A Unified Framework for Measuring a Network's Mean Time-to-Compromise

Measuring the mean time-to-compromise provides important insights for understanding a network's weaknesses and for guiding corresponding defense approaches. Most existing network security metrics only deal with the threats of known vulnerabilities and cannot handle zero day attacks with consistent semantics. In this thesis, we propose a unified framework for measuring a network's mean time-to-compromise by considering both known, and zero day attacks. Specifically, we first devise models of the mean time for discovering and exploiting individual vulnerabilities. Unlike existing approaches, we replace the generic state transition model with a more vulnerability-specific graphical model. We then employ Bayesian networks to derive the overall mean time-to-compromise by aggregating the results of individual vulnerabilities. Finally, we demonstrate the framework's practical application to network hardening through case studies

Transformer Network for Multi-Person Tracking and Re-Identification in Unconstrained Environment

Multi-object tracking (MOT) has profound applications in a variety of fields, including surveillance, sports analytics, self-driving, and cooperative robotics. Despite considerable advancements, existing MOT methodologies tend to falter when faced with non-uniform movements, occlusions, and appearance-reappearance scenarios of the objects. Recognizing this inadequacy, we put forward an integrated MOT method that not only marries object detection and identity linkage within a singular, end-to-end trainable framework but also equips the model with the ability to maintain object identity links over long periods of time. Our proposed model, named STMMOT, is built around four key modules: 1) candidate proposal generation, which generates object proposals via a vision-transformer encoder-decoder architecture that detects the object from each frame in the video; 2) scale variant pyramid, a progressive pyramid structure to learn the self-scale and cross-scale similarities in multi-scale feature maps; 3) spatio-temporal memory encoder, extracting the essential information from the memory associated with each object under tracking; and 4) spatio-temporal memory decoder, simultaneously resolving the tasks of object detection and identity association for MOT. Our system leverages a robust spatio-temporal memory module that retains extensive historical observations and effectively encodes them using an attention-based aggregator. The uniqueness of STMMOT lies in representing objects as dynamic query embeddings that are updated continuously, which enables the prediction of object states with attention mechanisms and eradicates the need for post-processing

Types of Machine Learning Algorithms

Non

An admission control scheme for IEEE 802.11e wireless local area networks

Includes bibliographical references (leaves 80-84).Recent times has seen a tremendous increase in the deployment and use of 802.11 Wireless Local Area Networks (WLANs). These networks are easy to deploy and maintain, while providing reasonably high data rates at a low cost. In the paradigm of Next-Generation-Networks (NGNs), WLANs can be seen as an important access network technology to support IP multimedia services. However a traditional WLAN does not provide Quality of Service (QoS) support since it was originally designed for best effort operation. The IEEE 802. 11e standard was introduced to overcome the lack of QoS support for the legacy IEEE 802 .11 WLANs. It enhances the Media Access Control (MAC) layer operations to incorporate service differentiation. However, there is a need to prevent overloading of wireless channels, since the QoS experienced by traffic flows is degraded with heavily loaded channels. An admission control scheme for IEEE 802.11e WLANs would be the best solution to limit the amount of multimedia traffic so that channel overloading can be prevented. Some of the work in the literature proposes admission control solutions to protect the QoS of real-time traffic for IEEE 802.11e Enhanced Distributed Channel Access (EDCA). However, these solutions often under-utilize the resources of the wireless channels. A measurement-aided model-based admission control scheme for IEEE 802.11e EDCA WLANs is proposed to provide reasonable bandwidth guarantees to all existing flows. The admission control scheme makes use of bandwidth estimations that allows the bandwidth guarantees of all the flows that are admitted into the network to be protected. The bandwidth estimations are obtained using a developed analytical model of IEEE 802.11e EDCA channels. The admission control scheme also aims to accept the maximum amount of flows that can be accommodated by the network's resources. Through simulations, the performance of the proposed admission control scheme is evaluated using NS-2. Results show that accurate bandwidth estimations can be obtained when comparing the estimated achievable bandwidth to actual simulated bandwidth. The results also validate that the bandwidth needs of all admitted traffic are always satisfied when the admission control scheme is applied. It was also found that the admission control scheme allows the maximum amount of flows to be admitted into the network, according the network's capacity

Reliability Analysis of Electric Power Systems Considering Cyber Security

The new generation of the electric power system is the modern smart grid which is essentially a cyber and physical system (CPS). Supervisory control and data acquisition (SCADA)/energy management system (EMS) is the key component of CPS, which is becoming the main target of both external and insider cyberattacks. Cybersecurity of the SCADA/EMS system is facing big challenges and influences the reliability of the electric power system. Characteristics of cyber threats will impact the system reliability. System reliability can be influenced by various cyber threats with different attack skill levels and attack paths. Additionally, the change of structure of the target system may also result in the change of the system reliability. However, very limited research is related to the reliability analysis of the electric power system considering cybersecurity issue. A large amount of mathematical methods can be used to quantify the cyber threats and simulation processes can be applied to build the reliability analysis model. For instance, to analyze the vulnerabilities of the SCADA/EMS system in the electric power system, Bayesian Networks (BNs) can be used to model the attack paths of cyberattacks on the exploited vulnerabilities. The mean time-to-compromise (MTTC) and mean time-to-failure (MTTF) based on the Common Vulnerability Scoring System (CVSS) can be applied to characterize the properties of cyberattacks. What’s more, simulation approaches like non-sequential or sequential Monte Carlo Simulation (MCS) is able to simulate the system reliability analysis and calculate the reliability indexes. In this thesis, reliability of the SCADA/EMS system in the electric power system considering different cybersecurity issues is analyzed. The Bayesian attack path models of cyberattacks on the SCADA/EMS components are built by Bayesian Networks (BNs), and cyberattacks are quantified by its mean time-to-compromise (MTTC) by applying a modified Semi-Markov Process (SMP) and MTTC models. Based on the IEEE Reliability Test System (RTS) 96, the system reliability is analyzed by calculating the electric power system reliability indexes like LOLP and EENS through MCS. What’s more, cyberattacks with different lurking strategies are considered and analyzed. According to the simulation results, it shows that the system reliability of the SCADA/EMS system in the electric power system considering cyber security is closely related to the MTTC of cyberattacks, which is influenced by the attack paths, attacking skill levels, and the complexity of the target structure. With the increase of the MTTC values of cyberattacks, LOLP values decrease, which means that the reliability of the system is better, and the system is safer. In addition, with the difficulty level of lurking strategies of cyberattacks getting higher and higher, though the LOLP values of scenarios don’t increase a lot, the EENS values of the corresponding scenarios increase dramatically, which indicates that the system reliability is more unpredictable, and the cyber security is worse. Finally, insider attacks are discussed and corresponding LOLP values and EENS values considering lurking behavior are estimated and compared. Both LOLP and EENS values dramatically increase owing to the insider attacks that result in the lower MTTCs. This indicates that insider attacks can lead to worse impact on system reliability than external cyber attacks. The results of this thesis may contribute to the establishment of perfect countermeasures against with cyber attacks on the electric power system

Review Paper on Enhancing COVID-19 Fake News Detection With Transformer Model

The growing propagation of disinformation about the COVID-19 epidemic needs powerful fake news detection technologies. This review provides an in-depth examination of existing techniques, including traditional machine learning methods such as Random Forest and Naive Bayes, as well as sophisticated models for deep learning such as Bi- GRU, CNN, and LSTM, RNN, & transformer-based architecture such as BERT and XLM- Roberta, are also available. One noticeable development is the merging of traditional algorithmswith sophisticated transformers, which emphasize the quest of improved accuracy and flexibility.However, important research gaps have been identified. There has been little research on cross- lingual detection algorithms, revealing a substantial gap in multilingual false news detection, which is critical in the global context of COVID-19 information spread. Furthermore, the researchemphasizes the need of flexible methodologies by emphasizing the need for appropriate preprocessing strategies for various content types. Furthermore, the lack of common assessment measures is a barrier, underlining the need of unified frameworks for successfully benchmarking and comparing models. This analysis provides light on the changing COVID-19 false news detection environment, emphasizing the need for novel, adaptive, and internationally relevant approaches to successfully address the ubiquitous dissemination of disinformation during the current pandemic

Security and Privacy Issues of Big Data

This chapter revises the most important aspects in how computing infrastructures should be configured and intelligently managed to fulfill the most notably security aspects required by Big Data applications. One of them is privacy. It is a pertinent aspect to be addressed because users share more and more personal data and content through their devices and computers to social networks and public clouds. So, a secure framework to social networks is a very hot topic research. This last topic is addressed in one of the two sections of the current chapter with case studies. In addition, the traditional mechanisms to support security such as firewalls and demilitarized zones are not suitable to be applied in computing systems to support Big Data. SDN is an emergent management solution that could become a convenient mechanism to implement security in Big Data systems, as we show through a second case study at the end of the chapter. This also discusses current relevant work and identifies open issues.Comment: In book Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence, IGI Global, 201

Performance assessment in water supply and distribution

Abstract unavailable please refer to PD