Abstract
Ubiquitous nature of Internet services across the globe has undoubtedly expanded the strategies and operational mode being used by cybercriminals to perpetrate their unlawful activities through intrusion on various networks. Network intrusion has led to many global financial loses and privacy problems for Internet users across the globe. In order to safeguard the network and to prevent Internet users from being the regular victims of cyber-criminal activities, new solutions are needed. This research proposes solution for intrusion detection by using the improved hashing-based Apriori algorithm implemented on Hadoop MapReduce framework; capable of using association rules in mining algorithm for identifying and detecting network intrusions. We used the KDD dataset to evaluate the effectiveness and reliability of the solution. Our results obtained show that this approach provides a reliable and effective means of detecting network intrusion.
Highlights
The survival of any business organization depends upon the security mechanisms that adequately protect and prevent from illegal entrance into confidential data of the organization
An et al [4] proposed using a hypergraph clustering model based on the Apriori algorithm to find the association between fog computing nodes with a higher risk of threat of the distributed denial of service (DDoS) attack
We evaluate our results against state-of-the-art of other authors achieved using a variety of different methods on KDDcup99 dataset
Summary
The survival of any business organization depends upon the security mechanisms that adequately protect and prevent from illegal entrance into confidential data of the organization. The anomaly-based IDS [12] refer to a baseline or learned model of system’s normal activity, a significant deviation from this baseline is used to recognize the active intrusion attempts and trigger the alarm. Used fuzzy Weighted Association rule (WAR) based on the Apriori algorithm and genetic network programming (GNP) and for generation of rules to evaluate network misuse and anomaly detection. An et al [4] proposed using a hypergraph clustering model based on the Apriori algorithm to find the association between fog computing nodes with a higher risk of threat of the distributed denial of service (DDoS) attack. The hashing-based modification allows too find the frequency of the k-itemsets without the use of computationally expensive candidate sets, which makes it usable for detecting network attacks in near real-time, whereas the MapReduce framework allows to handle network traffic on large networks. We demonstrate the applicability of the proposed method to identify and detect network intrusions using the KDD dataset as a benchmark
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