Itinerary recommendation involves suggesting a sequence of Points of Interests (POIs) that users obtain maximum satisfaction under a time budget. Existing models have three challenges. First, they model user interest as non-time dependent, which cannot capture user interest appropriately because user interest can be contextual on time, e.g., interest in restaurants are likely higher during typical meal times. Second, they model the distance dependency of user interest as a linear one, which does not always adequately capture this relationship, e.g., it could be a cubic decay relationship. Finally, existing studies treat POI recommendation and itinerary optimisation as two separate problems, which can result in sub-optimal itinerary recommendations. In this paper, we propose a deep learning model that recommends POIs and constructs the itinerary simultaneously and in an integrated manner. It captures user dynamic interest and non-linear spatial dependencies in itinerary recommendations. The proposed model has two steps, where the candidate selection policy generates a set of personalised candidate POIs based on user interest and the itinerary construction step maximises user interest within budget time. To recommend an appropriate candidate set, we propose a multi-head, attention-based transformer to leverage periodic trends and recent activities to capture user dynamic preferences. We also introduce a new co-visiting patterns-based graph convolutional network (GCN) model to capture user non-linear spatial dependencies. To construct the full itinerary from the dynamic candidate sets, we apply greedy policy that incrementally constructs itineraries within the budget time which aims to maximise user interest and minimize queuing time. Experimental results show that the proposed deep learning model outperforms state-of-the-art baselines in itinerary recommendation in four theme parks and four cities datasets. The proposed model outperforms the baselines in itinerary recommendation from 7.79% to 26.28% on various datasets in terms of F1-score value. We also show that the proposed candidate generation approach outperforms the state-of-the-art next POI recommendation models in eight real datasets. The proposed model outperforms the baselines on average by 11.29 % in terms of F1-score@5 values and 9.08% in terms of F1-score@10 values. We have publicly shared our source code at GitHub 1 for the reproducibility of our proposed model.

Protecting software supply chains from malicious packages is paramount in the evolving landscape of software development. Attacks on the software supply chain involve attackers injecting harmful software into commonly used packages or libraries in a software repository. For instance, JavaScript uses Node Package Manager (NPM), and Python uses Python Package Index (PyPi) as their respective package repositories. In the past, NPM has had vulnerabilities such as the event-stream incident, where a malicious package was introduced into a popular NPM package, potentially impacting a wide range of projects. As the integration of third-party packages becomes increasingly ubiquitous in modern software development, accelerating the creation and deployment of applications, the need for a robust detection mechanism has become critical. On the other hand, due to the sheer volume of new packages being released daily, the task of identifying malicious packages presents a significant challenge. To address this issue, in this paper, we introduce a metadata-based malicious package detection model, MeMPtec. This model extracts a set of features from package metadata information. These extracted features are classified as either easy-to-manipulate (ETM) or difficult-to-manipulate (DTM) features based on monotonicity and restricted control properties. By utilising these metadata features, not only do we improve the effectiveness of detecting malicious packages, but also we demonstrate its resistance to adversarial attacks in comparison with existing state-of-the-art. Our experiments indicate a significant reduction in both false positives (up to 97.56%) and false negatives (up to 91.86%).

Point-of-interest (POI) recommendation is a challenging problem due to different contextual information and a wide variety of human mobility patterns. Prior studies focus on recommendation that considers user travel spatiotemporal and sequential patterns behaviours. These studies do not pay attention to user personal interests, which is a significant factor for POI recommendation. Besides user interests, queuing time also plays a significant role in affecting user mobility behaviour, e.g., having to queue a long time to enter a POI might reduce visitor's enjoyment. Recently, attention-based recurrent neural networks-based approaches show promising performance in the next POI recommendation task. However, they are limited to single head attention, which can have difficulty in finding the appropriate user mobility behaviours considering complex relationships among POI spatial distances, POI check-in time, user interests and POI queuing times. In this research work, we are the first to consider queuing time and user interest awareness factors for next POI recommendation. We demonstrate how it is non-trivial to recommend a next POI and simultaneously predict its queuing time. To solve this problem, we propose a multi-task, multi-head attention transformer model called TLR-M_UI. The model recommends the next POIs to the target users and predicts queuing time to access the POIs simultaneously by considering user mobility behaviours. The proposed model utilises POIs description-based user personal interest that can also solve the new categorical POI cold start problem. Extensive experiments on six real-world datasets show that the proposed models outperform the state-of-the-art baseline approaches in terms of precision, recall, and F1-score evaluation metrics. The model also predicts and minimizes the queuing time. For the reproducibility of the proposed model, we have publicly shared our implementation code at GitHub (https://github.com/sajalhalder/TLR-M_UI).

Abstract Personalized itinerary recommendation has garnered wide research interests for their ubiquitous applications. Recommending personalized itineraries is complex because of the large number of points of interest (POI) to consider in order to construct an itinerary based on visitors’ interest and preference, time budget and uncertain queuing time. Previous studies typically aim to plan itineraries that maximize POI popularity, visitors’ interest and minimize queuing time. However, existing solutions may not reflect visitor preferences because when creating itineraries, they prefer to recommend POIs with short prior visiting periods. These recommendations can conflict with real-life scenarios as visitors typically spend less time at POIs that they do not enjoy, thus leading to the inclusion of unsuitable POIs. Moreover, constructing itineraries based on selected POIs is a challenging and time-consuming process. Existing approaches involve searching through a large number of non-optimal, duplicate itineraries that are time-consuming to review and generate. To address these issues, we propose an adaptive Monte Carlo tree search (MCTS)-based reinforcement learning algorithmEffiTourRecusing an effective POI selection strategy by giving preference to POIs with long visiting times and short queuing times along with high POI popularity and visitor interest. In addition, to reduce non-optimal and duplicated itineraries generation, we propose an efficient MCTS search pruning technique to explore a smaller, more promising portion of solution space. Experiment results in real theme park datasets show clear advantages of our proposed method over baselines, where our method outperforms the current state-of-the-art by 20.89 to 52.32% in precision, 8.36 to 21.35% in F1-score and 40.00 to 67.64% in execution time.

Abstract Anomaly detection has been an essential and dynamic research area in the data mining. A wide range of applications including different social medias have adopted different state-of-the-art methods to identify anomaly for ensuring user’s security and privacy. The social network refers to a forum used by different groups of people to express their thoughts, communicate with each other, and share the content needed. This social networks also facilitate abnormal activities, spread fake news, rumours, misinformation, unsolicited messages, and propaganda post malicious links. Therefore, detection of abnormalities is one of the important data analysis activities for the identification of normal or abnormal users on the social networks. In this paper, we have developed a hybrid anomaly detection method named DT-SVMNB that cascades several machine learning algorithms including decision tree (C5.0), Support Vector Machine (SVM) and Naïve Bayesian classifier (NBC) for classifying normal and abnormal users in social networks. We have extracted a list of unique features derived from users’ profile and contents. Using two kinds of dataset with the selected features, the proposed machine learning model called DT-SVMNB is trained. Our model classifies users as depressed one or suicidal one in the social network. We have conducted an experiment of our model using synthetic and real datasets from social network. The performance analysis demonstrates around 98% accuracy which proves the effectiveness and efficiency of our proposed system.

Graph classification is an important topic in graph mining research since it has many applications, such as, social web mining, function prediction of molecules for drug design, XML document classification and anomaly detection in program flows. The key difficulty in graph classification lies in selecting a subset of optimal features from a huge number of structural features. The features need to be highly discriminative and small in numbers for better classification accuracy and running time. In this paper, we propose a novel feature selection framework that selects an optimal feature subset by removing redundant subgraphs. Topologically similar subgraphs have similar discriminative powers and coverage. We cluster these subgraphs and select one subgraph as a feature. We also propose an efficient topological similarity-based clustering method that guarantees the efficiency of our framework. Empirical results show that the proposed framework achieves significantly improved classification accuracy and running time in comparison with the state-of-the-art methods.