Backdoor attacks pose a persistent security risk to deep neural networks (DNNs) due to their stealth and durability. While recent research has explored leveraging model unlearning mechanisms to enhance backdoor concealment, existing attack strategies still leave persistent traces that may be detected through static analysis. In this work, we introduce the first paradigm of revocable backdoor attacks, where the backdoor can be proactively and thoroughly removed after the attack objective is achieved. We formulate the trigger optimization in revocable backdoor attacks as a bilevel optimization problem: by simulating both backdoor injection and unlearning processes, the trigger generator is optimized to achieve a high attack success rate (ASR) while ensuring that the backdoor can be easily erased through unlearning. To mitigate the optimization conflict between injection and removal objectives, we employ a deterministic partition of poisoning and unlearning samples to reduce sampling-induced variance, and further apply the Projected Conflicting Gradient (PCGrad) technique to resolve the remaining gradient conflicts. Experiments on CIFAR-10 and ImageNet demonstrate that our method maintains ASR comparable to state-of-the-art backdoor attacks, while enabling effective removal of backdoor behavior after unlearning. This work opens a new direction for backdoor attack research and presents new challenges for the security of machine learning systems.

In recent years, machine learning and deep learning have made remarkable progress and are now widely applied in various fields, including image classification, autonomous driving, natural language processing, and medical diagnosis. However, training these models requires large datasets, which often contain substantial amounts of sensitive personal information, such as medical records and financial details. Without effective privacy protection measures during model training, the risk of sensitive data leakage increases, potentially resulting in severe privacy violations and a loss of trust. As an innovative data representation technique, the Negative Database has proven to be an effective solution in privacy-sensitive domains. Negative databases can be derived from SAT (Boolean Satisfiability Problem) instances, and the hardness of these instances is directly correlated with the level of data protection provided by the Negative Databases. Developing efficient SAT instance generation algorithms to create harder SAT instances can significantly enhance the privacy protection capabilities of the Negative Database. This paper analyzes the hardness conditions of SAT solvers using the Conflict-Driven Clause Learning strategy and proposes a two-stage SAT instance generation algorithm to generate harder SAT instances. These hard instances not only aid in constructing more secure negative databases for enhanced privacy protection but also provide valuable test cases for evaluating and improving SAT solvers.

Software aging, characterized by an increasing failure rate or performance degradation in long-running software systems, poses significant risks, including substantial financial losses and potential threats to human lives. This phenomenon is primarily driven by the accumulation of runtime errors, commonly referred to as aging-related bugs (ARBs). Aging-related bug prediction (ARBP) has been proposed to facilitate the detection and remediation of ARBs prior to software release. However, ARBP’s effectiveness heavily depends on the quality of dataset features used. Previous research has largely relied on a standard set of manually designed metrics, often overlooking that these metrics may fail to distinguish between code segments with different semantics, even when they exhibit identical metric values. While some studies have attempted to develop models that learn semantic features from source code, they typically focus on token-level or graph-level features, neglecting a comprehensive exploration of ARB characteristics within the source code. Specifically, there is insufficient discussion on whether deep semantic features can adequately capture the essential traits that trigger aging phenomena. In this paper, we propose a novel multi-view graph feature learning framework based on Graph-Transformer, which integrates newly proposed ARB features extracted from Abstract Syntax Trees with Code Property Graphs for feature learning. Our approach effectively captures hierarchical structures and variable dependencies, facilitating the identification of complex interactions that contribute to ARBs. Additionally, we implement sub-graph sampling and class imbalance strategies to enhance model performance. Experimental results across three datasets demonstrate that our method surpasses state-of-the-art approaches, a code property graph-based feature extraction method (specifically SGT), achieving precision improvements of 8.2% on Linux, 15.4% on MySQL, and 2.5% on NetBSD, thereby establishing a new benchmark for ARB prediction.

The inefficiency of the decades-old JPEG standard imposes a significant maintenance and cost burden on largescale software ecosystems that handle trillions of legacy files. Current solutions are ineffective, as modern codecs are incompatible with JPEG’s unique artifacts, while existing recompression tools rely on ad-hoc, handcrafted algorithms with fundamental performance limitations. To address this challenge systematically, we introduce PLLR, a reusable software framework built upon a novel design paradigm: learned information decomposition. Instead of manual rule-making, our framework automates the process of identifying and separating redundancies. It employs a Variational Autoencoder to learn a global, probabilistic model of the image content, effectively decoupling the predictable signal components from a highly sparse, low-entropy residual. By isolating this essential information, the subsequent entropy coding stage becomes significantly more efficient. Our implementation of this framework establishes a new state-of-the-art, achieving a fully lossless file size reduction of 31.54% on the Kodak dataset. This result validates the superiority of our learned decomposition framework for legacy data compression and offers a practical pathway to reduce the immense economic and environmental costs associated with large-scale data systems.

Driver fatigue detection is a core functional module in intelligent driver monitoring systems, and its performance directly affects the reliability and safety of Advanced Driver Assistance Systems (ADAS). Existing computer vision-based fatigue detection methods have issues such as redundant feature extraction and insufficient modeling of long-term temporal dependencies. In particular, when facial actions are highly similar to yawning, they are prone to causing misjudgments in decisionmaking. To address these challenges, this paper proposes a facial feature and attention-based temporal convolutional network (FATCN) for software engineering practice, it improves the performance by optimizing feature engineering. FA-TCN includes two core software modules: the first is the facial feature detection module (FFD-Module), it simplifies the input dimensions by extracting the temporal features of the eye aspect ratio (EAR) and mouth aspect ratio (MAR), enhances the robustness of the module in complex driving scenarios; the second is the facial action classification module (FAC-Module), it uses an attention-based temporal convolutional network (TCN) to optimize the longsequence feature modeling capability by parallelizing convolution operations. The experimental results show that, on the YawDD dataset, FA-TCN outperforms existing advanced methods and effectively enhances the recognition ability of intelligent driving software for subtle fatigue actions.

Deep learning-based log anomaly detection demands extensive labeled data, posing significant challenges for emerging systems with limited logs. Transfer learning mitigates this issue by leveraging knowledge from data-rich source domains, enabling effective adaptation to data-scarce target domains. The challenge in recent cross-domain research lies in the joint optimization of knowledge transfer efficacy and model generalization capability. To address these limitations, we propose LogCADA, a novel logarithmic anomaly detection framework based on transfer learning, which can obtain effective common features through double-layer adversarial training, and distinguish common features and unique features between different domains through multi-source domain contrast alignment to achieve better knowledge transfer.The results demonstrate that our method can be adapted from dual source to single target domain and effectively overcome the inherent limitations of traditional cross-domain anomaly detection methods, yielding significant practical value for real-world log analysis scenarios.

Log anomaly detection is critical for maintaining system reliability and observability in complex cloud and microservice environments. However, existing methods often remain limited to binary classification, struggle to adapt to dynamic log patterns, and suffer from semantic loss due to log parsing. To address these challenges, we propose GLog, an end-to-end framework that enables dynamic anomaly type prediction without requiring manual type labels. GLog first fine-tunes instruction-tuned large language models using normal/abnormal labels to achieve high-accuracy anomaly detection on raw, unparsed log sequences. It then clusters the detected anomalies to automatically generate pseudo anomaly type labels and descriptions, which are further used for second-stage fine-tuning, enabling the model to predict specific anomaly types with interpretable outputs. By leveraging full log semantics and dynamically updating its anomaly type repository, GLog reduces manual annotation costs and adapts to evolving system behaviors in large-scale environments.

Industrial Control Systems (ICS) serve as the backbone of critical infrastructures, controlling and automating the stable operation of industrial processes. With the progressive integration of industrial processes and Information Technology (IT), ICS have evolved from closed, isolated systems to open, interconnected networks. This evolution has significantly expanded attack surfaces and increased security vulnerabilities, making ICS more susceptible to cyber attacks. Intrusion Detection Systems (IDS), particularly those based on deep learning that can learn spatio-temporal features from raw network traffic, are the most effective methods for protecting ICS. However, most existing DL-based IDS adopt a Traditional Spatio-Temporal Feature (TSTF) view for representation learning, which often ignores the unique characteristics of industrial protocols and ICS communication patterns, and loses important fine-grained discriminative information. As a result, the detection models perform poorer with higher false negatives and false positives when applied in ICS. To overcome the above issue, we propose a novel Segment-Based Spatio-Temporal Feature (SSTF) view, which leverages temporal dependencies among the same segments in different packets within a flow and spatial correlations between different segments. Additionally, we introduce a dual-view intrusion detection framework-DVSTdetector, that integrates both the TSTF and SSTF views and employs two workflows to promote better representation learning from both global and local perspectives in parallel, obtaining more robust spatiotemporal features. A publicly available dataset (WDT) and a private dataset (XLP) are used to evaluate our approach. The experimental results demonstrate its effectiveness and superiority, outperforming six state-of-the-art approaches and achieving high performance across six metrics: Accuracy ($99.35 \%$, 99.87%), Precision (99.28%, 99.93%), Recall (98.52%, 99.90%), F1-score ($\mathbf{9 8. 9 0 \%, ~} \mathbf{9 9. 9 1 \%}$), AUC-ROC ($\mathbf{9 9. 1 1 \%, ~ 9 9. 8 4 \%), ~ a n d ~ a ~ l o w ~ F a l s e ~}$ Positive Rate ($\mathbf{0. 2 9 \%, ~} \mathbf{0. 2 1 \%}$).

Hands-off detection (HOD) is used in autonomous driving vehicle (ADV). However, how to choose an appropriate HOD timespan (HODT) either practically or legislatively remains unsolved. In this paper, an asymmetric Nash bargaining-based HODT determination is introduced. Utility functions of autonomous driving system (ADS) and driver are developed. HODT is determined based on the asymmetric Nash bargaining solution. Experimental result shows existing adopted HODTs can be obtained using the proposed method. Appropriate HODT can be obtained considering safety requirement and driver's need. Under high safety requirement, HODT is short regardless of driver's need. When safety requirement is not strict, HODT can be prolonged to satisfy driver's need. By constructing situation awareness (SA) evolution process considering HODT, ceiling HODT can be obtained, which can be used in legislation as the longest HODT that can be chosen. Experiment also shows superiority of dynamical HODT over the constant HODT. The proposed method offers a possible theoretical solution to HODT determination, showing potential in practice and legislation.

Protocols serve as the foundation for communication between devices and systems in modern computing environments. However, their widespread use also exposes them to various security threats. A flaw in protocol implementation can be exploited to compromise system security, leading to severe disruptions or unauthorized access. Fuzzing has become a widely used technique for protocol vulnerability detection. However, the existing protocol fuzzing approaches often lack effective guidance strategies for exploring the complex protocol state space, which leads to limited coverage and suboptimal testing efficiency.In this paper, we propose BayesFuzz, a Bayesian-guided greybox fuzzing approach for stateful protocols. It maintains a probability table to record the probability of sending different messages and triggering different state transitions in the current state. This table is updated via Bayesian inference based on feedback from each fuzzing iteration. Accordingly, BayesFuzz is able to select the optimal message for the current state during test case generation, thereby improving fuzzing efficiency in complex stateful protocols. Experimental results confirm the effectiveness of BayesFuzz. Compared with state-of-the-art fuzzers BooFuzz and AFLNET, BayesFuzz increases branch coverage by averagely 23.18% and 45.3% within 24 hours. Furthermore, it successfully discovered an unknown vulnerability in the MQTT protocol implementation.

Anomaly detection in HTTP traffic is critical for securing web applications against evolving cyber threats. We propose a semi-supervised method that combines domain-specific language modeling with sequence reconstruction to identify anomalies in HTTP requests. Our approach leverages only benign traffic for training and uses reconstruction errors for detecting malicious activity. It achieves a strong balance between precision and recall while maintaining low computational requirements, making it suitable for real-time and edge deployments. Extensive evaluations on three public HTTP datasets show that our method outperforms traditional baselines and fine-tuned BERT models, with an F1-score of 0.92 and AUC of 0.96. We also introduce a simple interpretability mechanism by attributing anomalies to token-level reconstruction errors, providing insights into detected threats. The proposed solution is scalable, lightweight, and effective across diverse attack scenarios without requiring large labeled datasets.

With the development of genome sequencing technology, genomic data have been widely collected and used in real-world scenarios, such as genomic medicine and similar patient query (SPQ) cases, based on similarity comparisons of genomic sequences. However, genomic data are unique for every person and contain a large amount of sensitive information involving personal privacy. Therefore, determining how to protect privacy while utilizing genomic data has become a key issue. In this paper, we mainly investigate the privacy protection of SPQs, and we propose five algorithms (NDB-ED, NDB-Band, NDB-Block, NDB-SIS and NDB-SDS) based on a promising technique called negative representation of information (NRI). The proposed algorithms use five kinds of similarity comparison approaches widely applied in SPQs and convert all genomic sequences into negative databases (NDBs, among the most important NRI forms) for privacy protection. When performing an SPQ, NDB-ED approximates the edit distance between the sketches (the statistics of NDBs) of two genomic sequences to evaluate the dissimilarity. Banded edit distance and block-wise edit distance are two effective approximate edit distance algorithms, which can greatly reduce the time complexity. NDB-Band and NDB-Block are used to estimate the banded edit distance and the block-wise edit distance between the sketches of NDB pairs, respectively, to further improve query efficiency and reduce communication overhead. Besides, private genome set intersection size (SIS) and set difference size (SDS) can also be used instead of edit distance to evaluate the dissimilarity between genomic pairs during SPQs. NDB-SIS and NDB-SDS estimate the SIS and SDS between the sketches, for similarity comparison. The experimental results demonstrate that the proposed algorithms can achieve promising results in terms of accuracy and efficiency (Our best algorithm improves accuracy by at least 10% and query time is at least 700 times faster than existing algorithms during SPQs).

Distributed video coding (DVC) transfers the complex process of the encoder to the decoder, which is suitable for video applications with limited encoding resources. Deep learning has shown impressive performance in video coding tasks in learning nonlinear compact representations of input frames and reconstructing video frame details. It is worth exploring whether deep learning implementation of the DVC paradigm is feasible and whether performance gains can be obtained. This paper proposes a deep DVC scheme (DDVC) using a quality enhancement network (QEN), which maps pixels to a more compressible latent space via an autoencoder resulting in a compact representation of Wyner-Ziv (WZ) frames. Moreover, considering the spatio-temporal correlation between the WZ frame and the Key frame, the QEN on the decoder side, using CNN and LSTM iteratively extracts common information between the WZ frame and the Key frame, which could further finetune the WZ frame reconstruction. We evaluated DDVC in limited encoding resources application scenarios with 19 related video sequences. Results on the video sequences with different motion intensity levels show that DDVC significantly outperforms existing schemes in reconstruction quality with the same compression ratio. We open-sourced the implementation at GitHub1.

Software aging results from aging-related bugs (ARBs) in long-running systems, that usually causes performance decline and system crashes. Since collecting ARB data is challenging due to its scarcity, it hinders the development of effective prediction models. Moreover, existing cross-project ARB prediction methods often ignore project-specific distribution differences and neglect class imbalance and overlap issues between ARB and non-ARB classes. In this paper, a hybrid approach that combines the balanced distribution adaptation (BDA), the improved subclass discriminant analysis (ISDA), and the self-paced ensemble under-sampling (SPE) techniques, called BISP in short, is proposed to address the aforementioned problems. The main idea behind BISP is first to use BDA to adaptively reduce the difference of projects' marginal distribution and conditional distribution, and then employ ISDA and SPE to alleviate the severe class imbalance together with class overlap. Experimental results obtained for six classifiers and six cross-project datasets show that compared with the state-of-the-art approaches TLAP and JDA-ISDA based on transfer learning, BISP improves the average balance by 34.8% and 2.3% and improves the average AUC by 26.5% and 8.4%, respectively. Compared with the deep learning approach SRLA, BISP can improve the average balance value by 5.1%.

Log anomaly detection aims to discover abnormal events from massive log data to ensure the security and reliability of software systems. However, due to the heterogeneity of log formats and syntaxes across different systems, existing log anomaly detection methods often need to be designed and trained for specific systems, lacking generalization ability. To address this challenge, we propose LogDLR, a novel unsupervised cross-system log anomaly detection method. The core idea of LogDLR is to use universal sentence embeddings and a Transformer-based autoencoder to extract domain-invariant latent representations from log entries, which can effectively adapt to log format changes and capture semantic information and dependencies in log sequences. To obtain domain-invariant latent representations, we adopt a domain-adversarial training strategy, introducing a domain discriminator that competes with the Transformer-based encoder through a gradient reversal layer, forcing the encoder to learn shared knowledge between different system logs. Finally, the Transformer-based decoder detects anomalies based on the domain-invariant representations obtained by the encoder. We evaluate LogDLR in simulated cross-system scenarios using three publicly available log datasets. The experimental results show that LogDLR can handle heterogeneous logs effectively in cross-system scenarios and achieve efficient and accurate anomaly detection on both source and target systems.

System logs record the system’s status and application behavior, providing support for various system management and diagnostic tasks. However, existing methods for log anomaly detection face several challenges, including limitations in recognizing current types of anomalous logs and difficulties in performing online incremental updates to the anomaly detection models. To address these challenges, this paper introduces DRLLog, which applies Deep Reinforcement Learning (DRL) networks to detect anomalous events. DRLLog uses Deep Q Network (DQN) as the agent, with log entries serving as reward signals. By interacting with the environment generated from log data and adopting various action behaviors, it aims to maximize the reward value obtained as feedback. Through this approach, DRLLog achieves learning from historical log data and perception of the current environment, enabling continuous learning and adaptation to different log sequence patterns. Additionally, DRLLog introduces low-rank adaptation by using two low-rank parameter matrices in the fully connected layer of the DQN to represent changes in its weight matrix. During online model learning, only low-rank parameter matrices of the model are updated, effectively reducing the model’s overhead. Furthermore, DRLLog introduces focal loss to focus more on learning the features of anomalous logs, effectively addressing the issue of imbalanced quantities between normal and anomalous logs. We evaluated the performance on widely used log datasets, including HDFS, BGL and ThunderBird, showing an average improvement of 3% in F1-Score compared to baseline methods. During online model learning, DRLLog achieves an average reduction of 90% in parameter count and a significant decrease in training and testing time as well.

Membership inference attacks (MIAs), which aim to determine whether a specific sample is included in a machine learning model’s training set, have been recognized as a major privacy threat in recent years. Recent studies have shown that membership inference attacks can lead to privacy leaks in language models. Among existing methods for language model membership inference attacks, reference-based attacks exhibit good performance but require the attacker to obtain the training data distribution of the target model, which is impractical. Reference-free attacks impose lower requirements on attackers but tend to produce unsatisfactory results due to their excessive reliance on the overfitting of the target model. In this paper, we propose a practical Membership Inference Attack based on Weight-enhanced Likelihood (WEL-MIA). We investigate the membership inference difficulty at the token level and find that there are greater mean discrepancy of membership signals between members and non-members for tokens which are more difficult to predict. We also recognize that unreliable calibration probabilities pose an impediment to reference-based membership inference attacks. Anchored on the observation, we design different weights for tokens in the text, providing a new way of aggregating token-level member-ship signals for individual samples. Our attack uses a reference model to calibrate token-level probabilities, with the reference model fine-tuned on the target dataset, thereby producing more reliable calibration probabilities. In our assumption, attackers do not possess any auxiliary data, eliminating the need for the reference model to have prior knowledge of the same domain or distribution. We validate our method on several language models and datasets, and the results demonstrate that WEL-MIA achieves significant performance without relying on any auxiliary data.

Visual recognition algorithms based on deep neural network (DNN) have been widely used in the design of automatic driving to recognize traffic sign images. However, there exists adversarial patches which are essentially the anormal image block that can be locally observed but not noticed by humans. And these visual recognition algorithms often suffer from the effect of adversarial patches, due to these patches can change the algorithms recognition result of the images. To solve the above issues, this work proposes the anomaly edge detection and image inpainting defense (AEDD) for visual recognition. This framework uses anomaly location to obtain the anomaly area, uses edge detection to get an accurate edge of the anomaly area, finally uses image inpainting to repair this area. We also combine two attack algorithms with three patch sizes, and generate six types of adversarial patches on the GTSRB dataset. We have demonstrated the effectiveness of our approach, resulting in an average 6.6% increase in defense accuracy compared to the state-of-the-art methods. Our code is available at https://github.com/drtt438/AEDD for the purpose of reproducibility.

Image compression has become an important task for reducing storage and transmission costs. However, recent models for learned image compression have been developed to increase the network’s number of layers and channels to achieve better visual effects. This resulted in higher computing and memory resources, making deploying the model on compute-constrained platforms such as wireless devices impractical. In this paper, we propose a lightweight autoencoder with hierarchical priors. The lightweight autoencoder reduces the model’s parameter size and calculation amount based on ensuring high fidelity and low bit rates of the image. Simulation results indicate the proposed model yields a smaller size: the parameters are reduced by 81.66%, and the calculation amount is reduced by 94.7% over the benchmark. Besides, the proposed model results in a speed improvement of 200 times. At the same time, our model achieves nearly the same performance as the baseline on MS-SSIM and LPIPS distortion metrics.

Industrial Control System (ICS) is vital to critical infrastructures, yet it faces increasing security threats. Current Intrusion Detection System (IDS) designed for ICS often overlooks the unbalanced resource distribution among devices at different layers and primarily focus on known attacks, rendering it difficult to be deployed on all key nodes and vulnerable to unknown threats. To address above issues, we propose a Combined Intrusion Detection Framework (CIDF). This innovative approach is based on strategy of “multi-level layered deployment, combined detection”, deploying the Packet Signature model and the Enhanced Fast Search and Find of Density Peaks (EFSFDP) model on devices at different layers. To achieve optimal use of resource and full protection for ICS and combining the advantages of multiple detection methods to effective detect both known and unknown attacks. The Evaluation using a public gas pipeline dataset and a private dataset shows our approach outperforms existing methods, achieving an average Accuracy, Precision, and Recall of 94%, 95.5%, and 86.5% respectively, and along with superior detection speed.

Ensuring privacy often results in sacrificing the accuracy of iris recognition systems. A primary challenge in contemporary iris biometric privacy methods lies in striking a balance between recognition accuracy and privacy of protected iris templates. Hence, any proposition for privacy-protected iris recognition must prioritize irreversibility, revocability, and unlinkability to uphold robust privacy standards while achieving higher recognition accuracy. This research proposes an approach that stands as a robust solution with acceptable advancement in three challenges: recognition accuracy, privacy protection and computational efficiency. We experimented with an innovative technique that manipulates the columns of an iris template by fusing the bit pattern of the template using the XOR operation. The transformation process is non-linear. This fusion introduces randomness and variability to the fused templates. It also poses enhanced privacy protection. Two datasets were used to validate the proposed approach. Based on the results of dataset 1, the proposed approach accepts genuine users at a rate of 99.11% while it accepts 0.01% of imposters. For dataset 2, the Genuine Acceptance Rate (GAR) is depicted as 81.12% while FAR is at 0.01%. The proposed approach can be applied in practice due to its higher computational efficiency. As further improvements, the research can be extended to more widespread databases and higher-quality iris samples.

In the context of limited maintenance resources, predicting the severity of code smells is more practically useful than simply detecting them. Fontana et al. first empirically investigated some classification algorithms and some regression algorithms, for severity prediction. Their results showed that random forest and decision tree performed well on Mean Absolute Error (MAE), Mean Squared Error (MSE), and Spearman and Kendall rank correlation coefficients. However, they did not consider the issue of imbalanced data distribution in the severity dataset, and used inappropriate performance evaluation metrics. Therefore, we revisit the effectiveness of 10 classification methods and 11 regression methods, for code severity prediction using Cumulative Lift Chart (CLC) and Severity@20% as the primary performance metrics and Accuracy as the secondary performance indicator. The results show that the Gradient Boosting Regression (GBR) method performs the best in terms of these metrics.

In recent years, deep learning has been applied in a wide variety of domains and gains outstanding success. In order to achieve high accuracy, a large amount of training data and high-performance hardware are necessary for deep learning. In real-world applications, many deep learning developers usually rent cloud GPU servers to train or deploy their models. Since training data may contain sensitive information, training models on cloud servers will cause severe privacy leakage problem. To solve this problem, we propose a privacy-preserving deep learning model based on matrix transformation. Specifically, we transform original data by adding or multiplying a random matrix. The obtained data is significantly different from the origin and it is hard to recover original data, so it can protect the privacy in original data. Experimental results demonstrate that the models trained with processed data can achieve high accuracy.

Previous research have utilized public software defect datasets such as NASA, RELINK, and SOFTLAB, which only contain class label information. Almost all Effort-Aware Defect Prediction (EADP) studies are carried out around these datasets. However, EADP studies typically relying on bug density (i.e., the ratio between bug numbers and the lines of code) for ranking software modules. In order to investigate the impact of neglecting bug number information in software defect datasets on the performance of EADP models, we examine the performance degradation of the best-performing learning to rank methods when class labels are utilized instead of bug numbers. The experimental results show that neglecting bug number information in building EADP models results in an increase in the detected bugs. However, it also leads to a significant increase in the initial false alarms, ranging from 45.5% to 90.9% of the datasets, and an significant increase in the modules that need to be inspected, ranging from 5.2% to 70.4%. Therefore, we recommend not only the class labels but also the bug number information should be disclosed when publishing software defect datasets, in order to construct more accurate EADP models.

Software aging refers to a problem of performance decay in long-running software systems. This phenomenon is primarily attributed to the accumulation of run-time errors, commonly known as aging-related bugs (ARBs). Detecting ARBs through Aging-related Bug Prediction (ARBP) is crucial in ensuring system reliability. The effectiveness of ARBP heavily relies on the quality of datasets. However, ARB datasets often suffer from class overlap, where instances from different classes exhibit similar feature values. Class overlap poses a significant challenge as it compromises the quality of training data and subsequently impacts ARBP accuracy. To address this issue, we propose an improved Fuzzy C-means clustering method named IFCM, designed to mitigate class overlap in ARBP tasks. IFCM can identify whether an instance occurs overlap, and identify the overlap degree of this instance through the predefined parameters. We evaluate our proposed method on two public datasets Linux and MySQL and one self-collected dataset NetBSD using five different classifiers with five performance metrics (AUC, F1, Balance, PD, PF). Comparison with four existing methods (No clean, NCL, IKMCCA, ROCT) demonstrates that IFCM is effective in alleviating class overlap in ARBP. For Instance, IFCM achieves promising results in terms of AUC blue (which are 0.762, 0.757, and 0.642) and Balance (which are 0.709, 0.736, and 0.595) at the dataset level.

The application of iris biometric data has become prevalent across various domains, encompassing access control, identity verification, and criminal investigations. Consequently, there is a pressing need to develop effective methods for safeguarding the privacy of iris data. While numerous methods for iris data protection have been proposed, the majority of them fall short of meeting the ISO/IEC 24745 standards about irreversibility, revocability, and unlinkability. In this paper, we introduce a novel iris data protection method called TNCB, which is based on a transformation network. The TNCB involves performing a block-wise permutation of the original iris images using application-specific parameters, followed by pixel-by-pixel modulo and inversion fusion operations. The resulting images are subsequently employed for pre-training a recognition network that will be used to recognize protected images. Afterwards, a transformation network is introduced to achieve a further non-invertible transformation. Our security analysis demonstrates that the TNCB could fulfill the three major security requirements. To validate its effectiveness, we conducted a series of attack and performance experiments on the CASIA-Iris-Lamp and CASIA-Iris-Thousand datasets. Experimental results substantiated the robustness of TNCB in maintaining recognition performance while safeguarding the privacy of iris data. Furthermore, experimental results also highlight that our scheme could effectively support iris recognition in both open-set and close-set modes.

Genotype imputation estimates missing genotypes from the haplotype or genotype reference panel in individual genetic sequences, which boosts the potential of genome-wide association and is essential in genetic data analysis. However, the genetic sequences involve people’s privacy, confirming an individual’s identification and even disease information. This work proposes a secure genotype imputation model, which uses a linear regression model and the homomorphic encryption scheme over ciphertext to impute missing genotypes. The inference model is trained with float plaintext parameters, which are round into integers to avoid high complexity homomorphic evaluation on float number operations without bootstrapping operations. Even though the rounding parameters in the inference model are not the same as those in the trained model, We find that it will no effect on the outcome of the homomorphic prediction. Thus, a high-efficiency genotype imputation inference model over the ciphertext is obtained while keeping the high-security level. The simulation results indicate that the accuracy of the secure inference model is almost the same as the original model trained on float parameters. The secure inference model’s accuracy is 98.6% for a single genotype.

The generation of SAT instances is an important issue in computer science, and it is useful for researchers to verify the effectiveness of SAT solvers. Addressing this issue could inspire researchers to propose new search strategies. SAT problems exist in various real-world applications, some of which have more than one solution. However, although several algorithms for generating random SAT instances have been proposed, few can be used to generate hard instances that have multiple predefined solutions. In this paper, we propose the KHidden-M algorithm to generate SAT instances with multiple predefined solutions that could be hard to solve by the local search strategy when the number of predefined solutions is small enough and the Hamming distance between them is not less than half of the solution length. Specifically, first, we generate an SAT instance that is satisfied by all of the predefined solutions. Next, if the generated SAT instance does not satisfy the hardness condition, then a strategy will be conducted to adjust clauses through multiple iterations to improve the hardness of the whole instance. We propose three strategies to generate the SAT instance in the first part. The first strategy is called the random strategy, which randomly generates clauses that are satisfied by all of the predefined solutions. The other two strategies are called the estimating strategy and greedy strategy, and using them, we attempt to generate an instance that directly satisfies or is closer to the hardness condition for the local search strategy. We employ two SAT solvers (i.e., WalkSAT and Kissat) to investigate the hardness of the SAT instances generated by our algorithm in the experiments. The experimental results show the effectiveness of the random, estimating and greedy strategies. Compared to the state-of-the-art algorithm for generating SAT instances with predefined solutions, namely, M-hidden, our algorithm could be more effective in generating hard SAT instances.

In the Android system, software aging is an essential factor affecting user experience. Its occurrence will lead to poor responsiveness or crash/hang failure of the system. Recently, the strategies to schedule rejuvenation are marching toward a situation that needs to consider both usage behavioral aspects of its users (i.e., switch between active and sleep modes) and two-level software aging process (i.e., Operating System (OS) and Application Software (AS)), because rejuvenating the OS or AS during active time slot contributes to terrible user experience. To be able to achieve higher user experience and lower user interference, in this paper, we present to employ the Continuous Time Markov Chain (CTMC) model to study the impact of software aging and rejuvenation on user experience on two different rejuvenation strategies: condition-based and time-based rejuvenations. In contrast to the existing works, our models capture the interactions between usage behavioral aspects of users and two-level aging and rejuvenation. We then define three metrics to evaluate the user experience, including User-perceived (1) Fluency (UF), (2) Failure Probability (UFP), and (3) Availability (UA). The numerical analysis has the following noticed conclusions. The optimal value of UF yielded by condition-based rejuvenation reaches a 3.486% improvement over that of time-based. Therefore, the former is an appealing rejuvenation solution. Moreover, compared with single-level (OS and AS) rejuvenation models, two-level rejuvenation indeed improves the user experience. Concretely, the values of three metrics achieve 80.20% and 14.45%,83.39% and 98.45%, 0.004% and 0.048% improvements, respectively.

The irrelevance coverage model (ICM) is an extension of the imperfect fault coverage model (IFCM), which considers both uncovered failure and component irrelevance. In the ICM, an irrelevant component cannot occur an uncovered failure since it will be isolated (shutdown) from the system. In traditional ICM, the irrelevant component is triggered by a covered component failure. However, in the multi-state system (MSS), the degrade state of the operational components may also cause the other component to be irrelevant. To address this issue, the minimal irrelevance trigger (MIT) is redefined for the MSS by analyzing the relation between component states and system demand. Further, we extend the ICM to the MSS. We apply multi-state multi-valued decision diagram (MMDD) to calculate the reliability of the MSS in the ICM. The experimental result shows that not only the failure of component but also the deterioration of component may lead to component becoming irrelevant in the MSS.

Binary code similarity detection for cross-platform is widely used in plagiarism detection, malware detection and vulnerability search, aiming to detect whether two binary functions over different platforms are similar. Existing cross-architecture approaches mainly rely on the approximate matching calculation of complex high-dimensional features, such as graph, which are inevitably slow and unsuitable for large-scale applications. To solve this problem, we propose a novel approach based on index table called CBSDI, improving efficiency by screening a batch of mismatched functions before similarity detection. We select three features and compare them across architectures to select the most appropriate one to construct the index table, and this table can be embedded in other tools. The evaluation shows that the index table can roughly cut the computational costs in half when there are few errors. Moreover, compared with the related works in the literature, our proposed approach can improve not only the efficiency but also the accuracy.

In recent years, the rapid development of deep learning is attributed to the large amount of labeled data brought by the digital age. When there is no labeled data available in some application scenarios, domain adaptation can be used to transfer knowledge from the source domain with labeled data to the target domain without labeled data. In the process of domain adaptation, the target client requires direct access to the source data or model, which would lead to the risk of privacy leakage, e.g., Membership Inference Attacks (MIA). Attackers can collect the prediction vector of the model through black-box access to the source model, and then infer an individual’s membership in the source training dataset. To deal with this privacy issue, we propose a GAN-based Privacy-Preserving Unsupervised Domain Adaptation Framework. Specifically, the target client learns a conditional generator, sends the intermediate results perturbed by differential privacy to the source client, and the source client uses the source model to provide guidance for the generator so that the generator can generate the data corresponding to the input label that is the same as the data distribution in the target domain. We evaluate the performance of our proposed method on digital dataset and office-31dataset, which are popular domain adaptation benchmark datasets, and verify the security by the accuracy and F1-score of Membership Inference Attacks.

Anomaly events indicating the unhealthy status of the computer system are recorded in the system log (Syslog). Therefore, Syslog-based anomaly event detection is crucial for diagnosing system issues and problems. However, existing log-based anomaly detection approaches use raw and unstructured log entriesindependentlyandincompletely, i.e., without considering the context of each event and event metadata in the logs. They employ incomplete representation of unstructured log data, limiting the deep learning model’s capacity in the early stage, which tends to omit anomaly events and cause false alarms. In this work, we propose DeepSyslog, which represents Syslog with the context of log events and event metadata in the logs. Inspired by the sequence nature of the log stream, we employ unsupervised sentence embedding to extract the semantic and context information hidden in the log stream, rather than word embedding or one-hot embedding, which only capture the similarities between log words. The sentence embedding is further integrated with event metadata to form complete representations of Syslog, which can distinguish the anomaly caused by the correlated log entries and exceptional event metadata in the log. The simulation results on widely used log datasets show that DeepSyslog achieves high performance compared with the existing log-based anomaly event detection approaches.

This paper presents a feasibility and effective ap-proximation method to estimate the failure probability of the top event of a dynamic fault tree. The method is based on a minimal canonical form and uses a quantitative relationship between the smallest cut sequence and the entire sequence. Comparison with discrete-time Bayesian networks and Monte Carlo simulation methods, the validity of this method is assessed on two case studies approximating the probabilities of the top event of a Hypothetical Cardiac Assist System (HCAS) and a fictitious system. The case study results show that our method can achieve similar accuracy with smaller relative error and shorter execution time.

The reliability-redundancy allocation problem (RRAP) has been widely investigated during the last decade. In most of existing studies, component failures are assumed to be covered perfectly which means all faults can be timely detected, located, and isolated. However, the coverage could be imperfect in reality and a not-covered component failure may lead to system failure without constraint. In this paper, the RRAP is solved considering the imperfect fault coverage model (IFCM, only faulty components can be covered) and the irrelevance coverage model (ICM, both faulty and irrelevant components can be covered). It has been proved that an excessive level of redundancy may reduce the system reliability rather than improve it when the fault coverage is imperfect. Therefore, when the IFCM and the ICM are considered in the RRAP, in addition to resource constraints, the coverage model itself also limits the level of redundancy. Three benchmark problems are investigated in this paper. The genetic algorithm is adopted to solve the new mixed integer nonlinear programming problem. The results show that the optimal designs of system in the two coverage models are different from the existing researches that only consider the perfect fault coverage model. The redundant components used in the optimal solution are less than the existing studies. The advantage of the ICM over the IFCM is also verified in this paper.

In systems with imperfect fault coverage (IFC), all components are subject to uncovered failures, possibly threatening the whole system. Therefore, to improve the system reliability, it is important to timely detect, identify, and shut down the components that are no more relevant for the system operation. This article addresses quantitative evaluation of the relevance of components, assuming that they have independent and identically distributed lifetimes to characterize the impact of the system design only on the system reliability and energy consumption. To this end, the dynamic relevance measure is defined to characterize the irrelevant components in different stages of the system lifetime depending on the number of occurred component failures, supporting the evaluation of the probability that the system fails due to uncovered failures of irrelevant components. Moreover, the system reliability over time is also efficiently derived, both in the case that irrelevance is not considered and in the case that irrelevant components can be immediately isolated, notably supporting any general (i.e., non-Markovian) distribution for the failure time of components. Feasibility and effectiveness of the approach are assessed on two real-scale case studies addressing reliability evaluation of a flight control system and a multihop wireless sensor network.

Face anti-spoofing is becoming increasingly indispensable for face recognition systems, which are vulnerable to various spoofing attacks performed using fake photos and videos. In this paper, a novel “LDN-TOP representation followed by ProCRC classification” pipeline for face anti-spoofing is proposed. We use local directional number pattern (LDN) with the derivative-Gaussian mask to capture detailed appearance information resisting illumination variations and noises, which can influence the texture pattern distribution. To further capture motion information, we extend LDN to a spatial-temporal variant named local directional number pattern from three orthogonal planes (LDN- TOP). The multi-scale LDN- TOP capturing complete information is extracted from color images to generate the feature vector with powerful representation capacity. Finally, the feature vector is fed into the probabilistic collaborative representation based classifier (ProCRC) for face anti-spoofing. Our method is evaluated on three challenging public datasets, namely CASIA FASD, Replay-Attack database, and UVAD database using sequence-based evaluation protocol. The experimental results show that our method can achieve promising performance with 0.37% EER on CASIA and 5.73% HTER on UVAD. The performance on Replay-Attack database is also competitive.

Software aging, which is caused by Aging-Related Bugs (ARBs), refers to the phenomenon of performance degradation and eventual crash in long running systems. In order to discover and remove ARBs, ARB prediction is proposed. However, due to the low presence and reproducing difficulty of ARBs, it is usually difficult to collect sufficient ARB data within a project. Therefore, cross-project ARB prediction is proposed as a solution to build the target project's ARB predictor by using the labeled data from the source project. A key point for cross-project ARB prediction is to reduce distribution difference between source and target project. However, existing approaches mainly focus on the marginal distribution difference while somehow overlook the conditional distribution difference, and they mainly use random oversampling to alleviate the class imbalance which may lead to overfitting. To address these problems, we propose a new crossproject ARB prediction approach based on Joint Distribution Adaptation (JDA) and Improved Subclass Discriminant Analysis (ISDA), called JDA-ISDA. The key idea of JDA-ISDA is first to use JDA to reduce the marginal distribution and conditional distribution difference jointly and then apply ISDA to alleviate the severe class imbalance problem. A set of experiments are carried out on two large open-source projects with six different machine learning (ML) classifiers. The experimental results demonstrate that compared with the state-of-the-art Transfer Learning based Aging-related bug Prediction (TLAP) and Supervised Representation Learning Approach (SRLA), JDA-ISDA is much more robust to different ML classifiers than TLAP, and the average improvement in terms of the balance value can be achieved up to 31.8%, and JDA-ISDA also outperforms TLAP and SRLA on average when logistic regression is chosen as the classifier for best performance prediction.

The sending/receiving of data (data communication) is the most power consuming in wireless sensor networks (WSN) since the sensor nodes are depending on batteries not generally rechargeable characterized by limited capacity. Data compression is among the techniques that can help to reduce the amount of the exchanged data between wireless sensor nodes resulting in power saving. Nevertheless, there is a lack of effective methods to improve the efficiency of data compression algorithms and to increase nodes’ energy efficiency. In this paper, we proposed a novel lossless compression approach based on delta encoding and two occurrences character solving (T-RLE) algorithms. T-RLE is an optimization of the RLE algorithm, which aims to improve the compression ratio. This method will lead to less storage cost and less bandwidth to transmit the data, which positively affects the sensor nodes’ lifetime and the network lifetime in general. We used real deployment data (temperature and humidity) from the sensor scope project to evaluate the performance of our approach. The results showed a significant improvement compared with some traditional algorithms.

The most valuable resource in Wireless Sensors Networks (WSNs) is power consumption since it directly influences the lifetime of micro-sensors. So, there are several techniques have been proposed to solve this issue, such as routing protocol (energy-efficient medium access control (MAC)) and routing methods. Recently, compression techniques are used to reduce the size of transmitted data (less storage cost) and transmission time (less bandwidth needed) over wireless channels, which is the main power consumer in wireless sensor networks. In this paper, a new lossless compression scheme for Wireless Sensor Networks (WSNs) is proposed. It built on the nature of the data we dropped from a real-world deployment (Sensorscope project), which take into account the integer and the float part of the samples and compressed them separately using different methods. Our method overcomes many traditional algorithms, and it performed 90% and 79% better in term of compression ratio for Temperature and Relative Humidity, respectively.

In a phased-mission system (PMS), an uncovered component fault may lead to a mission failure regardless of the status of other components, and the reliability can be analyzed with traditional imperfect fault coverage model (IFCM). The IFCM, however, only considers the coverage of faulty components. Recently, an irrelevancy coverage model (ICM) is proposed to cover both faulty components and irrelevant components, but the analysis is limited to normal non-phased mission systems. This paper first demonstrates that, the coverage of irrelevant components is also important in PMSs, as an initially relevant component could also become irrelevant later due to the failures of other components, and an uncovered fault of irrelevant component may threaten the whole mission as well. A method to analyze the reliability of PMS in ICM is proposed using sum of disjoint products (SDP) technique. Experimental results demonstrate not only the effectiveness of the proposed reliability analysis method, but also that the ICM can achieve higher reliability than the IFCM for PMSs in general.

To prevent the same known vulnerabilities from affecting different firmware, searching known vulnerabilities in binary firmware across different architectures is crucial. Because the accuracy of existing cross-architecture vulnerability search methods is not high, we propose a staged approach based on support vector machine (SVM) and attributed control flow graph (ACFG) at the function level to improve the accuracy using prior knowledge. Furthermore, for efficiency, we utilize the k-nearest neighbor (kNN) algorithm to prune and SVM to refine in the function prefilter stage. Although the accuracy of the proposed method using kNN-SVM approach is slightly lower than the accuracy of the method using only SVM, its efficiency is significantly enhanced. We have implemented our approach CVSkSA to search several vulnerabilities in real-world firmware images. The experimental results show that the accuracy of the proposed method using kNN-SVM approach is close to the accuracy of the method using only SVM in most cases, while the former is approximately four times faster than the latter.

Optimizing energy efficient Virtual Machine Consolidation (VMC) in a cloud computing environment, which is a non-linear multi-objective NP-hard problem, plays a vital role in decreasing energy consumption, and increasing Quality of Service (QoS). In this paper, VMC is formulated as a multi-objective optimization problem, which has three conflicting objectives, power consumption, Service Level Agreements Violation (SLAV) and Mean Time Before Host Shutdown (MTBHS). We propose a multi-objective optimization algorithm based on Multi-Objective Sine Cosine Algorithm (MOSCA) for the VMC. We evaluate the performance of our model by applying two multi-objective algorithms, namely, Multi-Objective Evolutionary Algorithm based on Decomposition (MOEAD) and Non-dominated Sorting Genetic Algorithm (NSGAII). Our research mainly focus on two tasks, i.e.,evaluating and comparing the multi-objective algorithms to find out the optimal solution and develop a MOSCA based algorithm to solve the proposed VMC model. The simulation results illustrated that the propose multi-objective model meets the optimal solutions amongst the three conflicting objectives, which significantly reduces the power consumption, SLAV and maximize the MTBHS. It got the best performance according to the Multi-objective Optimization Problem (MOP) indicators.

Nowadays, biometric recognition has been widely used in real-world applications, but it has also brought potential privacy threats to users. Iris template protection enables an effective iris recognition while protecting personal privacy. In this paper, we propose a method for iris template protection based on randomized response technique and aggregated block information. Specifically, the iris data are first permuted according to an application-specific parameter; next, the permuted data are flipped using the randomized response technique; finally, the result is divided into blocks, and the aggregated information (i.e., the sum of all bits) in each block is calculated and stored instead of original iris data for privacy protection. We demonstrate that the proposed method supports the shifting and masking strategies for enhancing recognition performance. Moreover, the proposed method satisfies the three privacy requirements prescribed in ISO/IEC 24745: irreversibility, revocability and unlinkability. Experimental results show that the proposed method could effectively maintain the recognition performance (w.r.t. the original iris recognition system without privacy protection) on the iris database CASIA-IrisV3-Interval.

Biometrics have been widely studied in recent years, and they are increasingly employed in real-world applications. Meanwhile, a number of potential threats to the privacy of biometric data arise. Iris template protection demands that the privacy of iris data should be protected when performing iris recognition. According to the international standard ISO/IEC 24745, iris template protection should satisfy the irreversibility, revocability, and unlinkability. However, existing works about iris template protection demonstrate that it is difficult to satisfy the three privacy requirements simultaneously while supporting effective iris recognition. In this paper, we propose an iris template protection method based on local ranking. Specifically, the iris data are first XORed (Exclusive OR operation) with an application-specific string; next, we divide the results into blocks and then partition the blocks into groups. The blocks in each group are ranked according to their decimal values, and original blocks are transformed to their rank values for storage. We also extend the basic method to support the shifting strategy and masking strategy, which are two important strategies for iris recognition. We demonstrate that the proposed method satisfies the irreversibility, revocability, and unlinkability. Experimental results on typical iris datasets (i.e., CASIA-IrisV3-Interval, CASIA-IrisV4-Lamp, UBIRIS-V1-S1, and MMU-V1) show that the proposed method could maintain the recognition performance while protecting the privacy of iris data.

Energy efficiency and delay optimization are significant for the proliferation of wireless multimedia sensor network (WMSN). In this article, an energy‐efficient, delay‐efficient, hardware and software cooptimization platform is researched to minimize the energy cost while guaranteeing the deadline of the real‐time WMSN tasks. First, a multicore reconfigurable WMSN hardware platform is designed and implemented. This platform uses both the heterogeneous multicore architecture and the dynamic voltage and frequency scaling (DVFS) technique. By this means, the nodes can adjust the hardware characteristics dynamically in terms of the software run‐time contexts. Consequently, the software can be executed more efficiently with less energy cost and shorter execution time. Then, based on this hardware platform, an energy and delay multiobjective optimization algorithm and a DVFS adaption algorithm are investigated. These algorithms aim to search out the global energy optimization solution within the acceptable calculation time and strip the time redundancy in the task executing process. Thus, the energy efficiency of the WMSN node can be improved significantly even under strict constraint of the execution time. Simulation and real‐world experiments proved that the proposed approaches can decrease the energy cost by more than 29% compared to the traditional single‐core WMSN node. Moreover, the node can react quickly to the time‐sensitive events.

Facial landmark localization on images with occlusions is an important and challenging task in many visual applications. Recently, the cascaded pose regression has attracted increasing attention, since it achieved superior performance in terms of facial landmark localization under occlusions. However, such approach is sensitive to initialization, where an improper initialization will decrease the performance sharply. In this paper, we propose a novel initialization method to get a robust initial shape by analysing correlation of Local Binary Patterns (LBP) histograms between the estimated face and training faces. The shape of the training face that is most correlated with the estimated face, will be selected as the initialization for the regression. The selected shape is closer to the real shape of the estimated face, which makes the landmark localization more accurate. Besides, in order to make the initial shape more robust to occlusions, we propose a boosted smart restarts technique by checking location and occlusion jointly instead of checking location only. We show that the proposed method significantly improves performance over existing landmark localization methods on the challenging dataset of COFW. The experimental results demonstrate that the proposed method reduces error by 11.9% and failure cases by 20.8% on COFW dataset. Moreover, it detects face occlusions with 85/40% precision/recall.

In this paper, a novel multiscale ellipse descriptor (MED) method is proposed for shape description and matching. MED extracts the competitive features of shape contour by measuring the spatial location relationship between contour sample points and topology structure information of segmented multiscale zone. This method not only has the discriminative ability to describe the global and local information, but also is robustness to various linear (rotation, scale and translation transforms) and non-linear (irregular intra-class deformation) transforms. Experimental results on two public available databases consistently demonstrate that, our proposed method is effective and efficient when compared with other state-of-the-art shape retrieval benchmarks (such as 9.72% higher and 64 times faster than popular IDSC method on leaf 100 dataset).

Software testing is an important activity in quality assurance and it generates large amount of knowledge.Software testers need to gather domain knowledge to be able to successfully conduct a software testing activity.Not having a proper knowledge base within its own context by software testing environments cause software testers to query limited knowledge available or consult peer software testers, which would greatly impact on their decision-making process.Ontologies emerge as one of the more appropriate knowledge management tools for supporting knowledge representation, processing, storage and retrieval.Given great importance to knowledge for software testing, and the potential benefits of managing software testing knowledge, using semantic web technologies, ontology based knowledge management system is developed.A Software testing knowledge sharing ontology is designed to describe software testing domain knowledge.SPARQL is used as the query language to retrieve software testing knowledge from the semantic storage.Both Ontology experts and non-experts evaluated the developed ontology.We believe our software testing ontology can support other software organizations to improve the sharing of knowledge and learning practices.

Software aging is a phenomenon of progressive degradation of software execution environment caused by software faults. In this paper, we propose software life-extension as an operational countermeasure against software aging and present the mathematical foundations of software life-extension by means of stochastic modeling. A semi-Markov process is used to capture the behavior of a system with software life-extension and to analyze the system's availability and completion times of jobs running on it. The semi-Markov process can correctly model the time-based life-extension and allows us to derive the optimal trigger for starting life-extension in terms of system availability and mean job completion time. We also present an effective combination of software life-extension and software rejuvenation that can maximize the system availability compared with a system using either rejuvenation or software life-extension.

This paper addresses the coverage (including identification and isolation) of irrelevant components in systems with imperfect fault coverage (IFC). In fault-tolerant systems, a single not-covered component fault may thwart the automatic recovery mechanisms, and lead to a system or subsystem failure. The models that consider the effects of IFC are known as coverage models (CMs). In traditional CMs, except those considering functional dependency (a similar concept to relevancy but with different assumptions and semantics), coverage is typically limited to faulty components regardless of their relevancies. Consequently, an operational but irrelevant component will not be isolated, and may threaten the system by its future uncovered (not-covered) failures. Although the system is generally assumed to be coherent, which implies the relevancy of each component in the initial system state, the traditional CMs do not consider the fact that an initially relevant component could become irrelevant after the failures of other components. We propose the irrelevancy coverage model (ICM) to cover the irrelevant components in addition to the faulty components. In the ICM, a component will be isolated from the system whenever it becomes irrelevant (even it is not failed), such that its future not-covered failures will not affect the system anymore. By incorporating the coverage of irrelevant components, the ICM opens up a new cost-effective approach to improve system reliability without additional redundancy.

Design of storage system configuration is one of the key issues for providing dependable IT systems. An appropriate RAID storage configuration should consider both performance and availability. To assist the design, this paper presents the performability models for RAID10 and RAID6 that can be used to compare the configuration quantitatively. A performability advantage of RAID6 over RAID10 in sequential read access is discovered by the numerical study in conjunction with performance benchmark results.

Comprehensive analytic model for system availability analysis often confronts the largeness issue where a system designer cannot easily handle the model and the solution is not given in a feasible solution time. Hierarchical decomposition of a large state-space model gives a promising solution to the largeness issue when the model is decomposable. However, the decomposability of analytic model is not always manually tractable especially when the model is generated in an automated manner. In this paper, we propose an automated model composition technique from a system design to a hierarchical stochastic model which is the judicious combination of combinatorial and state-space models. In particular, from SysML-based system specifications, a top-level fault tree and associated stochastic reward nets are automatically generated in hierarchical manner. The obtained hierarchical stochastic model can be solved analytically considerably faster than monolithic state-space models. Through an illustrative example of three-tier web application system on a virtualized infrastructure, the accuracy and efficiency of the solution are evaluated in comparison to a monolithic state space model and a static fault tree.

This paper presents software life-extension, a new technique for counteracting software aging by preventive operation to extend the lifetime of software execution. Software aging is a phenomenon of progressive degradation of execution environment due to aging-related software faults and it might cause resource depletion resulting in system failures. To extend the lifetime of the software affected by aging, we use a virtual machine to execute the software and allocate additional memory to the virtual machine upon software aging detection. Although software life-extension is a temporal solution as it only postpones the occurrence of a failure, it provides a simple, cost-effective, and non-intrusive countermeasure to software aging. The feasibility and effectiveness of software life-extension are studied by the experiments on memcached, a widely adopted general-purpose in-memory cache server. From the experimental results, we present a Semi-Markov process (SMP) describing the general behavior of software life-extension and analyze the model which gives the prediction of the system availability as well as the user-perceived availability.

Large-scale disasters may cause simultaneous failures of many components in information systems. In the design for disaster recovery, operational procedures to recover from simultaneous component failures need to be determined so as to satisfy the time-to-recovery objective within the limited budget. For this purpose, it is beneficial to identify the minimal unacceptable combination of component failures which violates the requirements for time-to-recovery or the required cost. The identified combination allows us to know the limitation of the recovery capability of the designed recovery operation procedure. In this paper, we propose a technique to identify the minimal unacceptable combination of component failures by predicting the required time and cost for recovery from each combination of component failures. We synthesize analytic models from the description of recovery operation procedure in the form of SysML Activity Diagram, and solve the models to predict the time-to-recovery and the cost. The feasibility of the proposed technique is evaluated in an example of recovery operation procedures for a commercial database management system.

The voting gate, or k-out-of-n (k/n) gate, is a standard logic gate used in fault trees modelling fault-tolerant systems. It is traditionally expanded into a combination of AND and OR gates, and this expansion may result in combinatorial explosion problem in the calculation of minimal cut sets (MCSs) of the fault tree for even a not very big n, especially when the voting gate inputs are intermediate rather than basic events. In this paper we propose a set of reduction rules to simplify the voting gates without direct expanding, and also propose a concept of minimal cut vote (MCV) denoting a k/n gate whose inputs are all basic events and whose k-combinations are all MCSs of the fault tree. With the proposed reduction rules and MCV concept, the MCSs of fault trees can be evaluated and weeded more efficiently and the result can be represented in a more compact form. The results of experiments on practical fault trees with voting gates show that our method not only outperforms conventional MCS evaluation methods by several orders of magnitude but also provides performance comparably to that provided by binary decision tree (BDD) based algorithms.

The manual development of system reliability models such as fault trees could be costly and error prone in practice. In this paper, we focus on the problems of some traditional dynamic fault trees and present our static solutions to represent dynamic relations such as functional and sequential dependencies. The implementation of a tool for the automatic synthesis of our static fault trees from SysML system models is introduced.

This paper discusses how to model digital licenses as observational transition systems (OTSs) with CafeOBJ, a formal algebraic specification language. To extend the concept of licensing to cover various application domains of digital rights management, we first analyze the concepts of permission and obligation with some real-world examples which are not covered by current XML-based Rights Expression Languages (RELs), and then discuss how to formally specify licenses in terms of deontic and temporal logic with OTS/CafeOBJ method. Several important deontic and temporal modeling issues of licenses are also addressed for discussion. The proposed formal license language can be used not only for the formal specifications of licenses which capture both static observations and dynamic state transitions of the licenses, but also for the formal verification of licenses thanks to the executability and theorem proving facility of CafeOBJ.