Discovering Rules from Disk Events for Predicting Hard Drive Failures

Agarwal, Vipul; Bhattacharyya, Chiranjib; Niranjan, T.; Susarla, Sai

doi:10.1109/icmla.2009.62

Cited by 17 publications

(5 citation statements)

References 5 publications

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“…The latter would generally involve logical, mechanical and electrical repair work of storage media (see analysis phase of McKemmish (1999)'s framework). This is an intricate process as it may involve repairing a wide range of storage media (magnetic, optical and semiconductor) before digital evidence can be analysed for forensic investigations and understanding of the underlying storage media technologies (Agrawal et al 2009), file systems and data formats is essential.…”

Section: A Overview Of Digital Forensicsmentioning

confidence: 99%

iOS Forensics: How Can We Recover Deleted Image Files with Timestamp in a Forensically Sound Manner?

Ariffin¹,

D'Oorazio²,

Choo³

et al. 2013

2013 International Conference on Availability, Reliability and Security

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iOS devices generally allow users to synch their images (pictures) and video files using iTunes between Apple products (e.g. an iPhone and a MacBook Pro). Recovering deleted images, particularly in a forensically sound manner, from iOS devices can be an expensive and challenging exercise (due to the hierarchical encrypted file system, etc). In this paper, we propose an operational technique that allows digital forensic practitioners to recover deleted image files by referring to iOS journaling file system. Using an iPhone as a case study, we then conduct a forensic analysis to validate our proposed technique.

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Section: A Overview Of Digital Forensicsmentioning

confidence: 99%

iOS Forensics: How Can We Recover Deleted Image Files with Timestamp in a Forensically Sound Manner?

Ariffin¹,

D'Oorazio²,

Choo³

et al. 2013

2013 International Conference on Availability, Reliability and Security

View full text Add to dashboard Cite

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“…Murray et al [3] develop a multi-instance naive Bayes classifier to reduce the number of false alarms when predicting disk failures, while Tan and Gu [10] investigates the performance of a tree augmented naive Bayesian method to predict the future drive status. Agarwal et al [7] investigate the performance of a rule-based classifier for discovering disk failures, Li et al [20] address the same problem by using decision trees, and Zhu et al [16] adopt neural networks and SVM. Lu et al [46] implement a convolutional neural network with long shortterm memory that predicts HDD failures with a 10-day lookahead window by considering SMART features, disk performance metrics, and disk physical location.…”

Section: Related Workmentioning

confidence: 99%

“…In addition to the raw features, we also consider the cumulative effect of SMART 187 (Reported Uncorrectable Errors). For cumulative features (i.e., SMART 4,5,7,9,10,12,192,193,197,198,199,240,241,and 242), we also calculate their noncumulative version (the difference with the previous observation), noted as "diff".…”

mentioning

confidence: 99%

The Life and Death of SSDs and HDDs: Similarities, Differences, and Prediction Models

Pinciroli,

Yang,

Alter

et al. 2020

Preprint

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Data center downtime typically centers around IT equipment failure. Storage devices are the most frequently failing components in data centers. We present a comparative study of hard disk drives (HDDs) and solid state drives (SSDs) that constitute the typical storage in data centers. Using a six-year field data of 100,000 HDDs of different models from the same manufacturer from the BackBlaze dataset and a six-year field data of 30,000 SSDs of three models from a Google data center, we characterize the workload conditions that lead to failures and illustrate that their root causes differ from common expectation but remain difficult to discern. For the case of HDDs we observe that young and old drives do not present many differences in their failures. Instead, failures may be distinguished by discriminating drives based on the time spent for head positioning. For SSDs, we observe high levels of infant mortality and characterize the differences between infant and non-infant failures. We develop several machine learning failure prediction models that are shown to be surprisingly accurate, achieving high recall and low false positive rates. These models are used beyond simple prediction as they aid us to untangle the complex interaction of workload characteristics that lead to failures and identify failure root causes from monitored symptoms.

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“…All SMART attributes backed by researches were taken into consideration and was used for the training of both Decision Tree and ANFIS. These includes SMART 5,10,184,187,188,196,197,198,201 and 230. However, since SMART 230 is not present in the dataset from BackBlaze, it is removed from the final set of columns as shown in Table I.…”

Section: A Data Preparationmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Inference System Approach on Predicting Hard Disk Failures towards Reliable Data Center

Olalia¹

2018

IJRASET

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Despite the high accuracy showcased by some studies in predicting hard disk failure using decision tree in the classification process, the accuracy of the decision tree is in question due to its vast difference among other algorithms ranging from 17.7% to 60.92%. This paper confirms the claim of some studies about the overfitting of decision tree when used with a large amount of data, real-valued and numeric attributes like SMART at-tributes. Utilizing ANFIS algorithm in predicting imminent hard disk failure surpasses other algorithms (CHAID, C&R Tree, Neural Network, MLR, and SVM) by 4.2% while keeping its distance from the very high percentage (99.58) over-fitted decision tree at 86.08%. The ANFIS also predicted the failure 5 days before it actually happens. Keywords: ANFIS, Predicting Hard Disk Failure, SMART Attributes, Data Center, Imminent Disk Failure I. INTRODUCTIONHaving files rested and backed up on a data center or a cloud storage platform ensures continuous operation, data protection and recovery [1]. It is the responsibility of the data center to do all their necessary plans and procedures to be so. Critical factors such as experience, financial stability, security, support and physical infrastructure need to be maintained to have a reliable data center [2]. In the Philippines, 80% of business enterprises experienced data loss costing them around $8 billion worth of data loss [3]. On a larger scale, companies around the world experienced an average data loss of 400% in just two years accumulating a total amount loss of $1.7 trillion, 30% of which came from cloud storage. Admittedly, 51% of surveyed companies have no disaster recovery plan [4]. Study shows that the leading cause of data loss is the hardware failure, human error, software corruption, computer viruses and natural disaster among which hardware failure rank first at 57% [5]. Recent studies conducted by Data Barracks shows that hardware failure is still one of the topmost cause of data loss at 25%, being human error as the leader at 29% [6]. With the help of machine learning, failure can be preempted thereby avoiding data loss before it happens. There are some researches delving into this problem [7][8] [9][10][11] [12]. Each research presented varying results. Among these different researches and algorithms used, decision tree came out as the most accurate. Similarly, Suchatpong and Bhumkittipich's study com-pared decision tree with the neural network, SVM, CHAID and C&R Tree. Decision tree ranks first at 99.58% while neural network is at 56.09% accurate, SVM is at 38.66%, CHAID at 50.42% and C&R tree at 56.93%, which are way lower that the decision tree. While the result of the decision tree is promising, there is an observable irregularity in the result. The decision tree is too accurate compare to other algorithm in predicting hard drive failure. IBM states that if an algorithm has 98% accuracy while other techniques tried has 60% accuracy, it is most probably overfitting, which is exactly the case [13]. Initial investigatio...

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Discovering Rules from Disk Events for Predicting Hard Drive Failures

Cited by 17 publications

References 5 publications

iOS Forensics: How Can We Recover Deleted Image Files with Timestamp in a Forensically Sound Manner?

iOS Forensics: How Can We Recover Deleted Image Files with Timestamp in a Forensically Sound Manner?

The Life and Death of SSDs and HDDs: Similarities, Differences, and Prediction Models

Adaptive Neuro-Fuzzy Inference System Approach on Predicting Hard Disk Failures towards Reliable Data Center

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