The development of robust anomaly-based network detection systems, which are preferred over static signal-based network intrusion, is vital for cybersecurity. The development of a flexible and dynamic security system is required to tackle the new attacks. Current intrusion detection systems (IDSs) suffer to attain both the high detection rate and low false alarm rate. To address this issue, in this paper, we propose an IDS using different machine learning (ML) and deep learning (DL) models. This paper presents a comparative analysis of different ML models and DL models on Coburg intrusion detection datasets (CIDDSs). First, we compare different ML- and DL-based models on the CIDDS dataset. Second, we propose an ensemble model that combines the best ML and DL models to achieve high-performance metrics. Finally, we benchmarked our best models with the CIC-IDS2017 dataset and compared them with state-of-the-art models. While the popular IDS datasets like KDD99 and NSL-KDD fail to represent the recent attacks and suffer from network biases, CIDDS, used in this research, encompasses labeled flow-based data in a simulated office environment with both updated attacks and normal usage. Furthermore, both accuracy and interpretability must be considered while implementing AI models. Both ML and DL models achieved an accuracy of 99% on the CIDDS dataset with a high detection rate, low false alarm rate, and relatively low training costs. Feature importance was also studied using the Classification and regression tree (CART) model. Our models performed well in 10-fold cross-validation and independent testing. CART and convolutional neural network (CNN) with embedding achieved slightly better performance on the CIC-IDS2017 dataset compared to previous models. Together, these results suggest that both ML and DL methods are robust and complementary techniques as an effective network intrusion detection system.

As Internet of Things (IoT) networks expand globally with an annual increase of active devices, providing better safeguards to threats is becoming more prominent. An intrusion detection system (IDS) is the most viable solution that mitigates the threats of cyberattacks. Given the many constraints of the ever-changing network environment of IoT devices, an effective yet lightweight IDS is required to detect cyber anomalies and categorize various cyberattacks. Additionally, most publicly available datasets used for research do not reflect the recent network behaviors, nor are they made from IoT networks. To address these issues, in this paper, we have the following contributions: (1) we create a dataset from IoT networks, namely, the Center for Cyber Defense (CCD) IoT Network Intrusion Dataset V1 (CCD-INID-V1); (2) we propose a hybrid lightweight form of IDS—an embedded model (EM) for feature selection and a convolutional neural network (CNN) for attack detection and classification. The proposed method has two models: (a) RCNN: Random Forest (RF) is combined with CNN and (b) XCNN: eXtreme Gradient Boosting (XGBoost) is combined with CNN. RF and XGBoost are the embedded models to reduce less impactful features. (3) We attempt anomaly (binary) classifications and attack-based (multiclass) classifications on CCD-INID-V1 and two other IoT datasets, the detection_of_IoT_botnet_attacks_N_BaIoT dataset (Balot) and the CIRA-CIC-DoHBrw-2020 dataset (DoH20), to explore the effectiveness of these learning-based security models. Using RCNN, we achieved an Area under the Receiver Characteristic Operator (ROC) Curve (AUC) score of 0.956 with a runtime of 32.28 s on CCD-INID-V1, 0.999 with a runtime of 71.46 s on Balot, and 0.986 with a runtime of 35.45 s on DoH20. Using XCNN, we achieved an AUC score of 0.998 with a runtime of 51.38 s for CCD-INID-V1, 0.999 with a runtime of 72.12 s for Balot, and 0.999 with a runtime of 72.91 s for DoH20. Compared to KNN, XCNN required 86.98% less computational time, and RCNN required 91.74% less computational time to achieve equal or better accurate anomaly detections. We find XCNN and RCNN are consistently efficient and handle scalability well; in particular, 1000 times faster than KNN when dealing with a relatively larger dataset-Balot. Finally, we highlight RCNN and XCNN’s ability to accurately detect anomalies with a significant reduction in computational time. This advantage grants flexibility for the IDS placement strategy. Our IDS can be placed at a central server as well as resource-constrained edge devices. Our lightweight IDS requires low train time and hence decreases reaction time to zero-day attacks.

Phosphorylation, which is mediated by protein kinases and opposed by protein phosphatases, is an important post-translational modification that regulates many cellular processes, including cellular metabolism, cell migration, and cell division. Due to its essential role in cellular physiology, a great deal of attention has been devoted to identifying sites of phosphorylation on cellular proteins and understanding how modification of these sites affects their cellular functions. This has led to the development of several computational methods designed to predict sites of phosphorylation based on a protein’s primary amino acid sequence. In contrast, much less attention has been paid to dephosphorylation and its role in regulating the phosphorylation status of proteins inside cells. Indeed, to date, dephosphorylation site prediction tools have been restricted to a few tyrosine phosphatases. To fill this knowledge gap, we have employed a transfer learning strategy to develop a deep learning-based model to predict sites that are likely to be dephosphorylated. Based on independent test results, our model, which we termed DTL-DephosSite, achieved efficiency scores for phosphoserine/phosphothreonine residues of 84%, 84% and 0.68 with respect to sensitivity (SN), specificity (SP) and Matthew’s correlation coefficient (MCC). Similarly, DTL-DephosSite exhibited efficiency scores of 75%, 88% and 0.64 for phosphotyrosine residues with respect to SN, SP, and MCC.

Anomaly detection and multi-attack classification are major concerns for cyber defense. Several publicly available datasets have been used extensively for the evaluation of Intrusion Detection Systems (IDSs). However, most of the publicly available datasets may not contain attack scenarios based on evolving threats. The development of a robust network intrusion dataset is vital for network threat analysis and mitigation. Proactive IDSs are required to tackle ever-growing threats in cyberspace. Machine learning (ML) and deep learning (DL) models have been deployed recently to detect the various types of cyber-attacks. However, current IDSs struggle to attain both a high detection rate and a low false alarm rate. To address these issues, we first develop a Center for Cyber Defense (CCD)-IDSv1 labeled flow-based dataset in an OpenStack environment. Five different attacks with normal usage imitating real-life usage are implemented. The number of network features is increased to overcome the shortcomings of the previous network flow-based datasets such as CIDDS and CIC-IDS2017. Secondly, this paper presents a comparative analysis on the effectiveness of different ML and DL models on our CCD-IDSv1 dataset. In this study, we consider both cyber anomaly detection and multi-attack classification. To improve the performance, we developed two DL-based ensemble models: Ensemble-CNN-10 and Ensemble-CNN-LSTM. Ensemble-CNN-10 combines 10 CNN models developed from 10-fold cross-validation, whereas Ensemble-CNN-LSTM combines base CNN and LSTM models. This paper also presents feature importance for both anomaly detection and multi-attack classification. Overall, the proposed ensemble models performed well in both the 10-fold cross-validation and independent testing on our dataset. Together, these results suggest the robustness and effectiveness of the proposed IDSs based on ML and DL models on the CCD-IDSv1 intrusion detection dataset.