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This paper aims to validate the mission‐critical OSS (open‐source software) model acceptance process using a third‐order formative‐formative measuring model. A two‐stage formative‐formative model was used for partial least square analysis. It includes eight primary mission‐critical OSS adoption constructs and three second‐order (technological, organizational, and environmental). Although nomological validity was affirmed, the empirical findings show the absence of multicollinearity. This study will give organizations in critical industries enough information to understand the importance and weight of each component/factor of the mission‐critical OSS adoption model. The coefficient of determination R2 exceeds the minimum threshold (0.750), and all the hypotheses are significant. This study also contributed to the existing literature by identifying those factors that influence mission‐critical OSS adoption, allowing firms in critical sectors to better understand their mission‐critical OSS adoption issues. Cross‐cultural validation across a broad community is proposed to increase the validity and generalization of the sample scales. [ABSTRACT FROM AUTHOR]

Ransomware is an emerging category of malware that locks computer data via powerful cryptographic algorithms. The global propagation of ransomware is a serious threat for individuals and organizations. The banking sector and financial institutions are the prime targets of such ransomware attacks. In case of such an attack, the field of digital forensics helps in estimation of the severity and data loss caused by the attack. Traditional digital forensics investigations make use of static or behavioral analysis to detect malware in infected systems. However, these procedures are challenged by malware obfuscation techniques. Malicious processes can stay inactive and undetected if only a single memory dump is analyzed. Thus, there is a need to collect numerous memory dumps of an individual program that can help with comprehensive and accurate analysis. In this article, we have developed a framework for volatile memory acquisition at regular time intervals to analyze the behavior of individual processes in memory. Through memory forensics, salient features are extracted from the infected memory dumps. These features can be utilized to classify malicious and benign processes efficiently through machine learning as compared to conventional techniques. [ABSTRACT FROM AUTHOR]

Summary: Malware is a constant threat to the safety of the public Internet and private networks. It also affects the security of endpoint devices. An infected endpoint device can take part in aggressive or slow distributed denial of service attacks globally. Polymorphic malware has rendered traditional signature‐based detection ineffective. Hence the efforts to identify malware have been focused on behavioral modeling to identify and classify malware. This behavioral identification paved the way for artificial intelligence (AI) in cybersecurity. AI can detect a zero‐day attack and malware, but it suffers from several false positives. This article presents an extensive analysis of traditional and AI‐based methods for malware detection and related challenges. AI is vulnerable to attacks, such as dataset poisoning and adversarial data input, which can reduce model accuracy and increase false negatives. AI has helped to improve malware detection and reduce manual work through automation of feature extraction and feature selection. It is also beneficial to create models that are less prone to malware variations and capture the malicious behavior holistically. This article explores the transition of malware detection from traditional to AI‐based techniques. Furthermore, it also explains how some conventional approaches are still relevant today in terms of detection speed. [ABSTRACT FROM AUTHOR]

Summary: As time is progressing, the number and the complexity of methods adopted for launching distributed denial of service (DDoS) attacks are changing. Therefore, we propose a methodology for the development of a generalized machine learning (ML)‐based model for the detection of DDoS attacks. After exploring various attributes of the dataset chosen for this study, we propose an integrated feature selection (IFS) method which consists of three stages and integration of two different methods, that is, filter and embedded methods to select features which highly contribute to the detection of various types of DDoS attacks. We use light gradient boosting machine (LGBM) algorithm for training of the model for classification of benign and malicious flows. For ensuring satisfactory performance and generalized behavior of the developed model, we test it by passing records of unseen DDoS attack types. Several performance metrics are employed for the evaluation of the model. By comparing the performance of developed model against state‐of‐the‐art models, we state an improvement of around 20% for almost all the reported metrics. We also show that the performance of the model improves if feature space is reduced by 77%. Furthermore, the generalized behavior of the developed model is justified by demonstrating a trade‐off between high variance and high bias ML models. [ABSTRACT FROM AUTHOR]

Summary: Accurate application layer classification of Internet traffic has been a necessary requirement for various regulatory, control, and operational purposes of Internet service provider (ISP). Due to the dynamic and ever evolving nature of Internet applications generating a diverse mixture of Internet traffic, it has been necessary to apply deep packet inspection (DPI) techniques for traffic classification. DPI methods offer accuracy but degrade overall network throughput and thus cause problems in ensuring quality of service (QoS) and maintaining service‐level agreements. Moreover, Internet traffic is mostly end to end encrypted. This in turn limits the applicability of DPI techniques and renders them useless, unless the encryption tunnel is broken by the service provider which would risk violating user privacy. To address these trade‐offs between classification accuracy, performance, and user privacy, we resort to machine learning (ML)‐based algorithms. In this article, we apply three ensemble ML algorithms and report their performance metrics in the application layer classification of Internet traffic. [ABSTRACT FROM AUTHOR]

Quantum key distribution (QKD) has emerged as a secure solution of secret key distribution utilizing the well established theories of modern physics. Since its introduction in 1984, many interesting and innovative ideas have been proposed for QKD in order to improve the security and efficiency of the scheme keeping in view of its applications and practical implementation. High error rate QKD scheme for long distance communication - the so-called KMB09 protocol - is one such scheme which was designed to achieve longer communication distance in QKD, without compromising its security, by allowing the utilisation of higher dimensional photon states which is not possible with standard BB84 scheme. However the practical implementation of KMB09 protocol has not been presented yet because of its unique design. In this paper, we propose a framework for the practical implementation of QKD system that runs KMB09 protocol in two or more dimensions of photon states. We present the KMB09 based QKD system design and its simulation for practical implementation based on the encoding of secret bits in higher order Gaussian beam spatial modes. The proposed framework is specifically evaluated in terms of efficiency or success rate with two and four dimensions of photon states. We find that the simulation results of the proposed framework are inline with the numerical and analytical results of the same QKD model presented earlier. [ABSTRACT FROM AUTHOR]