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Abstract Background Computer-aided methods for analyzing white blood cells (WBC) are popular due to the complexity of the manual alternatives. Recent works have shown highly accurate segmentation and detection of white blood cells from microscopic blood images. However, the classification of the observed cells is still a challenge, in part due to the distribution of the five types that affect the condition of the immune system. Methods (i) This work proposes W-Net, a CNN-based method for WBC classification. We evaluate W-Net on a real-world large-scale dataset that includes 6562 real images of the five WBC types. (ii) For further benefits, we generate synthetic WBC images using Generative Adversarial Network to be used for education and research purposes through sharing. Results (i) W-Net achieves an average accuracy of 97%. In comparison to state-of-the-art methods in the field of WBC classification, we show that W-Net outperforms other CNN- and RNN-based model architectures. Moreover, we show the benefits of using pre-trained W-Net in a transfer learning context when fine-tuned to specific task or accommodating another dataset. (ii) The synthetic WBC images are confirmed by experiments and a domain expert to have a high degree of similarity to the original images. The pre-trained W-Net and the generated WBC dataset are available for the community to facilitate reproducibility and follow up research work. Conclusion This work proposed W-Net, a CNN-based architecture with a small number of layers, to accurately classify the five WBC types. We evaluated W-Net on a real-world large-scale dataset and addressed several challenges such as the transfer learning property and the class imbalance. W-Net achieved an average classification accuracy of 97%. We synthesized a dataset of new WBC image samples using DCGAN, which we released to the public for education and research purposes.

Machine Learning (ML) based Network Intrusion Systems (NIDSs) operate on flow features which are obtained from flow exporting protocols (i.e., NetFlow). Recent success of ML and Deep Learning (DL) based NIDS solutions assume such flow information (e.g., avg. packet size) is obtained from all packets of the flow. However, often in practice flow exporter is deployed on commodity devices where packet sampling is inevitable. As a result, applicability of such ML based NIDS solutions in the presence of sampling (i.e., when flow information is obtained from sampled set of packets instead of full traffic) is an open question. In this study, we explore the impact of packet sampling on the performance and efficiency of ML-based NIDSs. Unlike previous work, our proposed evaluation procedure is immune to different settings of flow export stage. Hence, it can provide a robust evaluation of NIDS even in the presence of sampling. Through sampling experiments we established that malicious flows with shorter size (i.e., number of packets) are likely to go unnoticed even with mild sampling rates such as 1/10 and 1/100. Next, using the proposed evaluation procedure we investigated the impact of various sampling techniques on NIDS detection rate and false alarm rate. Detection rate and false alarm rate is computed for three sampling rates (i.e., 1/10, 1/100, 1/1000), for four different sampling techniques and for three (two tree-based, one deep learning based) classifiers. Experimental results show that systematic linear sampler - SketFlow performs better compared to non-linear samplers such as Sketch Guided and Fast Filtered sampling. We also found that random forest classifier with SketchFlow sampling was a better combination. The combination showed higher detection rate and lower false alarm rate across multiple sampling rates compared to other sampler-classifier combinations. Our results are consistent in multiple sampling rates, exceptional case is observed for Sketch Guided Sampling (SGS) as it caused a drastic performance drop when sampling rate was changed from 1/100 to 1/1000. Our results provide valuable insights for network practitioners and researchers regarding on how packet sampling effects ML-based NIDS performance. In this regard full source code for sampling and ML experiments has been released: github.com/Jumabek/sampledFlowMeter and github.com/Jumabek/nids-with-sampling

Recent natural language processing (NLP) techniques have accomplished high performance on benchmark data sets, primarily due to the significant improvement in the performance of deep learning. The advances in the research community have led to great enhancements in state-of-the-art production systems for NLP tasks, such as virtual assistants, speech recognition, and sentiment analysis. However, such NLP systems still often fail when tested with adversarial attacks. The initial lack of robustness exposed troubling gaps in current models’ language understanding capabilities, creating problems when NLP systems are deployed in real life. In this paper, we present a structured overview of NLP robustness research by summarizing the literature in a systemic way across various dimensions. We then take a deep-dive into the various dimensions of robustness, across techniques, metrics, embedding, and benchmarks. Finally, we argue that robustness should be multi-dimensional, provide insights into current research, identify gaps in the literature to suggest directions worth pursuing to address these gaps

Abstract Blockchain‐based audit systems suffer from low scalability and high message complexity. The root cause of these shortcomings is the use of “Practical Byzantine Fault Tolerance” (PBFT) consensus protocol in those systems. Alternatives to PBFT have not been used in blockchain‐based audit systems due to the limited knowledge about their functional and operational requirements. Currently, no blockchain testbed supports the execution and benchmarking of different consensus protocols in a unified testing environment. This paper demonstrates building a blockchain testbed that supports the execution of five state‐of‐the‐art consensus protocols in a blockchain system; namely PBFT, Proof‐of‐Work (PoW), Proof‐of‐Stake (PoS), Proof‐of‐Elapsed Time (PoET), and Clique. Performance evaluation of those consensus algorithms is carried out using data from a real‐world audit system. These results show that the Clique protocol is best suited for blockchain‐based audit systems, based on scalability features.

Proxy servers act as an intermediary and a gateway between users and other servers on the Internet, and have many beneficial applications targeting the privacy of users, including bypassing server-side blocking, regional restrictions, etc. Despite the beneficial applications of proxies, they are also used by adversaries to hide their identity and to launch many attacks. As such, many websites restrict access from proxies, resulting in blacklists to filter out those proxies and to aid in their blocking. In this work, we explore the ecosystem of proxies by understanding their affinities and distributions comparatively. We compare residential and open proxies in various ways, including country-level and city-level analyses to highlight their geospatial distributions, similarities, and differences against a large number of blacklists and categories therein, i.e., spam and maliciousness analysis, to understand their characteristics and attributes. We conclude that, while aiming to achieve the same goal, residential and open proxies still have distinct characteristics warranting considering them separately for the role they play in the larger Internet ecosystem. Moreover, we highlight the correlation of proxy locality distribution and five country-level characteristics, such as their Internet censorship, political stability, and Gross Domestic Product (GDP).

Enterprises are increasingly considering security as an added cost, making it necessary for those enterprises to see a tangible incentive in adopting security measures. Despite data breach laws, prior studies have suggested that only 4% of reported data breach incidents have resulted in litigation in federal courts, showing the limited legal ramifications of security breaches and vulnerabilities. In this paper, we study the hidden cost of software vulnerabilities reported in the National Vulnerability Database (NVD) through stock price analysis. We perform a high-fidelity data augmentation to ensure data reliability and to estimate vulnerability disclosure dates as a baseline for estimating the implication of software vulnerabilities. We further build a model for stock price prediction using the nonlinear autoregressive neural network with exogenous factors (NARX) Neural Network model to estimate the effect of vulnerability disclosure on the stock price. Compared to prior work, which relies on linear regression models, our approach is shown to provide better prediction performance. Our analysis also shows that the effect of vulnerabilities on vendors varies, and greatly depends on the specific software industry. Whereas some industries are shown statistically to be affected negatively by the release of software vulnerabilities, even when those vulnerabilities are not broadly covered by the media, some others were not affected at all.

We are facing a big data world, embedded with interconnected IoT (Internet of Things) devices that generate large volumes of data. They pose a significant challenge to academia and industries focused on digital security: A variety of new malware and other threats are emerging at a fast pace, and existing preventive methods are struggling to deal with them within the golden time by depending solely on the known attack signature. Recently, there has been considerable advancement in computing, particularly in the field of Artificial Intelligence (AI). Advanced technologies such as Machine Learning and Deep Learning are being actively deployed in cyber security, and new results and issues have been reported.

Using runtime execution artifacts to identify malware and its associated “family” is an established technique in the security domain. Many papers in the literature rely on explicit features derived from network, file system, or registry interaction. While effective, the use of these fine-granularity data points makes these techniques computationally expensive. Moreover, the signatures and heuristics are often circumvented by subsequent malware authors. In this work, we propose Chatter, a system that is concerned only with the order in which high-level system events take place. Individual events are mapped onto an alphabet and execution traces are captured via terse concatenations of those letters. Then, leveraging an analyst labeled corpus of malware, n-gram document classification techniques are applied to produce a classifier predicting malware family. This paper describes that technique and its proof-of-concept evaluation. In its prototype form only network events are considered and eleven malware families are used. We show the technique achieves 83%-94% accuracy in isolation and makes non-trivial performance improvements when integrated with a baseline classifier of combined order features to reach an accuracy of up to 98.8%.

Successful software authorship de-anonymization has both software forensics applications and privacy implications. However, the process requires an efficient extraction of authorship attributes. The extraction of such attributes is very challenging, due to various software code formats from executable binaries with different toolchain provenance to source code with different programming languages. Moreover, the quality of attributes is bounded by the availability of software samples to a certain number of samples per author and a specific size for software samples. To this end, this work proposes a deep Learning-based approach for software authorship attribution, that facilitates large-scale, format-independent, language-oblivious, and obfuscation-resilient software authorship identification. This proposed approach incorporates the process of learning deep authorship attribution using a recurrent neural network, and ensemble random forest classifier for scalability to de-anonymize programmers. Comprehensive experiments are conducted to evaluate the proposed approach over the entire Google Code Jam (GCJ) dataset across all years (from 2008 to 2016) and over real-world code samples from 1,987 public repositories on GitHub. The results of our work show high accuracy despite requiring a smaller number of samples per author. Experimenting with source-code, our approach allows us to identify 8,903 GCJ authors, the largest-scale dataset used by far, with an accuracy of 92.3%. Using the real-world dataset, we achieved an identification accuracy of 94.38% for 745 C programmers on GitHub. Moreover, the proposed approach is resilient to language-specifics, and thus it can identify authors of four programming languages (e.g., C, C++, Java, and Python), and authors writing in mixed languages (e.g., Java/C++, Python/C++). Finally, our system is resistant to sophisticated obfuscation (e.g., using C Tigress) with an accuracy of 93.42% for a set of 120 authors. Experimenting with executable binaries, our approach achieves 95.74% for identifying 1,500 programmers of software binaries. Similar results were obtained when software binaries are generated with different compilation options, optimization levels, and removing of symbol information. Moreover, our approach achieves 93.86% for identifying 1,500 programmers of obfuscated binaries using all features adopted in Obfuscator-LLVM tool. [ABSTRACT FROM AUTHOR]

Enterprises are increasingly considering security as an added cost, making it necessary for those enterprises to see a tangible incentive in adopting security measures. Despite data breach laws, prior studies have suggested that only 4% of reported data breach incidents have resulted in litigation in federal courts, showing the limited legal ramifications of security breaches and vulnerabilities. In this paper, we study the hidden cost of software vulnerabilities reported in the National Vulnerability Database (NVD) through stock price analysis. We perform a high-fidelity data augmentation to ensure data reliability and to estimate vulnerability disclosure dates as a baseline for estimating the implication of software vulnerabilities. We further build a model for stock price prediction using the nonlinear autoregressive neural network with exogenous factors (NARX) Neural Network model to estimate the effect of vulnerability disclosure on the stock price. Compared to prior work, which relies on linear regression models, our approach is shown to provide better prediction performance. Our analysis also shows that the effect of vulnerabilities on vendors varies, and greatly depends on the specific software industry. Whereas some industries are shown statistically to be affected negatively by the release of software vulnerabilities, even when those vulnerabilities are not broadly covered by the media, some others were not affected at all. [ABSTRACT FROM AUTHOR]

Sensor nodes are vulnerable to compromise due to their unattended deployment. The low cost requirement of the sensor node precludes the use of expensive tamper resistant hardware for sensor physical protection. Therefore, the adversary can easily reprogram the compromised sensors and deviate their functionality. We address this problem by proposing software-based remote code attestation mechanisms for different WSN scenarios, which can limit the misuse introduced by malicious attackers. An attestation scheme of node-to-base station is proposed and generalized to two distributed group based attestation schemes. Our group based attestation schemes localize the attestation process and substitute the need for running multi hops attestation for each node out of the base station range. The simulation results show that proposed schemes are feasible and well-suited for wireless sensors. Moreover, we analyze the security of the proposed protocols and show that, unlike the previously related proposed work in literature, the proposed schemes loosely depend on the accurate measurement of the execution time. [ABSTRACT FROM AUTHOR]

A group signature scheme allows a group member of a group to sign messages on behalf of the group anonymously. In case of dispute, a special entity of the group, group manager, can reveal the signer of a valid group signature. In 2005, Zhang et al. proposed a new group signature with forward security based on their earlier scheme in ICICS 2003. Recently, Zhou et al. proposed a dynamic group signature scheme with forward security at GCC 2007, In the year of 2008, Zhang and Geung pointed out the scheme is insecure and suggested an improvement. In this paper, we analyze a security analysis of Zhang et al.'s group signature scheme and Zhou et al.'s group signature scheme. We also discuss why the improved Zhou et al.'s scheme by Zhang et al. is still insecure. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a grid-based key pre-distribution scheme in wireless sensor networks, which aims to improve the connectivity and resiliency while maintaining a reasonable overhead. We consider simplification of the key establishment logic and enhancement of the connectivity via plat polynomial assignment on a three-dimensional grid for node allocation and keying material assignment. We demonstrate that our scheme results in improvements via a detailed discussion on the connectivity, resource usage, security features and resiliency. A comparison with other relevant works from the literature along with a demonstrated implementation on typical sensor nodes shows the feasibility of the introduced scheme and its applicability for large networks. [ABSTRACT FROM AUTHOR]

In this paper, the random key pre-distribution scheme introduced in ACM CCS'02 by Eschenauer and Gligor is reexamined, and a generalized form of key establishment is introduced. As the communication overhead is one of the most critical constraints of any successful protocol design, we introduce an alternative scheme in which the connectivity is maintained at the same level as in the original work, while the communication overhead is reduced by about 40% of the original overhead, for various carefully chosen parameters. The main modification relies on the use of a two-level key pool design and two round assignment/key establishment phases. Further analysis demonstrates the efficiency of our modification. [ABSTRACT FROM AUTHOR]