Network Intrusion Detection Systems (NIDS) monitor networking environments for suspicious events that could compromise the availability, integrity, or confidentiality of the network’s resources. To ensure NIDSs play their vital roles, it is necessary to identify how they can be attacked by adopting a viewpoint similar to the adversary to identify vulnerabilities and defenses hiatus. Accordingly, effective countermeasures can be designed to thwart any potential attacks. Machine learning (ML) approaches have been adopted widely for network anomaly detection. However, it has been found that ML models are vulnerable to adversarial attacks. In such attacks, subtle perturbations are inserted to the original inputs at inference time in order to evade the classifier detection or at training time to degrade its performance. Yet, modeling adversarial attacks and the associated threats of employing the machine learning approaches for NIDSs was not addressed. One of the growing challenges is to avoid ML-based systems’ diversity and ensure their security and trust. In this paper, we conduct threat modeling for ML-based NIDS using STRIDE and Attack Tree approaches to identify the potential threats on different levels. We model the threats that can be potentially realized by exploiting vulnerabilities in ML algorithms through a simplified structural attack tree. To provide holistic threat modeling, we apply the STRIDE method to systems’ data flow to uncover further technical threats. Our models revealed a noticing of 46 possible threats to consider. These presented models can help to understand the different ways that a ML-based NIDS can be attacked; hence, hardening measures can be developed to prevent these potential attacks from achieving their goals.

Personally Identifiable Information (PII) is a key concept in privacy regulation. This form of information can provide revealing information about individuals, which may be collected and used for malicious purposes, such as social engineering and identity theft [1]. Consequently, privacy preserving legislations, such as GDPR place the responsibility of appropriately handling PII onto organisations who may process large amounts of personal data as part of their day to day operations. Therefore, it is necessary to develop processes that can provide privacy assurances but also to do so with increased automation and reliability [2]. This work will focus on assessing the ability of the Natural Language Processing tool, sentiment analysis and text classification algorithms to detect PII automatically, reliably and without too much complexity. To achieve this a dataset containing web pages from Newcastle University’s website, with a focus on staff profiles was created and manually labelled to indicate which sentences contained PII. The dataset was then used to train three text classification algorithms: Multinominal Naïve Bayes, Random Forest Classifier and LSTM model in order to predict the labels of an unseen portion of the dataset. The algorithms all performed well at detecting PII, with Random Forest achieving the highest accuracy at 96% and 96% F1-Score. Nevertheless, the models all mislabelled more sentences containing PII as not containing PII, than those which did not contain PII but were labelled as doing so.