Network Intrusion Detection with a Hashing Based Apriori Algorithm Using Hadoop MapReduce

Azeez, Nureni Ayofe; Ayemobola, Tolulope Jide; Misra, Sanjay; Maskeliūnas, Rytis; Damaševičius, Robertas

doi:10.3390/computers8040086

Cited by 39 publications

(17 citation statements)

References 53 publications

(52 reference statements)

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“…The Apriori algorithm is one of the algorithms that is widely used in the discovery of association rules. The Apriori algorithm is considered to be the best known algorithm for mining (Liu et al, 2017) association rules used to find a common set of items (Azeez et al, 2019). This algorithm is also successfully applied in many fields such as, hypothesis, numerical investigation, big data processing, etc.…”

Section: Apriori Algorithmmentioning

confidence: 99%

“…(Junianto & Rachman, 2020). The pseudocode of the algorithm is summarized in Algorithm 2 (Azeez et al, 2019). D. RapidMiner Today, there are various well-known general purpose tools and frameworks that can be used for data mining purposes such as: Rapidminer, Weka, SPSS, Knime, Orange, Spark Lib, and so on.…”

Section: Fig 1 Association Rule Mining Process Flow Chartmentioning

confidence: 99%

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Application of Association Rule Method Using Apriori Algorithm to Find Sales Patterns Case Study of Indomaret Tanjung Anom

Santoso

2021

Brilliance

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Data mining can generally be defined as a technique for finding patterns (extraction) or interesting information in large amounts of data that have meaning for decision support. One of the well-known and commonly used association rule discovery data mining methods is the Apriori algorithm. The Association Rule and the Apriori Algorithm are two very prominent algorithms for finding a number of frequently occurring sets of items from transaction data stored in databases. The calculation is done to determine the minimum value of support and minimum confidence that will produce the association rule. The association rule is used to produce the percentage of purchasing activity for an itemset within a certain period of time using the RapidMiner software. The results of the test using the priori algorithm method show that the association rule, that customers often buy toothpaste and detergents that have met the minimum confidence value. By searching for patterns using this a priori algorithm, it is hoped that the resulting information can improve further sales strategies.

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Section: Apriori Algorithmmentioning

confidence: 99%

Section: Fig 1 Association Rule Mining Process Flow Chartmentioning

confidence: 99%

Application of Association Rule Method Using Apriori Algorithm to Find Sales Patterns Case Study of Indomaret Tanjung Anom

Santoso

2021

Brilliance

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“…Any transport, application, or protocol such as Internet Control Message Protocol (ICMP), File transfer protocol (FTP), User Datagram Protocol (UDP), Simple Mail Transfer Protocol (SMTP), Transmission Control Protocol (TCP), Hypertext Transfer Protocol (HTTP), etc., can be used to execute these attacks. Network-based Intrusion Detection Systems (NIDS) are suggested to be used to handle such attacks to scan the network and to identify such attacks [23,24].…”

Section: Introductionmentioning

confidence: 99%

Botnet Attack Detection Using Local Global Best Bat Algorithm for Industrial Internet of Things

et al. 2021

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The need for timely identification of Distributed Denial-of-Service (DDoS) attacks in the Internet of Things (IoT) has become critical in minimizing security risks as the number of IoT devices deployed rapidly grows globally and the volume of such attacks rises to unprecedented levels. Instant detection facilitates network security by speeding up warning and disconnection from the network of infected IoT devices, thereby preventing the botnet from propagating and thereby stopping additional attacks. Several methods have been developed for detecting botnet attacks, such as Swarm Intelligence (SI) and Evolutionary Computing (EC)-based algorithms. In this study, we propose a Local-Global best Bat Algorithm for Neural Networks (LGBA-NN) to select both feature subsets and hyperparameters for efficient detection of botnet attacks, inferred from 9 commercial IoT devices infected by two botnets: Gafgyt and Mirai. The proposed Bat Algorithm (BA) adopted the local-global best-based inertia weight to update the bat’s velocity in the swarm. To tackle with swarm diversity of BA, we proposed Gaussian distribution used in the population initialization. Furthermore, the local search mechanism was followed by the Gaussian density function and local-global best function to achieve better exploration during each generation. Enhanced BA was further employed for neural network hyperparameter tuning and weight optimization to classify ten different botnet attacks with an additional one benign target class. The proposed LGBA-NN algorithm was tested on an N-BaIoT data set with extensive real traffic data with benign and malicious target classes. The performance of LGBA-NN was compared with several recent advanced approaches such as weight optimization using Particle Swarm Optimization (PSO-NN) and BA-NN. The experimental results revealed the superiority of LGBA-NN with 90% accuracy over other variants, i.e., BA-NN (85.5% accuracy) and PSO-NN (85.2% accuracy) in multi-class botnet attack detection.

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“…U2R or a User to Root Attack, the hacker uses certain privileges as a normal user, then, later on, tries to exploit the vulnerabilities of the system to gain super user benefits that is, it is the act of illegitimately procuring the root's privileges while accessing a local machine legally. Even though various research studies established for intrusion detection, 2,3 but still the amount of new kind of attacks are increasing day by day. Such attacks cause severe damage in IoT smart environments, 4 wireless networks, 5 cyber‐physical systems, 6 and fog computing 7 .…”

Section: Introductionmentioning

confidence: 99%

An intrusion detection system using optimized deep neural network architecture

Ramaiah

Vanmathi

Ravi

et al. 2021

Trans Emerging Tel Tech

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Internet usage became increasingly ubiquitous. The concern regarding security and privacy has become essential for Internet users. As the usage of the Internet increases the number of cyber‐attacks also increases substantially. Intrusion detection is one of the challenging aspects of network security. Efficient intrusion detection is crucial for every organization to mitigate the vulnerability. This paper presents a novel intrusion detection system to detect malicious attacks targeted at a smart environment. The proposed Intrusion detection method uses a correlation tool and a random forest method to detect the predominant independent variables for improvising neural‐based attack classifier. To detect a malicious attack, a shallow neural network and an optimized neural‐based classifier are presented. The designed intrusion detection system has experimented on the KDDCUP99 dataset. The experimental results reveal that the performance of the proposed intrusion detection system is superior in terms of quantitative metrics. Thus, the proposed system can be deployed in the IoT and wireless networks to detect cyber‐attacks.

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Network Intrusion Detection with a Hashing Based Apriori Algorithm Using Hadoop MapReduce

Cited by 39 publications

References 53 publications

Application of Association Rule Method Using Apriori Algorithm to Find Sales Patterns Case Study of Indomaret Tanjung Anom

Application of Association Rule Method Using Apriori Algorithm to Find Sales Patterns Case Study of Indomaret Tanjung Anom

Botnet Attack Detection Using Local Global Best Bat Algorithm for Industrial Internet of Things

An intrusion detection system using optimized deep neural network architecture

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