Empirical Analysis of Data Streaming and Batch Learning Models for Network Intrusion Detection

Adewole, Kayode S.; Salau-Ibrahim, Taofeekat T.; Imoize, Agbotiname Lucky; Oladipo, Idowu Dauda; AbdulRaheem, Muyideen; Awotunde, Joseph Bamidele; Balogun, Abdullateef Oluwagbemiga; Isiaka, Rafiu Mope; Aro, Taye Oladele

doi:10.3390/electronics11193109

Cited by 7 publications

(4 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, in both situations, a minor change in the data might result in a significant change in the structure. A novel crime detection approach known as naive Bayes (NB) is used for crime prediction and analysis [32]- [34]. Comes [11] only had an accuracy rate of 66% in predicting crimes and did not take into account computing speed, resilience, or scalability which are also important.…”

Section: Crime Prediction Approaches (Cv ML Dl)mentioning

confidence: 99%

Techniques for predicting dark web events focused on the delivery of illicit products and ordered crime

Rawat

Oki

Sankaran

et al. 2023

IJECE

View full text Add to dashboard Cite

Malicious actors, specially trained professionals operating anonymously on the dark web (DW) platform to conduct cyber fraud, illegal drug supply, online kidnapping orders, CryptoLocker induction, contract hacking, terrorist recruitment portals on the online social network (OSN) platform, and financing are always a possibility in the hyperspace. The amount and variety of unlawful actions are increasing, which has prompted law enforcement (LE) agencies to develop efficient prevention tactics. In the current atmosphere of rapidly expanding cybercrime, conventional crime-solving methods are unable to produce results due to their slowness and inefficiency. The methods for accurately predicting crime before it happens "automated machine" to help police officers ease the burden on personnel while also assisting in preventing offense. To achieve and explain the results of a few cases in which such approaches were applied, we advise combining machine learning (ML) with computer vision (CV) strategies. This study's objective is to present dark web crime statistics and a forecasting model for generating alerts of illegal operations like drug supply, people smuggling, terrorist staffing and radicalization, and deceitful activities that are connected to gangs or organizations showing online presence using ML and CV to help law enforcement organizations identify, and accumulate proactive tactics for solving crimes.

show abstract

Section: Crime Prediction Approaches (Cv ML Dl)mentioning

confidence: 99%

Techniques for predicting dark web events focused on the delivery of illicit products and ordered crime

Rawat

Oki

Sankaran

et al. 2023

IJECE

View full text Add to dashboard Cite

show abstract

“…The authors of [11] conduct a comparison study between batch data mining algorithms (decision tree (J48) and projective adaptive resonance theory (PART)) and stream data mining algorithms (Hoeffding tree (HT) and OzaBagAdwin (OBA)). The dataset UNSW-NB15, which includes examples of normal activity and nine different kinds of intrusion network attacks, is used.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, these models are facing the ransomware attacks' rapid evolution. In addition to maintaining high accuracy levels, stream data mining techniques [10,11] can minimize model latency.…”

Section: Related Workmentioning

confidence: 99%

Proactive Ransomware Detection Using Extremely Fast Decision Tree (EFDT) Algorithm: A Case Study

Ba’abbad

Batarfi

2023

Computers

View full text Add to dashboard Cite

Several malware variants have attacked systems and data over time. Ransomware is among the most harmful malware since it causes huge losses. In order to get a ransom, ransomware is software that locks the victim’s machine or encrypts his personal information. Numerous research has been conducted to stop and quickly recognize ransomware attacks. For proactive forecasting, artificial intelligence (AI) techniques are used. Traditional machine learning/deep learning (ML/DL) techniques, however, take a lot of time and decrease the accuracy and latency performance of network monitoring. In this study, we utilized the Hoeffding trees classifier as one of the stream data mining classification techniques to detect and prevent ransomware attacks. Three Hoeffding trees classifier algorithms are selected to be applied to the Resilient Information Systems Security (RISS) research group dataset. After configuration, Massive Online Analysis (MOA) software is utilized as a testing framework. The results of Hoeffding tree classifier algorithms are then assessed to choose the enhanced model with the highest accuracy and latency performance. In conclusion, the 99.41% classification accuracy was the highest result achieved by the EFDT algorithm in 66 ms.

show abstract

“…Therefore, Incremental learning approaches [10], [11] have gained significant attention in the field of intrusion detection due to their ability to learn and adapt continuously without the need for a complete and ready-to-go dataset [12]. These approaches allow models to be trained and launched with limited initial data and can continue to learn and update as new data becomes available.…”

mentioning

confidence: 99%

An Incremental Majority Voting Approach for Intrusion Detection System Based on Machine Learning

Abdulboriy,

Shin

2024

IEEE Access

View full text Add to dashboard Cite

With the rapid growth of digitalization and the increasing volume of data, the cybersecurity threat landscape is expanding at an alarming rate. Intrusion Detection Systems (IDS) have been widely employed in conjunction with firewalls to safeguard networks. However, traditional IDS systems operate in a static manner, rendering them vulnerable to obsolescence and necessitating costly retraining efforts. As a result, the demand for dynamic models capable of handling continuous streams of network traffic has surged as they can learn from the incoming traffic without the need to old data and costly retraining. In response to this challenge, we have implemented an enhanced approach: an incremental majority voting IDS system, which utilizes existing tools and techniques to improve the robustness and adaptability of intrusion detection By leveraging the collective decision-making power of multiple machine learning models such as: KNN classifier, Softmax Regressor and Adaptive Random Forest classifier, our system aims to improve the accuracy, especially reducing false alarm rates, and effectiveness of intrusion detection in real-time scenarios. Through this research, we have managed to obtain a model which scores 96.43% of accuracy as well as giving 100% precision for majority type of attacks. By successfully handling the imbalanced nature of streaming data, our adopted model shows promising potential as a high-performing solution for IDS and can be considered one of the robust IDS models capable of dealing with real-world imbalanced datasets.

show abstract

Empirical Analysis of Data Streaming and Batch Learning Models for Network Intrusion Detection

Cited by 7 publications

References 45 publications

Techniques for predicting dark web events focused on the delivery of illicit products and ordered crime

Techniques for predicting dark web events focused on the delivery of illicit products and ordered crime

Proactive Ransomware Detection Using Extremely Fast Decision Tree (EFDT) Algorithm: A Case Study

An Incremental Majority Voting Approach for Intrusion Detection System Based on Machine Learning

Contact Info

Product

Resources

About