An Efficient Intrusion Detection Model Based on Fast Inductive Learning

Wu, Yaming; Wan, Wei; Guo, Lin; Zhang, Lejun

doi:10.1109/icmlc.2007.4370708

Cited by 12 publications

(3 citation statements)

References 6 publications

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“…Various researchers have given the following definitions on matter intrusion detection systems and technology in recent years. According to Yang and others [10], IDS is a system that detects and identifies intrusion behavior or intrusion enterprises by monitoring and analyzing Ag packets or system audit records in a computer system and then giving real-time intrusion warnings to system administrators.…”

Section: Literature Reviewmentioning

confidence: 99%

“…According to the attack detection technique, some studies assumed that they are in systems that detect identification/hybrid attacks, a mixture of misuse and abnormality models, as an additional category [14,15]. Also, detection of abnormality attacks can be decomposed by statistical abnormality test, artificial neural networks, full based on the detection of abnormality, data mining based on the detection of anomalies, immune-based abnormalities detection, and so on [10,16].…”

Section: Literature Reviewmentioning

confidence: 99%

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Comparison of the Host Based Intrusion Detection Systems and Network Based Intrusion Detection Systems

Efe¹,

ABACI

2022

Celal Bayar Üniversitesi Fen Bilimleri Dergisi

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Advanced Persistent Threat (APT) has recently emerged as sophisticated and tailor-made attacks. APTs pose threats mainly targeting military, defense, security infrastructure, high profile companies, and government units. Intrusion detection mechanisms are crucial for adequate protection, especially as a countermeasure for APT attacks done by hacktivists, cyber warriors, and cyber terrorists over management information systems (MIS) of government institutions and e-government applications. In this study, Intrusion detection and prevention systems have been studied in detail after being referred to the tasks and abilities of the intrusion detection systems that are at the core of the computer security technology presented today to meet the increasing need for information and network security. This paper aims to specify the differences between Host Based Intrusion Detection Systems (HIDS) and Network-Based Intrusion Detection Systems (NIDS) and compares the tools using HIDS and NIDS. It is asserted that to better assurance for APT attacks, there should be a Hybrid IDS approach covering both networks and hosts using both signature and behavioral detection mechanisms based on deep learning algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Comparison of the Host Based Intrusion Detection Systems and Network Based Intrusion Detection Systems

Efe¹,

ABACI

2022

Celal Bayar Üniversitesi Fen Bilimleri Dergisi

View full text Add to dashboard Cite

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“…Research on anomaly, host-based intrusion detection has adopted either of three main approaches: system-based, specification-based, and learningbased [11]. Existing anomaly detection methods are primarily categorized as statistical anomaly detection, anomaly detection based on neural network and anomaly detection based on data mining, etc [12]. Currently many IDS are rule-based system where the performances highly rely on the rules identified by security experts.…”

Section: Introductionmentioning

confidence: 99%

Improving Information Integrity using Artificial Bee Colony based Intrusion Detection System

Khan¹,

Asger²,

Butt³

2015

IJCA

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Nowadays, Information, which is managed by Database Management System, is deemed as an asset for any of the organizations. Malicious attacks over a computer network can decrease security and trust of a system which may lead to various threats, which will be mitigated by introducing Intrusion Detection System. Emerging Intrusion detection systems (IDS) cannot improve the Integrity and in order to offer higher security for the confidential data with information integrity, an Artificial Bee Colony algorithm based Intrusion Detection system (ABC-IDS) is proposed. Clustering, Mining and Classification are the three major phases of this proposed system. The primary step is clustering the given datasets which helps to recover the quality of the datasets by partitioning the quantity in a cluster. Clustering is worked out by the clustering algorithm, Fuzzy C-Means and the second phase Mining is completed on the clustered datasets in order to get mined results by successfully mining the given datasets with the aid of Frequent Item set mining. The generated rules in the mining process get optimized by the usage of Artificial Bee Colony algorithm. After acquiring optimized mined results, the Classification phase is carried out by using Artificial Neural Network classifier, which classifies the input dataset into Intrusion or Non-Intrusion packets. This proposed method is implemented in MATLAB platform over DARPA dataset and then it is analyzed for its accuracy of Intrusion detection rate and Non-Intrusion detection rate, which will also evidently improve the consistency and reliability of the ABC-IDS system. Moreover, comparison of the proposed methodology with the state-of-art works is done to prove the improvement in information integrity in the proposed method. Hence, the optimum Integrity is increased by this research by the increase of its intrinsic attributes of accuracy, consistency and reliability.

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