DeepAnom: An Ensemble Deep Framework for Anomaly Detection in System Processes

Ezeme, Okwudili M.; Lescisin, Michael; Mahmoud, Qusay H.; Azim, Akramul

doi:10.1007/978-3-030-18305-9_58

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…Therefore, their methods cannot handle a previously unseen system call without reconstructing the whole model. In [26], [27], the authors implemented an ensemble framework based on the encoder-decoder model using attention layer to do anomaly detection in both the event and temporal stream of the system call properties. This work uses similar principles as our work but differs in architecture.…”

Section: Related Workmentioning

confidence: 99%

“…This work uses similar principles as our work but differs in architecture. Furthermore, [26], [27] algorithms' predictions could suffer from cumulative errors as a result of the use of anomalous sub-sequence in the input samples. The authors of [28] propose a deep transfer network (DTN) that uses transfer learning to leverage the knowledge gained from rich labeled data in the source domain to facilitate diagnosing a new but similar target task.…”

Section: Related Workmentioning

confidence: 99%

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Design and Development of AD-CGAN: Conditional Generative Adversarial Networks for Anomaly Detection

2020

Self Cite

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Whether in the realm of software or hardware, datasets representing the state of systems are mostly imbalanced. This imbalance is because these systems' reliability requirements make the occurrence of an anomaly a rare phenomenon. Hence, most datasets on anomaly detection have a relatively small percentage that captures the anomaly. Recently, generative adversarial networks (GAN) have shown promising results in image generation tasks. Therefore, in this research work, we build on conditional GANs (CGAN) to generate plausible distributions of a given profile to solve the challenge of data imbalance in anomaly detection tasks and present a novel framework for anomaly detection. Firstly, we learn the pattern of the minority class data samples using a single class CGAN. Secondly, we use the knowledge base of the single class CGAN to generate samples that augment the minority class samples so that a binary class CGAN can train on the typical and malicious profiles with a balanced dataset. This approach inherently eliminates the bias imposed on algorithms from the dataset and results in a robust framework with improved generalization. Thirdly, the binary class CGAN generates a knowledge base that we use to construct the cluster-based anomaly detector. During testing, we do not use the single class CGAN, thereby providing us with a lean and efficient algorithm for anomaly detection that can do anomaly detection on semisupervised and non-parametric multivariate data. We test the framework on logs and image-based anomaly detection datasets with class imbalance. We compare the performance of AD-CGAN with GAN-derived and non-GAN-derived state of the art algorithms on benchmark datasets. AD-CGAN outperforms most of the algorithms in the standard metrics of Precision, Recall, and F-1 Score. Where AD-CGAN does not perform better in the parameters used, it has the advantage of being lightweight. Therefore, it can be deployed for both online and offline anomaly detection tasks since it does not use an input sample inversion strategy.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Design and Development of AD-CGAN: Conditional Generative Adversarial Networks for Anomaly Detection

2020

Self Cite

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“…Bu veri kümeleri, gömülü sistem verisi içeren erişime açık veri kümeleri oldukları için birçok araştırmada kullanılmışlardır ve bu veri kümelerinin ortak özellği ağ tabanlı gömülü sistemlere ait veri kümleri olmalarıdır. Bu çalışmada, ana bilgisayar tabanlı (host-based) Ezeme ve arkadaşları tarafından toplanan hava aracına ait sistem çağrıları, zaman bilgisi ve sistem çağrılarına ait farklı özellikleri içeren veri seti kullanılmıştır [13].…”

Section: Introductionunclassified

“…Ezeme ve arkadaşları, anormallik tespit tekniklerinden kümelenme tekniğini insansız hava aracı gömülü sistem çekirdek durumlarındaki anormallik tespiti için kullanmışlardır [14]. Kümelemeye dayalı tekniklerin ana fikri, verilen verilerden aykırı değerleri tespit etmek için standart kümeleme tekniklerinin uygulanmasıdır.…”

Section: Introductionunclassified

Time Series Anomaly Detection Embedded Systems By Using LSTM

Akkuzu Kaya,

Yıldız

2023

IJMSIT

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Özet -İnsansız Hava Araçları (İHA) için anomali tespiti önemli bir araştırma alanı olmuştur. Anormallikleri tespit etme tekniklerinden biri, geleneksel Makine Öğrenimi (ML) algoritmalarını uygulamaktır, ancak geleneksel ML yaklaşımları, özellikle uzun vadeli bağımlı noktalardaki anormallikleri tespit edemez. Bu çalışma, İHA sistem çağrılarının zaman serisindeki anormallikleri tespit etmek için Uzun Kısa Süreli Bellek (LSTM) yöntemini kullanır. Bunu yapmak için, LSTM ağı, bir İHA sistemindeki olayların zaman aralıklarındaki verilerin uzun vadeli bağımlılıklarını öğrenmek için birbiriyle çalışan birden fazla LSTM hücresinden oluşur. Bu makalede kullanılan veri seti, sistem çağrılarının sırasını ve türünü, sistem çağrısı olaylarının zaman damgalarını, işlem kimliklerini ve isteğe bağlı argümanları içeren bir İHA'dan sistem çağrısı olaylarından toplanmıştır. LSTM tekniği ile derinlemesine modern bir siber tehdit analizi sağlamayı amaçladığımız için veri seti bu çalışmanın amacına uygun bir veri setidir. Deneysel sonuçlar, LSTM tekniğinin sistem çağrılarının zaman serisindeki anormallikleri tespit etmedeki üstün performansını kanıtlıyor.

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A Systematic Analysis of Enhancing Cyber Security Using Deep Learning for Cyber Physical Systems

Gaba,

Budhiraja,

Kumar

et al. 2024

IEEE Access

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In this current era, cyber-physical systems (CPSs) have gained concentrated consideration in various fields because of their emergent applications. Though the robust dependence on communication networks creates cyber-physical systems susceptible to deliberated cyber related attacks and detecting these cyber-attacks are the most challenging task. There is the interaction among the components of the cyber and physical worlds, so CPS security needs a distinct approach from past security concerns. Deep learning (DL) distributes better performance than machine learning (ML) due to its layered architecture and the efficient algorithm for extracting prominent information from training data. So, the deep learning models are taken into consideration quickly for detecting cyber-attacks in cyber physical systems. As numerous attack detection methods have been proposed by various authors for enforcing CPS security, this paper reviews and analyzes multiple ways of attack detection presented for CPS using deep learning. We will be putting the excellent potential for detecting cyber-attacks for CPS concerning deep learning modules. The admirable performance is attained partly as highly quality datasets are eagerly obtainable for the use of the public. Moreover, various challenges and research inclinations are also discussed in impending research.

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DeepAnom: An Ensemble Deep Framework for Anomaly Detection in System Processes

Cited by 5 publications

References 7 publications

Design and Development of AD-CGAN: Conditional Generative Adversarial Networks for Anomaly Detection

Design and Development of AD-CGAN: Conditional Generative Adversarial Networks for Anomaly Detection

Time Series Anomaly Detection Embedded Systems By Using LSTM

A Systematic Analysis of Enhancing Cyber Security Using Deep Learning for Cyber Physical Systems

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