Applications in security and evasions in machine learning : a survey

In recent years, machine learning (ML) has become an important part to yield security and privacy in various applications. ML is used to address serious issues such as real-time attack detection, data leakage vulnerability assessments and many more. ML extensively supports the demanding requirements of the current scenario of security and privacy across a range of areas such as real-time decision-making, big data processing, reduced cycle time for learning, cost-efficiency and error-free processing. Therefore, in this paper, we review the state of the art approaches where ML is applicable more effectively to fulfill current real-world requirements in security. We examine different security applications' perspectives where ML models play an essential role and compare, with different possible dimensions, their accuracy results. By analyzing ML algorithms in security application it provides a blueprint for an interdisciplinary research area. Even with the use of current sophisticated technology and tools, attackers can evade the ML models by committing adversarial attacks. Therefore, requirements rise to assess the vulnerability in the ML models to cope up with the adversarial attacks at the time of development. Accordingly, as a supplement to this point, we also analyze the different types of adversarial attacks on the ML models. To give proper visualization of security properties, we have represented the threat model and defense strategies against adversarial attack methods. Moreover, we illustrate the adversarial attacks based on the attackers' knowledge about the model and addressed the point of the model at which possible attacks may be committed. Finally, we also investigate different types of properties of the adversarial attacks

PhagePro: prophage finding tool

Dissertação de mestrado em BioinformáticaBacteriophages are viruses that infect bacteria and use them to reproduce. Their reproductive cycle can be lytic or lysogenic. The lytic cycle leads to the bacteria death, given that the bacteriophage hijacks hosts machinery to produce phage parts necessary to assemble a new complete bacteriophage, until cell wall lyse occurs. On the other hand, the lysogenic reproductive cycle comprises the bacteriophage genetic material in the bacterial genome, becoming a prophage. Sometimes, due to external stimuli, these prophages can be induced to perform a lytic cycle. Moreover, the lysogenic cycle can lead to significant modifications in bacteria, for example, antibiotic resistance. To that end, PhagePro was created. This tool finds and characterises prophages inserted in the bacterial genome. Using 42 features, three datasets were created and five machine learning algorithms were tested. All models were evaluated in two phases, during testing and with real bacterial cases. During testing, all three datasets reached the 98 % F1 score mark in their best result. In the second phase, the results of the models were used to predict real bacterial cases and the results compared to the results of two tools, Prophage Hunter and PHASTER. The best model found 110 zones out of 154 and the model with the best result in dataset 3 had 94 in common. As a final test, Agrobacterium fabrum strC68 was extensively analysed. The results show that PhagePro was capable of detecting more regions with proteins associated with phages than the other two tools. In the ligth of the results obtained, PhagePro has shown great potential in the discovery and characterisation of bacterial alterations caused by prophages.Bacteriófagos são vírus que infetam bactérias usando-as para garantir a manutenção do seu genoma. Este processo pode ser realizado por ciclo lítico ou lipogénico. O ciclo lítico consiste em usar a célula para seu proveito, criar bacteriófagos e lisar a célula. Por outro lado, no ciclo lipogénico o bacteriófago insere o seu código genético no genoma da bactéria, o que pode levar à transferência de genes de interesse, tornando-se importante uma monitorização dos profagos. Assim foi desenvolvido o PhagePro, uma ferramenta capaz de encontrar e caracterizar bacteriófagos em genomas bactérias. Foram criadas features para distinguir profagos de bactérias, criando três datasets e usando algoritmos de aprendizagem de máquina. Os modelos foram avaliados durante duas fases, a fase de teste e a fase de casos reais. Na primeira fase de testes, o melhor modelo do dataset 1 teve 98% de F1 score, dataset 2 teve 98% e do dataset 3 também teve 98%. Todos os modelos, para teste em casos reais, foram comparados com previsões de duas ferramentas Prophage Hunter e PHASTER. O modelo com os melhores resultados obteve 110 de 154 zonas em comum com as duas ferramentas e o modelo do dataset 3 teve 94 zonas. Por fim, foi feita a análise dos resultados da bactéria Agrobacterium fabrum strC68. Os resultados obtidos mostram resultados diferentes, mas válidos, as ferramentas comparadas, visto que o PhagePro consegue detectar zonas com proteínas associadas a fagos que as outras tools não conseguem. Em virtude dos resultados obtidos, PhagePro mostrou que é capaz de encontrar e caracterizar profagos em bactérias.Este estudo contou com o apoio da Fundação para a Ciência e Tecnologia (FCT) portuguesa no âmbito do financiamento estratégico da unidade UIDB/04469/2020. A obra também foi parcialmente financiada pelo Projeto PTDC/SAU-PUB/29182/2017 [POCI-01-0145-FEDER-029182]

Neural malware detection

At the heart of today’s malware problem lies theoretically infinite diversity created by metamorphism. The majority of conventional machine learning techniques tackle the problem with the assumptions that a sufficiently large number of training samples exist and that the training set is independent and identically distributed. However, the lack of semantic features combined with the models under these wrong assumptions result largely in overfitting with many false positives against real world samples, resulting in systems being left vulnerable to various adversarial attacks. A key observation is that modern malware authors write a script that automatically generates an arbitrarily large number of diverse samples that share similar characteristics in program logic, which is a very cost-effective way to evade detection with minimum effort. Given that many malware campaigns follow this paradigm of economic malware manufacturing model, the samples within a campaign are likely to share coherent semantic characteristics. This opens up a possibility of one-to-many detection. Therefore, it is crucial to capture this non-linear metamorphic pattern unique to the campaign in order to detect these seemingly diverse but identically rooted variants. To address these issues, this dissertation proposes novel deep learning models, including generative static malware outbreak detection model, generative dynamic malware detection model using spatio-temporal isomorphic dynamic features, and instruction cognitive malware detection. A comparative study on metamorphic threats is also conducted as part of the thesis. Generative adversarial autoencoder (AAE) over convolutional network with global average pooling is introduced as a fundamental deep learning framework for malware detection, which captures highly complex non-linear metamorphism through translation invariancy and local variation insensitivity. Generative Adversarial Network (GAN) used as a part of the framework enables oneshot training where semantically isomorphic malware campaigns are identified by a single malware instance sampled from the very initial outbreak. This is a major innovation because, to the best of our knowledge, no approach has been found to this challenging training objective against the malware distribution that consists of a large number of very sparse groups artificially driven by arms race between attackers and defenders. In addition, we propose a novel method that extracts instruction cognitive representation from uninterpreted raw binary executables, which can be used for oneto- many malware detection via one-shot training against frequency spectrum of the Transformer’s encoded latent representation. The method works regardless of the presence of diverse malware variations while remaining resilient to adversarial attacks that mostly use random perturbation against raw binaries. Comprehensive performance analyses including mathematical formulations and experimental evaluations are provided, with the proposed deep learning framework for malware detection exhibiting a superior performance over conventional machine learning methods. The methods proposed in this thesis are applicable to a variety of threat environments here artificially formed sparse distributions arise at the cyber battle fronts.Doctor of Philosoph

Customer experience management: Expanding our understanding of the drivers and consequences of the customer experience

The present doctoral dissertation aims to analyze thenew business landscape that suggests the importance of customer experience ¿ its drivers and consequences from a dynamic perspective. The drivers of customer experience provide firms with crucial knowledge about the experience expectations and desires of the customers, thereby enabling firms to identify the key determinants which significantly shape customer perceptions toward the experience with the firm. This is very important for firms, since the effort dedicated by firms to improve customer experience is not always equally perceived and/or valued by customers. Likewise, integrating the consequences of customer experience allows firms to translate their investment in customer experience into specific opportunities and enhanced performance outcomes (financial, behavioral, and relational). This is specifically critical, considering that a customer experience perceived as favorable by customers might not have a positive impact on firm outcomes. Customer experience is not static but evolve over time. By taking into account the dynamic nature of customer experience, firms may capture the occurred changes in customers and adjust the factors under their controls immediately, thereby ensuring the alignment between customer experience expectations and firms¿ offerings. In this way, through a dynamic lens, we establish the linkage across what firms do, what customers think, what customers do, and finally what firms get. The thesis is consisted of three studies. Study 1 investigates the impact of firms¿ investments in three key strategic levers (i.e., value, the brand, and the relationship) on the customer experience as well as the direct and moderating role played by social influence. We integrate research in customer relationship management (i.e., customer equity framework) (Rust, Lemon, & Zeithaml, 2004) and customer experience management (Lemon & Verhoef, 2016; Verhoef et al., 2009) and offer a unifying framework to understand the linkages between the three equity drivers (i.e., value equity, brand equity, relationship equity), social influence, the customer experience, and its ultimate impact on profitability. Study 2 focuses on the separate and joint effects of customer experience and lock-in on customer retention. Building barriers to lock customers and improving the customer experience are two key strategies employed by firms to enhance customer retention. Although pursuing the same goal, these strategies work differently: the former relies more on a calculative, cost¿benefit approach to the exchange, while the latter promotes the affective aspects of the relationship. Finally, study 3 investigates how different dimensions of customer experience (recency effect, peak effect, trend effect, and fluctuation effect) and different relationship marketing (RM) actions (i.e., advertising communication, product innovation, and conflict) impact customer relationship expansion from a dynamic perspective, and distinguishes their short-term and long-term effects. Self-determination theory posits that motivation for pursuing activities are consisted of intrinsic (the ones originating from the self and one¿s desire) and extrinsic factors (originating from external demands).<br /

A Review on Human-Computer Interaction and Intelligent Robots

In the field of artificial intelligence, human–computer interaction (HCI) technology and its related intelligent robot technologies are essential and interesting contents of research. From the perspective of software algorithm and hardware system, these above-mentioned technologies study and try to build a natural HCI environment. The purpose of this research is to provide an overview of HCI and intelligent robots. This research highlights the existing technologies of listening, speaking, reading, writing, and other senses, which are widely used in human interaction. Based on these same technologies, this research introduces some intelligent robot systems and platforms. This paper also forecasts some vital challenges of researching HCI and intelligent robots. The authors hope that this work will help researchers in the field to acquire the necessary information and technologies to further conduct more advanced research