Intrusion detection system for grid computing using SNORT

Kumar, Manish; Hanumanthappa, M.; Kumar, T. V. Suresh

doi:10.1109/iccca.2012.6179141

Cited by 16 publications

(21 citation statements)

References 9 publications

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“…Kumar, Hanumanthappa and Kumar [14] have discussed the main issues in Snort IDS when integrating with the Cloud/ Grid computing. In order to overcome these issues in the legacy Snort IDS, Kumar, Hanumanthappa and Kumar [14] introduced a new model of intrusion detection system for grid computing using Snort.…”

Section: A) Detection Through Legacy Solutionsmentioning

confidence: 99%

“…As mentioned by Kumar, Hanumanthappa and Kumar [14], those NIDSs must have scalability and distributed nature in order to be used with the cloud environment. But those three NIDSs do not focus the scalable and distributed nature of their architecture.…”

Section: A) Detection Through Legacy Solutionsmentioning

confidence: 99%

“…Further, the special requirements of IDS to use with the grid/cloud computing environment were discussed by Kumar, Hanumanthappa and Kumar [14] and the main point they emphasize is "IDS must be distributed to work in a grid computing environment. It must monitor each node and, when an attack occurs, it must alert other nodes in the environment".…”

Section: Intrusion Detection Systemmentioning

confidence: 99%

“…But those three NIDSs do not focus the scalable and distributed nature of their architecture. It has also been mentioned that those NIDSs use a decentralized approach which is also unrealistic because of the cost of maintenance and administration [14].…”

Section: A) Detection Through Legacy Solutionsmentioning

confidence: 99%

“…In order to overcome these issues in the legacy Snort IDS, Kumar, Hanumanthappa and Kumar [14] introduced a new model of intrusion detection system for grid computing using Snort. The proposed model has used a single snort IDS for the whole cluster.…”

Section: A) Detection Through Legacy Solutionsmentioning

confidence: 99%

See 4 more Smart Citations

Review on state of art intrusion detection systems designed for the cloud computing paradigm

Premathilaka

Aponso

Krishnarajah

2013

2013 47th International Carnahan Conference on Security Technology (ICCST)

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Cloud Computing is an emerging technology that enhances capability, usability and scalability of computer systems. On account of some exclusive features, cloud computing system always differs from the traditional computer system; not only the capabilities but also the vulnerabilities and threats. Intrusion Detection System (IDS) is a significant component of computer system security and compliance practices that protects computer systems from various types of malicious activities and attacks. Intrusion Detection Systems have been evolved over decades and various types of systems are currently available to identify and eradicate attacks based on different system conditions and different aptitudes.The main purpose of this paper is to review the state-of-art Intrusion Detection Systems available for cloud computing paradigm, which adhere to features of cloud computing architecture. Scalability, elasticity, reliability, performance, security and distributed nature of the Intrusion Detection Systems will be reviewed in order to identify suitable approaches for cloud computing.

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Section: A) Detection Through Legacy Solutionsmentioning

confidence: 99%

Section: A) Detection Through Legacy Solutionsmentioning

confidence: 99%

Section: Intrusion Detection Systemmentioning

confidence: 99%

Section: A) Detection Through Legacy Solutionsmentioning

confidence: 99%

Section: A) Detection Through Legacy Solutionsmentioning

confidence: 99%

See 3 more Smart Citations

Review on state of art intrusion detection systems designed for the cloud computing paradigm

Premathilaka

Aponso

Krishnarajah

2013

2013 47th International Carnahan Conference on Security Technology (ICCST)

View full text Add to dashboard Cite

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Exploring Self-attention Mechanism of Deep Learning in Cloud Intrusion Detection

Dai

Zhou

et al. 2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Cloud computing offers elastic and ubiquitous computing services, thereby receiving extensive attention recently. However, cloud servers have also become the targets of malicious attacks or hackers due to the centralization of data storage and computing facilities. Most intrusion attacks to cloud servers are often originated from inner or external networks. Intrusion detection is a prerequisite to designing anti-intrusion countermeasures of cloud systems. In this paper, we explore deep learning algorithms to design intrusion detection methods. In particular, we present a deep learning-based method with the integration of conventional neural networks, self-attention mechanism, and Long short-term memory (LSTM), namely CNN-A-LSTM to detect intrusion. CNN-A-LSTM leverages the merits of CNN in processing local correlation data and extracting features, the time feature extracting capability of LSTM, and the self-attention mechanism to better exact features. We conduct extensive experiments on the KDDcup99 dataset to evaluate the performance of our CNN-A-LSTM model. Compared with other machine learning and deep learning models, our CNN-A-LSTM has superior performance.

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From zero-shot machine learning to zero-day attack detection

Sarhan

Layeghy

Gallagher

et al. 2023

Int. J. Inf. Secur.

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Machine learning (ML) models have proved efficient in classifying data samples into their respective categories. The standard ML evaluation methodology assumes that test data samples are derived from pre-observed classes used in the training phase. However, in applications such as Network Intrusion Detection Systems (NIDSs), obtaining data samples of all attack classes to be observed is challenging. ML-based NIDSs face new attack traffic known as zero-day attacks that are not used in training due to their non-existence at the time. Therefore, this paper proposes a novel zero-shot learning methodology to evaluate the performance of ML-based NIDSs in recognising zero-day attack scenarios. In the attribute learning stage, the learning models map network data features to semantic attributes that distinguish between known attacks and benign behaviour. In the inference stage, the models construct the relationships between known and zero-day attacks to detect them as malicious. A new evaluation metric is defined as Zero-day Detection Rate (Z-DR) to measure the effectiveness of the learning model in detecting unknown attacks. The proposed framework is evaluated using two key ML models and two modern NIDS data sets. The results demonstrate that for certain zero-day attack groups discovered in this paper, ML-based NIDSs are ineffective in detecting them as malicious. Further analysis shows that attacks with a low Z-DR have a significantly distinct feature distribution and a higher Wasserstein Distance range than the other attack classes.

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Intrusion detection system for grid computing using SNORT

Cited by 16 publications

References 9 publications

Review on state of art intrusion detection systems designed for the cloud computing paradigm

Review on state of art intrusion detection systems designed for the cloud computing paradigm

Exploring Self-attention Mechanism of Deep Learning in Cloud Intrusion Detection

From zero-shot machine learning to zero-day attack detection

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