Undesirable Types – The Surveillance of Journalists

Anderson, Fay

doi:10.2307/j.ctt22rbjhf.6

Cited by 5 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…NIDS has caught the scientific community's attention as a vital tool for guaranteeing network security since its inception by Anderson [15] in 1980. As a classification problem, NIDS can be categorized into three (3) different methodological concepts (i.e., Anomaly, Misuse, and hybridized detection).…”

Section: Related Workmentioning

confidence: 99%

CNN-GRU-FF: a double-layer feature fusion-based network intrusion detection system using convolutional neural network and gated recurrent units

Imrana,

Xiang,

Ali

et al. 2024

Complex Intell. Syst.

View full text Add to dashboard Cite

Identifying and preventing malicious network behavior is a challenge for establishing a secure network communication environment or system. Malicious activities in a network system can seriously threaten users’ privacy and potentially jeopardize the entire network infrastructure and functions. Furthermore, cyber-attacks have grown in complexity and number due to the ever-evolving digital landscape of computer and network devices in recent years. Analyzing network traffic using network intrusion detection systems (NIDSs) has become an integral security measure in modern networks to identify malicious and suspicious activities. However, most intrusion detection datasets contain imbalance classes, making it difficult for most existing classifiers to achieve good performance. In this paper, we propose a double-layer feature extraction and feature fusion technique (CNN-GRU-FF), which uses a modified focal loss function instead of the traditional cross-entropy to handle the class imbalance problem in the IDS datasets. We use the NSL-KDD and UNSW-NB15 datasets to evaluate the effectiveness of the proposed model. From the research findings, it is evident our CNN-GRU-FF method obtains a detection rate of 98.22% and 99.68% using the UNSW-NB15 and NSL-KDD datasets, respectively while maintaining low false alarm rates on both datasets. We compared the proposed model’s performance with seven baseline algorithms and other published methods in literature. It is evident from the performance results that our proposed method outperforms the state-of-the-art network intrusion detection methods.

show abstract

Section: Related Workmentioning

confidence: 99%

CNN-GRU-FF: a double-layer feature fusion-based network intrusion detection system using convolutional neural network and gated recurrent units

Imrana,

Xiang,

Ali

et al. 2024

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…For example, Eugene [62] claimed that profiling user behaviour for intrusion detection originated from Anderson [35], who introduces the idea of using an automated surveillance system that looks for profiles such as session logs, durations, program usage, device usage, and file usage. Anderson categorises users into three different user groups, namely: legitimate, masquerade, and clandestine.…”

Section: System Profilesmentioning

confidence: 99%

“…Due to this merit, there have been more attempts recently on this area such as analysis of the difference, in terms of speed or accuracy, between using a singular profile and combining multiple profiles. For example, Anderson [35] combines session logs, device usage, file usage durations and program usage to indirectly profile user behaviours; Denning [18] detects anomalies by extracting user logins, CPU usage and I/O usage. Others combine different user behaviours directly for anomaly detections.…”

Section: Combining Profiles In Idsmentioning

confidence: 99%

“…Many detection analysis methods are used for detecting anomaly behaviours, some use statistical approaches [18,35,37,45,54,61,63,70,87,[89][90][91][92][93][94][95], artificial neural networks [39,40,87], hidden Markov models [20,41,96], rule learning [65,66,85], decision trees [12,39,42,43,45,48], support vector machines [12,39,83,84,88,97,98] and fuzzy computing [45,99]. In Table 3, we summarised the analysis techniques and their advantages and disadvantages.…”

Section: Conclusion and Future Opportunitiesmentioning

confidence: 99%

“…There is a variety of techniques implementing these decisions in anomaly-based intrusion detection systems, such as statistical approaches [35][36][37][38], artificial neural networks [7,39,40], hidden Markov models [41], rule learning [18,42], decision trees [43], support vector and machines [12,17,44]. Each has its merits and drawbacks, for example, artificial neural networks are similar to the statistical approach to some extent, but artificial neural networks is easier to represent nonlinear relationships between inputs and outputs regardless of distortions and incompleteness of data.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

User profiling in intrusion detection: A review

Peng

Choo

Ashman

2016

Journal of Network and Computer Applications

106

View full text Add to dashboard Cite

Handling Intrusion Detection System using Snort Based Statistical Algorithm and Semi-supervised Approach

Nadiammai¹,

Hemalatha²

2013

RJASET

View full text Add to dashboard Cite

Intrusion detection system aims at analyzing the severity of network in terms of attack or normal one. Due to the advancement in computer field, there are numerous number of threat exploits attack over huge network. Attack rate increases gradually as detection rate increase. The main goal of using data mining within intrusion detection is to reduce the false alarm rate and to improve the detection rate too. Machine learning algorithms accomplishes to solve the detection problem. In this study, first we analyzed the statistical based anomaly methods such as ALAD, LEARAD and PHAD. Then a new approach is proposed for hybrid intrusion detection. Secondly, the advantage of both supervised and unsupervised has been used to develop a semi-supervised method. Our experimental method is done with the help of KDD Cup 99 dataset. The proposed hybrid IDS detects 149 attacks (nearly 83%) out of 180 attacks by training in one week attack free data. Finally, the proposed semi-supervised approach shows 98.88% accuracy and false alarm rate of 0.5533% after training on 2500 data instances.

show abstract

Undesirable Types – The Surveillance of Journalists

Cited by 5 publications

References 0 publications

CNN-GRU-FF: a double-layer feature fusion-based network intrusion detection system using convolutional neural network and gated recurrent units

CNN-GRU-FF: a double-layer feature fusion-based network intrusion detection system using convolutional neural network and gated recurrent units

User profiling in intrusion detection: A review

Handling Intrusion Detection System using Snort Based Statistical Algorithm and Semi-supervised Approach

Contact Info

Product

Resources

About