of service. While firewalls do provide some protection, they do not provide full protection.  This is because not all access to the network occurs through the firewall.  This is why firewalls need to be complemented by an intrusion detection system (IDS).An IDS does not usually take preventive measures when an attack is detected; it is a reactive rather than proactive agent. It plays the role of an informant rather than a police officer. In this research, an intrusion detection system that can be used to deny illegitimate access to some operations was developed. The IDS also controls the kind of operations performed by users (i.e. clients) on the network. However, unlike other methods, this requires no encryption or cryptographic processing on a per-packet basis. Instead, it scans the various messages sent on a network by the user. The system was developed using MicrosoftVisual Basic.

scholarly journals Network Intrusion Detection Techniques and Network Attack Types

2019 ◽  
Vol 8 (2S8) ◽  
pp. 934-940
Keyword(s):  
Intrusion Detection ◽  
Anomaly Detection ◽  
Network Intrusion Detection ◽  
Network Attack ◽  
Detection Techniques ◽  
Network Intrusion

With the huge improvement in innovation and with the big utilization of internet, massive increment in internet dangers has been seeing which activates contriving of latest strategies in device protection. those types of gadget attacks as some distance as unapproved get to, unordinary assaults can be remedy making use of network Intrusion Detection device(NIDS). machine Anomaly Detection(NAD) framework is a particular assortment of IDS. it's miles a corresponding innovation to frameworks that distinguish safety dangers depending on parcel marks. In NAD, the device is unendingly decided for event of anomalous activities or unexpected assaults. by way of utilising this NAD techniques, it is plausible to select out in the occasion that all and sundry tries to attack property or particular has with the aid of using and contrasting and the records accrued from past regarded assaults. This paper gives diagram on severa classifications NID techniques and moreover numerous styles of structures assaults. We bear in mind that this audit will provide a advanced keen of the special commands of attack sorts records, which gives degree to analyze to retain similarly.

scholarly journals Towards a Reliable Comparison and Evaluation of Network Intrusion Detection Systems Based on Machine Learning Approaches

2020 ◽  
Vol 10 (5) ◽  
pp. 1775 ◽  
Author(s):  
Roberto Magán-Carrión ◽  
Daniel Urda ◽  
Ignacio Díaz-Cano ◽  
Bernabé Dorronsoro
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Attack Detection ◽  
Intrusion Detection Systems ◽  
Network Intrusion Detection ◽  
Learning Approaches ◽  
Network Attack ◽  
Detection Systems ◽  
Network Intrusion ◽  
Network Intrusion Detection Systems

Presently, we are living in a hyper-connected world where millions of heterogeneous devices are continuously sharing information in different application contexts for wellness, improving communications, digital businesses, etc. However, the bigger the number of devices and connections are, the higher the risk of security threats in this scenario. To counteract against malicious behaviours and preserve essential security services, Network Intrusion Detection Systems (NIDSs) are the most widely used defence line in communications networks. Nevertheless, there is no standard methodology to evaluate and fairly compare NIDSs. Most of the proposals elude mentioning crucial steps regarding NIDSs validation that make their comparison hard or even impossible. This work firstly includes a comprehensive study of recent NIDSs based on machine learning approaches, concluding that almost all of them do not accomplish with what authors of this paper consider mandatory steps for a reliable comparison and evaluation of NIDSs. Secondly, a structured methodology is proposed and assessed on the UGR’16 dataset to test its suitability for addressing network attack detection problems. The guideline and steps recommended will definitively help the research community to fairly assess NIDSs, although the definitive framework is not a trivial task and, therefore, some extra effort should still be made to improve its understandability and usability further.

Flow Based Classification for Specification Based Intrusion Detection in Software Defined Networking

2019 ◽  
Vol 7 (2) ◽  
pp. 1-8
Author(s):  
Nithya Sampath ◽  
Dinakaran M.
Keyword(s):  
Intrusion Detection ◽  
Information Gain ◽  
Detection System ◽  
Software Defined Networking ◽  
Network Attack ◽  
Detection Systems ◽  
Learning Techniques ◽  
Proposed Model ◽  
And Control

Software defined networking assures the space for network management, SDNs will possibly replace traditional networks by decoupling the data plane and control plane which provides security by means of a global visibility of the network state. This separation provides a solution for developing secure framework efficiently. Open flow protocol provides a programmatic control over the network traffic by writing rules, which acts as a network attack defence. A robust framework is proposed for intrusion detection systems by integrating the feature ranking using information gain for minimizing the irrelevant features for SDN, writing fuzzy-association flow rules and supervised learning techniques for effective classification of intruders. The experimental results obtained on the KDD dataset shows that the proposed model performs with a higher accuracy, and generates an effective intrusion detection system and reduces the ratio of attack traffic.

Cross-Layer Based Intrusion Detection and Prevention for Network

2018 ◽  
pp. 38-56 ◽  
Author(s):  
Reema Kumari ◽  
Kavita Sharma
Keyword(s):  
Network Security ◽  
Intrusion Detection ◽  
Mobile Computing ◽  
Intrusion Detection System ◽  
Detection System ◽  
Malicious Node ◽  
New Techniques ◽  
Network Attack ◽  
Day By Day ◽  
Intrusion Detection And Prevention

Day by day technologies for mobile computing growing rapidly and its network security changed according to their need. The attacker always trying to learn some new techniques to break those security walls of the wireless network. To prevent our network from attacker various defense techniques are used. Firewalls and encryption are used to prevent our network from malware but it is not sufficient for protecting the networks. Many researchers implement new architecture and techniques or mechanism that protect and detect malicious node and their activity over the network that is intrusion detection system (IDS). IDS provides security wall and it provides network security as well as it has continuously monitored and taken appropriate action against the threat. In this Chapter, we are trying to explain some network attack that is resolved or detect through intrusion detection system by exploiting the technology or information that available across different layers of the protocol stack in order to improve the accuracy of detection.

scholarly journals The Effective Methods for Intrusion Detection With Limited Network Attack Data: Multi-Task Learning and Oversampling

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 185384-185398
Author(s):  
Lijian Sun ◽  
Yun Zhou ◽  
Yanjuan Wang ◽  
Cheng Zhu ◽  
Weiming Zhang
Keyword(s):  
Intrusion Detection ◽  
Network Attack ◽  
Task Learning ◽  
Attack Data

scholarly journals History Aware Anomaly Based IDS for Cloud IaaS

2013 ◽  
Vol 10 (6) ◽  
pp. 1779-1784 ◽  
Author(s):  
Punit Gupta ◽  
Pallavi Kaliyar
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Dos Attack ◽  
Infrastructure As A Service ◽  
Network Attack ◽  
Security Challenges ◽  
Different Types ◽  
Cross Site

Cloud Computing provides different types of services  such as SaaS, PaaS, IaaS. Each of them have their own security challenges, but IaaS undertakes all types of challenges viz., network attack ,behaviour based attack, request based attacks  i.e handling the requests from untrusted users, XSS (cross site scripting attack), DDOS and many more. These attacks are independent of each other and consequently the QoS provided by cloud is compromised. This paper proposes a History aware Behaviour based IDS (Intrusion Detection System) BIDS. BIDS provides detection of untrusted users, false requests that may lead to spoofing, XSS  or DOS attack and many more such attacks. In addition,  certain cases where user login or password is compromised. History aware BIDs can be helpful in detecting such attacks and maintaining the QoS provided to the user in cloud IaaS ( Infrastructure as a service).

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