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  1. Despite yielding benefits for organizations, infrastructure as code (IaC) scripts are susceptible to security weaknesses, such as hard-coded passwords. Existence of such security weaknesses necessitate integration of education materials related to secure development of IaC scripts. In this preliminary work, we describe our experiences of how application of authentic learning helped students learn about secure development of IaC scripts. Our paper shows education materials based on authentic learning to help students learn about secure IaC development.
    Free, publicly-accessible full text available July 7, 2023
  2. Context: Supervised learning-based projects (SLPs), i.e., software projects that use supervised learning algorithms, such as decision trees are useful for performing classification-related tasks. Yet, security weaknesses, such as the use of hard-coded passwords in SLPs, can make SLPs susceptible to security attacks. A characterization of security weaknesses in SLPs can help practitioners understand the security weaknesses that are frequent in SLPs and adopt adequate mitigation strategies. Objective: The goal of this paper is to help practitioners securely develop supervised learning-based projects by conducting an empirical study of security weaknesses in supervised learning-based projects. Methodology: We conduct an empirical study by quantifying the frequency of security weaknesses in 278 open source SLPs. Results: We identify 22 types of security weaknesses that occur in SLPs. We observe ‘use of potentially dangerous function’ to be the most frequently occurring security weakness in SLPs. Of the identified 3,964 security weaknesses, 23.79% and 40.49% respectively, appear for source code files used to train and test models. We also observe evidence of co-location, e.g., instances of command injection co-locates with instances of potentially dangerous function. Conclusion: Based on our findings, we advocate for a shift left approach for SLP development with security-focused code reviews, and applicationmore »of security static analysis.« less
    Free, publicly-accessible full text available June 1, 2023
  3. Machine learning (ML) operations or MLOps advocates for integration of DevOps- related practices into the ML development and deployment process. Adoption of MLOps can be hampered due to a lack of knowledge related to how development tasks can be automated. A characterization of bot usage in ML projects can help practitioners on the types of tasks that can be automated with bots, and apply that knowledge into their ML development and deployment process. To that end, we conduct a preliminary empirical study with 135 issues reported mined from 3 libraries related to deep learning: Keras, PyTorch, and Tensorflow. From our empirical study we observe 9 categories of tasks that are automated with bots. We conclude our work-in-progress paper by providing a list of lessons that we learned from our empirical study.
    Free, publicly-accessible full text available June 1, 2023
  4. Machine Learning (ML) analyze, and process data and develop patterns. In the case of cybersecurity, it helps to better analyze previous cyber attacks and develop proactive strategy to detect, prevent the security threats. Both ML and cybersecurity are important subjects in computing curriculum but ML for security is not well presented there. We design and develop case-study based portable labware on Google CoLab for ML to cybersecurity so that students can access, share, collaborate, and practice these hands-on labs anywhere and anytime without time tedious installation and configuration which will help students more focus on learning of concepts and getting more experience for hands-on problem solving skills.
  5. This Innovative Practice, work in progress (WIP) paper presents our experience related to two exercises that focus on automated security static analysis, a practice used to integrate security into development and operations (DevOps). The concept has gained popularity amongst information technology (IT) organizations. However, security-related concerns, such as security weaknesses in DevOps artifacts can cause serious consequences. Our preliminary findings indicate that (i) students positively perceive the introduced exercises; and (ii) the students perform well if they are provided necessary background on the exercises. Our WIP paper lays the groundwork to build course materials that will facilitate development, deployment, and dissemination of DevOps-related education materials that also incorporate cybersecurity concepts.
  6. Paiva, A.C.R. ; Cavalli, A.R. ; Ventura, Martins P. ; Perez-Castillo, R. (Ed.)
    The ubiquitous use of software in critical systems necessitates integrating cybersecurity concepts into the software engineering curriculum so that students studying software engineering have adequate knowledge to securely develop software projects, which could potentially secure critical systems. An experience report of developing and conducting a course can help educators to gain an understanding of student preferences on topics related to secure software development. We provide an experience report related to the ‘Secure Software Development’ course conducted at Tennessee Technological University. We discuss student motivations, as well as positive and negative perceptions of students towards exercises. Based on our findings, we recommend educators to integrate real-world exercises into a secure software development course with careful consideration of tool documentation, balance in exercise diversity, and student background.
  7. With the rapid technological advancement, security has become a major issue due to the increase in malware activity that poses a serious threat to the security and safety of both computer systems and stakeholders. To maintain stakeholder’s, particularly, end user’s security, protecting the data from fraudulent efforts is one of the most pressing concerns. A set of malicious programming code, scripts, active content, or intrusive software that is designed to destroy intended computer systems and programs or mobile and web applications is referred to as malware. According to a study, naive users are unable to distinguish between malicious and benign applications. Thus, computer systems and mobile applications should be designed to detect malicious activities towards protecting the stakeholders. A number of algorithms are available to detect malware activities by utilizing novel concepts including Artificial Intelligence, Machine Learning, and Deep Learning. In this study, we emphasize Artificial Intelligence (AI) based techniques for detecting and preventing malware activity. We present a detailed review of current malware detection technologies, their shortcomings, and ways to improve efficiency. Our study shows that adopting futuristic approaches for the development of malware detection applications shall provide significant advantages. The comprehension of this synthesis shall help researchers formore »further research on malware detection and prevention using AI.« less
  8. Traditional network intrusion detection approaches encounter feasibility and sustainability issues to combat modern, sophisticated, and unpredictable security attacks. Deep neural networks (DNN) have been successfully applied for intrusion detection problems. The optimal use of DNN-based classifiers requires careful tuning of the hyper-parameters. Manually tuning the hyperparameters is tedious, time-consuming, and computationally expensive. Hence, there is a need for an automatic technique to find optimal hyperparameters for the best use of DNN in intrusion detection. This paper proposes a novel Bayesian optimization-based framework for the automatic optimization of hyperparameters, ensuring the best DNN architecture. We evaluated the performance of the proposed framework on NSL-KDD, a benchmark dataset for network intrusion detection. The experimental results show the framework’s effectiveness as the resultant DNN architecture demonstrates significantly higher intrusion detection performance than the random search optimization-based approach in terms of accuracy, precision, recall, and f1-score.
  9. The ubiquitous usage of robots in modern society necessitates secure development of robotics systems. Practitioners who engage in robot development can benefit from a systematic study that investigates the categories of vulnerabilities that appear in robotics systems. The goal of this paper is to help practitioners mitigate vulnerabilities in robotics systems by conducting an empirical study of vulnerabilities in robotics systems. We conduct an empirical study where we analyze 176 robotics-related vulnerabilities collected from the Robot Vulnerability Database (RVD). Our findings show that: (i) robotics-related vulnerabilities can be classified into nine categories; (ii) memory-related vulnerabilities are the most frequent category, (iii) 92.6% of the reported vulnerabilities are software-related, and (iv) software components in robotics systems include more critical vulnerabilities compared to that of hardware components. Based on our findings, we provide a list of development activities that can be used to mitigate vulnerabilities for robotics systems.
  10. Network intrusion detection systems (NIDSs) play an essential role in the defense of computer networks by identifying a computer networks' unauthorized access and investigating potential security breaches. Traditional NIDSs encounters difficulties to combat newly created sophisticated and unpredictable security attacks. Hence, there is an increasing need for automatic intrusion detection solution that can detect malicious activities more accurately and prevent high false alarm rates (FPR). In this paper, we propose a novel network intrusion detection framework using a deep neural network based on the pretrained VGG-16 architecture. The framework, TL-NID (Transfer Learning for Network Intrusion Detection), is a two-step process where features are extracted in the first step, using VGG-16 pre-trained on ImageNet dataset and in the 2 nd step a deep neural network is applied to the extracted features for classification. We applied TL-NID on NSL-KDD, a benchmark dataset for network intrusion, to evaluate the performance of the proposed framework. The experimental results show that our proposed method can effectively learn from the NSL-KDD dataset with producing a realistic performance in terms of accuracy, precision, recall, and false alarm. This study also aims to motivate security researchers to exploit different state-of-the-art pre-trained models for network intrusion detection problems throughmore »valuable knowledge transfer.« less