A Study On Various Cyber Attacks And A Proposed Intelligent System For Monitoring Such Attacks

Choudhary, Atul S.; Choudhary, Pankaj P; Salve, Shrikant

doi:10.1109/icict43934.2018.9034445

Cited by 11 publications

(5 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The key benefits of utilizing the ML method described in this study include the following: the ability to emit criminal strategies, extract complex data relationships, produce results that are impossible for humans to predict, and identify models in both unstructured and structured data. [25,26].…”

Section: Figure 1 the Datasets Use A Variety Of Approaches To Generat...mentioning

confidence: 99%

Analyse and Predict the Detection of the Cyber - Attack Process by Using a Machine-Learning Approach

Singh,

Shivaputra

et al. 2024

EAI Endorsed Trans IoT

View full text Add to dashboard Cite

Crimes committed online rank among the most critical global concerns. Daily, they cause country and citizen economies to suffer massive financial losses. With the proliferation of cyber-attacks, cybercrime has also been on the rise. To effectively combat cybercrime, it is essential to identify its perpetrators and understand their methods. Identifying and preventing cyber-attacks are difficult tasks. To combat these concerns, however, new research has produced safety models and forecast tools grounded on artificial intelligence. Numerous methods for predicting criminal behaviour are available in the literature. While they may not be perfect, they may help in cybercrime and cyber-attack tactic prediction. To find out whether an attack happened and, if so, who was responsible, one way to look at this problem is by using real-world data. There is data about the crime, the perpetrator's demographics, the amount of property damaged, and the entry points for the assault. Potentially, by submitting applications to forensics teams, victims of cyber-attacks may get information. This study uses ML methods to analyse cyber-crime consuming two patterns and to forecast how the specified characteristics will furnish to the detection of the cyber-attack methodology and perpetrator. Based on the comparison of eight distinct machine-learning methods, one can say that their accuracy was quite comparable. The Support Vector Machine (SVM) Linear outperformed all other cyber-attack tactics in terms of accuracy. The initial model gave us a decent notion of the assaults that the victims would face. The most successful technique for detecting malevolent actors was logistic regression, according to the success rate. To anticipate who the perpetrator and victim would be, the second model compared their traits. A person’s chances of being a victim of a cyber-attack decrease as their income and level of education rise. The proposed idea is expected to be used by departments dealing with cybercrime. Cyber-attack identification will also be made easier, and the fight against them will be more efficient.

show abstract

Section: Figure 1 the Datasets Use A Variety Of Approaches To Generat...mentioning

confidence: 99%

Analyse and Predict the Detection of the Cyber - Attack Process by Using a Machine-Learning Approach

Singh,

Shivaputra

et al. 2024

EAI Endorsed Trans IoT

View full text Add to dashboard Cite

show abstract

“…NSL_KDD [26], UNSW_NB15 [27] datasets consist of a large number of packets; the first one contains four major attacks, while the second has two million packets and contains nine major attacks. The features of these datasets include TCP/IP header information of TCP/IP suite.…”

Section: Data Sets Descriptionmentioning

confidence: 99%

“…True Negative (TN) is the number of cases successfully forecasted as Normal class in cyber security, while True Positive (TP) is the number of instances correctly predicted as attacks, The number of normal occurrences identified as an attack is known as False Positive (FP), while the number of attack instances classified as normal is known as False Negative (FN). To evaluate the proposed system, standard metrics are used, such as measures like accuracy, precision, recall, and F1 score, while the Silhouette Index measure is presented to evaluate the quality of clusters [27].…”

Section: Evaluation Metricsmentioning

confidence: 99%

Build Network Intrusion Detection System based on combination of Fractal Density Peak Clustering and Artificial Neural Network

Alkafagi

2023

J. Al-Qadisiyah Comp. Sci. Math.

View full text Add to dashboard Cite

Imbalanced data poses a serious problem in intrusion detection systems. In this article, we propose a network intrusion detection system based on fractal density peak clustering and an artificial neural network (FD-ANN). The proposed detection system consists of three parts: data clustering based on the density-peak clustering (DPC) method, using the fractal concept as a membership weight of all data to the cluster, and a neural network to classify the data. The DPC method uses categorization of the tare data into subgroups with strongly correlated attributes to reduce the size of the training data and the imbalance of the sample. Each subgroup has its neural network to train the data. Based on fractal membership weights, the output of all classifiers of the sub-neural networks is combined using the aggregation function. The benchmarks of this model are based on the data sets NSL-KDD and UNSW-NB15. The proposed solution outperforms other known classification approaches in terms of overall accuracy, recall, precision, and F1 score.

show abstract

“…Classification [7][8][9], regression [10], and clustering [4] models have been used to improve the performance and increase the detection rate of intrusion detection systems (IDS). Intrusion data is knowns as stream data with large amounts of data generated at high speed in networking [7].…”

Section: Motivationmentioning

confidence: 99%

“…To overcome these challenges, all evolving methods to add, merge, and delete clusters should be implemented [2,3]. At the same time, the system should learn and adapt to unknown situations and detect potential temporal shifts and data drifts [4]. Cauchy density assigns new samples to clusters or forms new clusters.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Evolving Cauchy Possibilistic Clustering Based on the Self-Similarity Principle (DECS) for Enhancing Intrusion Detection System

2022

IJIES

View full text Add to dashboard Cite

Unsupervised machine learning plays a critical role in improving the security level of applications and systems. The cyberattack floods the network with data streams to deny services or destroy the network infrastructure. In this paper, a new development strategy (dynamic evolving cauchy possibilistic clustering based on the self-similarity principle (DECS)) is proposed to optimize the data stream clustering model based on the self-similarity principles (inter-cluster and intra-cluster). It is based on computing the self-similarity principles for data between and within clusters. The proposed system mitigates the dependence on the centre of the cluster to produce clusters with highly correlated data and minimal errors within the same cluster. The DECS consists of two phases: In the first phase, the data are clustered based on density points to generate clusters, and in the second phase, the clusters are evolved according to the inter-and intra-distance points to/in the clusters. The proposed model is tested on well-known intrusion datasets such as UNSW-NB15, KDD99 and NSL-KDD. In the experiments, DECS efficiently clustered the data with fewer errors and an optimal number of clusters compared to other models, it has achieved minimum error, high silhouette coefficient and an optimal number of clusters. The implementation results show that the average error of DECS over eight datasets is (0.45) with an average silhouette coefficient (0.63). At the same time, the pure cauchy possibilistic clustering obtains on average error (0.69) and silhouette coefficient (0.44).

show abstract

A Study On Various Cyber Attacks And A Proposed Intelligent System For Monitoring Such Attacks

Cited by 11 publications

References 4 publications

Analyse and Predict the Detection of the Cyber - Attack Process by Using a Machine-Learning Approach

Analyse and Predict the Detection of the Cyber - Attack Process by Using a Machine-Learning Approach

Build Network Intrusion Detection System based on combination of Fractal Density Peak Clustering and Artificial Neural Network

Dynamic Evolving Cauchy Possibilistic Clustering Based on the Self-Similarity Principle (DECS) for Enhancing Intrusion Detection System

Contact Info

Product

Resources

About