Benchmark-Based Reference Model for Evaluating Botnet Detection Tools Driven by Traffic-Flow Analytics

Botnets are some of the most recurrent cyber-threats, which take advantage of the wide heterogeneity of endpoint devices at the Edge of the emerging communication environments for enabling the malicious enforcement of fraud and other adversarial tactics, including malware, data leaks or denial of service. There have been significant research advances in the development of accurate botnet detection methods underpinned on supervised analysis but assessing the accuracy and performance of such detection methods requires a clear evaluation model in the pursuit of enforcing proper defensive strategies. In order to contribute to the mitigation of botnets, this paper introduces a novel evaluation scheme grounded on supervised machine learning algorithms that enable the detection and discrimination of different botnets families on real operational environments. The proposal relies on observing, understanding and inferring the behavior of each botnet family based on network indicators measured at flow-level. The assumed evaluation methodology contemplates six phases that allow building a detection model against botnet-related malware distributed through the network, for which five supervised classifiers were instantiated were instantiated for further comparisons—Decision Tree, Random Forest, Naive Bayes Gaussian, Support Vector Machine and K-Neighbors. The experimental validation was performed on two public datasets of real botnet traffic—CIC-AWS-2018 and ISOT HTTP Botnet. Bearing the heterogeneity of the datasets, optimizing the analysis with the Grid Search algorithm led to improve the classification results of the instantiated algorithms. An exhaustive evaluation was carried out demonstrating the adequateness of our proposal which prompted that Random Forest and Decision Tree models are the most suitable for detecting different botnet specimens among the chosen algorithms. They exhibited higher precision rates whilst analyzing a large number of samples with less processing time. The variety of testing scenarios were deeply assessed and reported to set baseline results for future benchmark analysis targeted on flow-based behavioral patterns

Benchmark-Based Reference Model for Evaluating Botnet Detection Tools Driven by Traffic-Flow Analytics

Botnets are some of the most recurrent cyber-threats, which take advantage of the wide heterogeneity of endpoint devices at the Edge of the emerging communication environments for enabling the malicious enforcement of fraud and other adversarial tactics, including malware, data leaks or denial of service. There have been significant research advances in the development of accurate botnet detection methods underpinned on supervised analysis but assessing the accuracy and performance of such detection methods requires a clear evaluation model in the pursuit of enforcing proper defensive strategies. In order to contribute to the mitigation of botnets, this paper introduces a novel evaluation scheme grounded on supervised machine learning algorithms that enable the detection and discrimination of different botnets families on real operational environments. The proposal relies on observing, understanding and inferring the behavior of each botnet family based on network indicators measured at flow-level. The assumed evaluation methodology contemplates six phases that allow building a detection model against botnet-related malware distributed through the network, for which five supervised classifiers were instantiated were instantiated for further comparisons—Decision Tree, Random Forest, Naive Bayes Gaussian, Support Vector Machine and K-Neighbors. The experimental validation was performed on two public datasets of real botnet traffic—CIC-AWS-2018 and ISOT HTTP Botnet. Bearing the heterogeneity of the datasets, optimizing the analysis with the Grid Search algorithm led to improve the classification results of the instantiated algorithms. An exhaustive evaluation was carried out demonstrating the adequateness of our proposal which prompted that Random Forest and Decision Tree models are the most suitable for detecting different botnet specimens among the chosen algorithms. They exhibited higher precision rates whilst analyzing a large number of samples with less processing time. The variety of testing scenarios were deeply assessed and reported to set baseline results for future benchmark analysis targeted on flow-based behavioral patterns

Towards Bayesian-Based Trust Management for Insider Attacks in Healthcare Software-Defined Networks

© 2004-2012 IEEE. The medical industry is increasingly digitalized and Internet-connected (e.g., Internet of Medical Things), and when deployed in an Internet of Medical Things environment, software-defined networks (SDNs) allow the decoupling of network control from the data plane. There is no debate among security experts that the security of Internet-enabled medical devices is crucial, and an ongoing threat vector is insider attacks. In this paper, we focus on the identification of insider attacks in healthcare SDNs. Specifically, we survey stakeholders from 12 healthcare organizations (i.e., two hospitals and two clinics in Hong Kong, two hospitals and two clinics in Singapore, and two hospitals and two clinics in China). Based on the survey findings, we develop a trust-based approach based on Bayesian inference to figure out malicious devices in a healthcare environment. Experimental results in either a simulated and a real-world network environment demonstrate the feasibility and effectiveness of our proposed approach regarding the detection of malicious healthcare devices, i.e., our approach could decrease the trust values of malicious devices faster than similar approaches

Computational Methods for Medical and Cyber Security

Over the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, many shortcomings have also been highlighted, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning, and generative adversarial networks, have successfully solved various tasks in these fields. Therefore, applying these new methods to life-critical missions is crucial, as is measuring these less-traditional algorithms' success when used in these fields

Behavioral analysis in cybersecurity using machine learning: a study based on graph representation, class imbalance and temporal dissection

The main goal of this thesis is to improve behavioral cybersecurity analysis using machine learning, exploiting graph structures, temporal dissection, and addressing imbalance problems.This main objective is divided into four specific goals: OBJ1: To study the influence of the temporal resolution on highlighting micro-dynamics in the entity behavior classification problem. In real use cases, time-series information could be not enough for describing the entity behavior classification. For this reason, we plan to exploit graph structures for integrating both structured and unstructured data in a representation of entities and their relationships. In this way, it will be possible to appreciate not only the single temporal communication but the whole behavior of these entities. Nevertheless, entity behaviors evolve over time and therefore, a static graph may not be enoughto describe all these changes. For this reason, we propose to use a temporal dissection for creating temporal subgraphs and therefore, analyze the influence of the temporal resolution on the graph creation and the entity behaviors within. Furthermore, we propose to study how the temporal granularity should be used for highlighting network micro-dynamics and short-term behavioral changes which can be a hint of suspicious activities. OBJ2: To develop novel sampling methods that work with disconnected graphs for addressing imbalanced problems avoiding component topology changes. Graph imbalance problem is a very common and challenging task and traditional graph sampling techniques that work directly on these structures cannot be used without modifying the graph’s intrinsic information or introducing bias. Furthermore, existing techniques have shown to be limited when disconnected graphs are used. For this reason, novel resampling methods for balancing the number of nodes that can be directly applied over disconnected graphs, without altering component topologies, need to be introduced. In particular, we propose to take advantage of the existence of disconnected graphs to detect and replicate the most relevant graph components without changing their topology, while considering traditional data-level strategies for handling the entity behaviors within. OBJ3: To study the usefulness of the generative adversarial networks for addressing the class imbalance problem in cybersecurity applications. Although traditional data-level pre-processing techniques have shown to be effective for addressing class imbalance problems, they have also shown downside effects when highly variable datasets are used, as it happens in cybersecurity. For this reason, new techniques that can exploit the overall data distribution for learning highly variable behaviors should be investigated. In this sense, GANs have shown promising results in the image and video domain, however, their extension to tabular data is not trivial. For this reason, we propose to adapt GANs for working with cybersecurity data and exploit their ability in learning and reproducing the input distribution for addressing the class imbalance problem (as an oversampling technique). Furthermore, since it is not possible to find a unique GAN solution that works for every scenario, we propose to study several GAN architectures with several training configurations to detect which is the best option for a cybersecurity application. OBJ4: To analyze temporal data trends and performance drift for enhancing cyber threat analysis. Temporal dynamics and incoming new data can affect the quality of the predictions compromising the model reliability. This phenomenon makes models get outdated without noticing. In this sense, it is very important to be able to extract more insightful information from the application domain analyzing data trends, learning processes, and performance drifts over time. For this reason, we propose to develop a systematic approach for analyzing how the data quality and their amount affect the learning process. Moreover, in the contextof CTI, we propose to study the relations between temporal performance drifts and the input data distribution for detecting possible model limitations, enhancing cyber threat analysis.Programa de Doctorado en Ciencias y Tecnologías Industriales (RD 99/2011) Industria Zientzietako eta Teknologietako Doktoretza Programa (ED 99/2011

A Blockchain-Based Retribution Mechanism for Collaborative Intrusion Detection

Collaborative intrusion detection approach uses the shared detection signature between the collaborative participants to facilitate coordinated defense. In the context of collaborative intrusion detection system (CIDS), however, there is no research focusing on the efficiency of the shared detection signature. The inefficient detection signature costs not only the IDS resource but also the process of the peer-to-peer (P2P) network. In this paper, we therefore propose a blockchain-based retribution mechanism, which aims to incentivize the participants to contribute to verifying the efficiency of the detection signature in terms of certain distributed consensus. We implement a prototype using Ethereum blockchain, which instantiates a token-based retribution mechanism and a smart contract-enabled voting-based distributed consensus. We conduct a number of experiments built on the prototype, and the experimental results demonstrate the effectiveness of the proposed approach

Impact of Location Spoofing Attacks on Performance Prediction in Mobile Networks

Performance prediction in wireless mobile networks is essential for diverse purposes in network management and operation. Particularly, the position of mobile devices is crucial to estimating the performance in the mobile communication setting. With its importance, this paper investigates mobile communication performance based on the coordinate information of mobile devices. We analyze a recent 5G data collection and examine the feasibility of location-based performance prediction. As location information is key to performance prediction, the basic assumption of making a relevant prediction is the correctness of the coordinate information of devices given. With its criticality, this paper also investigates the impact of position falsification on the ML-based performance predictor, which reveals the significant degradation of the prediction performance under such attacks, suggesting the need for effective defense mechanisms against location spoofing threats

Word Embeddings for Fake Malware Generation

Signature and anomaly-based techniques are the fundamental methods to detect malware. However, in recent years this type of threat has advanced to become more complex and sophisticated, making these techniques less effective. For this reason, researchers have resorted to state-of-the-art machine learning techniques to combat the threat of information security. Nevertheless, despite the integration of the machine learning models, there is still a shortage of data in training that prevents these models from performing at their peak. In the past, generative models have been found to be highly effective at generating image-like data that are similar to the actual data distribution. In this paper, we leverage the knowledge of generative modeling on opcode sequences and aim to generate malware samples by taking advantage of the contextualized embeddings from BERT. We obtained promising results when differentiating between real and generated samples. We observe that generated malware has such similar characteristics to actual malware that the classifiers are having difficulty in distinguishing between the two, in which the classifiers falsely identify the generated malware as actual malware almost of the time