In recent years, the growing complexity and scale of source code have rendered manual software vulnerability detection increasingly impractical. To address this challenge, automated approaches leveraging machine learning and code embeddings have gained substantial attention. This study investigates the optimal combination of code embedding techniques and machine learning classifiers for vulnerability detection in Python source code. We evaluate three embedding techniques, i.e., Word2Vec, CodeBERT, and GraphCodeBERT alongside two deep learning classifiers, i.e., Bidirectional Long Short-Term Memory (BiLSTM) networks and Convolutional Neural Networks (CNN). While CNN paired with GraphCodeBERT exhibits strong performance, the BiLSTM model using Word2Vec consistently achieves superior overall results. These findings suggest that, despite the advanced architectures of recent models like CodeBERT and GraphCodeBERT, classical embeddings such as Word2Vec, when used with sequence-based models like BiLSTM, can offer a slight yet consistent performance advantage. The study underscores the critical importance of selecting appropriate combinations of embeddings and classifiers to enhance the effectiveness of automated vulnerability detection systems, particularly for Python source code.

Software vulnerabilities are a fundamental reason for the prevalence of cyber attacks and their identification is a crucial yet challenging problem in cyber security. In this paper, we apply and compare different machine learning algorithms for source code vulnerability detection specifically for Python programming language. Our experimental evaluation demonstrates that our Bidirectional Long Short-Term Memory (BiLSTM) model achieves a remarkable performance (average Accuracy = 98.6%, average F-Score = 94.7%, average Precision = 96.2%, average Recall = 93.3%, average ROC = 99.3%), thereby, establishing a new benchmark for vulnerability detection in Python source code.

BGP Blackholing is an effective mitigation solution for networks to counter the frequent Distributed Denial of Service (DDoS) attacks. It enables to drop all network traffic that is directed towards a particular victim prefix under DDoS attack, ideally, as close to the source as possible. Despite its huge importance in the Internet, there is no tool available for the real-time visualization of BGP Blackholing activity. Visualization is one of the most powerful techniques for network operators to monitor network activity. From discovering successful network topology to expose anomalous behaviors in networks, easy-to-use visualizations are powerful weapons to capture important patterns on the Internet traffic[1, 5]. In this work, we propose a first real-time BGP Blackholing Visibility Tool (named as SmartX BGP-BVT) to detect and visualize community based BGP Blackholing on live BGP data. This tool will be helpful for network operators and researchers interested in BGP Blackholing service and DDoS mitigation in the Internet.

Accurate Internet traffic predictions can provide support to network operators for applications such as traffic engineering, bandwidth allocation, anomaly detection, etc. We apply and compare different forecasting techniques (traditional and machine learning-based techniques) on real BGP data that is collected from two well-known Internet exchange points (IXPs) to derive BGP future volume-based predictions. Our experimental evaluation shows that multivariate Bayesian Ridge outperforms all other forecasting techniques we consider. Through univariate LSTM, we are able to predict new BGP volume-based features. Furthermore, to study the impact of dataset size on BGP forecasting, we perform experiments on three BGP dataset sizes, i.e., Short (one-month), Medium (three-months), and Long (five-months) Periods. Our results show that the Short-Period BGP dataset seems to be sufficient for getting accurate predictions. We also present a use case study (forecast Google Leak anomaly) that supports our experimental evaluations. We provide our collected BGP datasets publically which will be helpful to perform further research experiments and analysis regarding BGP traffic predictions.

Forecasting network traffic is a challenging task for better network management. In this poster, we present a Border Gateway Protocol (BGP) traffic volume prediction framework that uses real BGP data from two famous Internet exchange points (IXPs) to train the LSTM network and generate future volume-based predictions. Our experimental evaluation shows that LSTM can indeed be used to predict BGP traffic volume with a very low prediction errors.