Layered Approach Using Conditional Random Fields for Intrusion Detection

Gupta, Kapil Kumar; Nath, Baikunth; Kotagiri, Rao

doi:10.1109/tdsc.2008.20

Cited by 120 publications

(78 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For our results, we give the Precision, Recall, and F-Value as of [18] and not the accuracy alone as with the given data set, it is easy to achieve very high accuracy by carefully selecting the sample size. The Precision, Recall, and F-Value are defined as follows:…”

Section: Results For Static Datamentioning

confidence: 99%

“…The determination of training and testing set are discussed in Section 3.2. As indicated by Gupta et.al [18] selected features may be useful for detecting certain type of attack. Thus we compare our results with the selected features as well.…”

Section: Results For Static Datamentioning

confidence: 99%

“…Very Recently Gupta et al [18] proposed an intrusion detection system using conditional random fields (CRF) and Layered approach. They considered the attack categories as layers and different features were selected for each layer.…”

Section: Related Workmentioning

confidence: 99%

“…The main purpose of an IDS is to detect as many attacks as possible with minimum number of false alarms, i.e., the system must be accurate in detecting attacks. However, an accurate system that cannot handle large amount of network traffic and is slow in decision making will not fulfill the purpose of an intrusion detection system [18]. Hence it is necessary to develop a system that detects most of the attacks, gives very few false alarms, copes with large amount of data, and is fast enough to make real-time decisions.…”

Section: Introductionmentioning

confidence: 99%

“…Although the IDS has led to a number of valuable network security techniques [3,4,14,15,16,17,18,33,40,41,42], the existing solutions are limited only to static data release.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A novel statistical technique for intrusion detection systems

Kabir

Wang

et al. 2018

Future Generation Computer Systems

186

View full text Add to dashboard Cite

This paper proposes a novel approach for intrusion detection system based on sampling with Least Square Support Vector Machine (LS-SVM). Decision making is performed in two stages. In the first stage, the whole dataset is divided into some predetermined arbitrary subgroups. The proposed algorithm selects representative samples from these subgroups such that the samples reflect the entire dataset. An optimum allocation scheme is developed based on the variability of the observations within the subgroups. In the second stage, least square support vector machine (LS-SVM) is applied to the extracted samples to detect intrusions. We call the proposed algorithm as optimum allocation-based least square support vector machine (OA-LS-SVM) for IDS. To demonstrate the effectiveness of the proposed method, the experiments are carried out on KDD 99 database which is considered a de facto benchmark for evaluating the performance of intrusions detection algorithm. All binary-classes and multiclass are tested and our proposed approach obtains a realistic performance in terms of accuracy and efficiency. Finally a way out is also shown the usability of the proposed algorithm for incremental datasets.

show abstract

Section: Results For Static Datamentioning

confidence: 99%

Section: Results For Static Datamentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Although the IDS has led to a number of valuable network security techniques [3,4,14,15,16,17,18,33,40,41,42], the existing solutions are limited only to static data release.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A novel statistical technique for intrusion detection systems

Kabir

Wang

et al. 2018

Future Generation Computer Systems

186

View full text Add to dashboard Cite

show abstract

A taxonomy and survey on autonomic management of applications in grid computing environments

Rahman

Ranjan

Buyya

et al. 2011

Concurrency and Computation

View full text Add to dashboard Cite

SUMMARYIn Grid computing environments, the availability, performance, and state of resources, applications, services, and data undergo continuous changes during the life cycle of an application. Uncertainty is a fact in Grid environments, which is triggered by multiple factors, including: (1) failures, (2) dynamism, (3) incomplete global knowledge, and (4) heterogeneity. Unfortunately, the existing Grid management methods, tools, and application composition techniques are inadequate to handle these resource, application and environment behaviors. The aforementioned characteristics impose serious requirements on the Grid programming and runtime systems if they wish to deliver efficient performance to scientific and commercial applications. To overcome the above challenges, the Grid programming and runtime systems must become autonomic or self-managing in accordance with the high-level behavior specified by system administrators. Autonomic systems are inspired by biological systems that deal with similar challenges of complexity, dynamism, heterogeneity, and uncertainty. To this end, we propose a comprehensive taxonomy that characterizes and classifies different software components and highlevel methods that are required for autonomic management of applications in Grids. We also survey several representative Grid computing systems that have been developed by various leading research groups in the academia and industry. The taxonomy not only highlights the similarities and differences of state-of-the-art technologies utilized in autonomic application management from the perspective of Grid computing, but also identifies the areas that require further research initiatives. We believe that this taxonomy and its mapping to relevant systems would be highly useful for academic-and industry-based researchers, who are engaged in the design of Autonomic Grid and more recently, Cloud computing systems.

show abstract

Real‐time anomaly detection using parallelized intrusion detection architecture for streaming data

Chellammal¹,

D²

2018

Concurrency and Computation

View full text Add to dashboard Cite

SummaryHigh usage levels of networking technologies has resulted in large amounts of data being generated. This in‐turn has lured several fraudsters, whose anomalous behaviors create undesired consequences to legitimate users. This paper proposes an Adaptive Parallelized Intrusion Detection (APID) architecture to handle the hugeness and data imbalance associated with streaming data. The architecture is composed of a feature selection strategy to reduce data size, an effective data segregation mechanism to handle data imbalance and a heterogeneous ensemble and a heuristic combiner mechanism to provide effective predictions. Adaptivity is incorporated by the reinforcement mechanism that retrains the model based on false predictions given by the model. The proposed APID architecture is generic; hence, it supports heterogeneous models and can also incorporate any number of machine learning models. Hence, it becomes flexible to adapt the model to data pertaining to any domain. Experiments were performed with KDD CUP 99, NSL‐KDD, and Koyoto 2006 datasets. Comparisons performed with recent works in literature indicates anomaly detection rates between 98% to 99% exhibiting the effectiveness of the proposed model.

show abstract

Layered Approach Using Conditional Random Fields for Intrusion Detection

Cited by 120 publications

References 20 publications

A novel statistical technique for intrusion detection systems

A novel statistical technique for intrusion detection systems

A taxonomy and survey on autonomic management of applications in grid computing environments

Real‐time anomaly detection using parallelized intrusion detection architecture for streaming data

Contact Info

Product

Resources

About