Biblio

Capturing the patterns in adversarial movement can provide valuable information regarding how the adversaries progress through cyberattacks. This information can be further employed for making comparisons and interpretations of decision making of the adversaries. In this study, we propose a framework based on concepts of social networks to characterize and compare the patterns, variations and shifts in the movements made by an adversarial team during a real-time cybersecurity exercise. We also explore the possibility of movement association with the skill sets using topological sort networks. This research provides preliminary insight on adversarial movement complexity and linearity and decision-making as cyberattacks unfold.

This paper describes a machine assistance approach to grading decisions for values that might be missing or need validation, using a mathematical algebraic form of an Expert System, instead of the traditional textual or logic forms and builds a neural network computational graph structure. This Experts System approach is also structured into a neural network like format of: input, hidden and output layers that provide a structured approach to the knowledge-base organization, this provides a useful abstraction for reuse for data migration applications in big data, Cyber and relational databases. The approach is further enhanced with a Bayesian probability tree approach to grade the confidences of value probabilities, instead of the traditional grading of the rule probabilities, and estimates the most probable value in light of all evidence presented. This is ground work for a Machine Learning (ML) experts system approach in a form that is closer to a Neural Network node structure.

The use of biometric-based authentication systems spread over daily life consumer electronics. Over the years, researchers' interest shifted from hard (such as fingerprints, voice and keystroke dynamics) to soft biometrics (such as age, ethnicity and gender), mainly by using the latter to improve the authentication systems effectiveness. While newer approaches are constantly being proposed by domain experts, in the last years Deep Learning has raised in many computer vision tasks, also becoming the current state-of-art for several biometric approaches. However, since the automatic processing of data rich in sensitive information could expose users to privacy threats associated to their unfair use (i.e. gender or ethnicity), in the last years researchers started to focus on the development of defensive strategies in the view of a more secure and private AI. The aim of this work is to exploit Adversarial Perturbation, namely approaches able to mislead state-of-the-art CNNs by injecting a suitable small perturbation over the input image, to protect subjects against unwanted soft biometrics-based identification by automatic means. In particular, since ethnicity is one of the most critical soft biometrics, as a case of study we will focus on the generation of adversarial stickers that, once printed, can hide subjects ethnicity in a real-world scenario.

Recommended systems are very popular nowadays. They are used online to help a user get the desired product quickly. Recommended Systems are found on almost every website, especially big companies such as Facebook, eBay, Amazon, NetFlix, and others. In specific cases, these systems help the user find a book, movie, article, product of his or her preference, and are also used on social networks to meet friends who share similar interests in different fields. These companies use referral systems because they bring amazing benefits in a very fast time. To generate more accurate recommendations, recommended systems are based on the user's personal information, eg: different ratings, history observation, personal profiles, etc. Use of these systems is very necessary but the way this information is received, and the privacy of this information is almost constantly ignored. Many users are unaware of how their information is received and how it is used. This paper will discuss how recommended systems work in different online companies and how safe they are to use without compromising their privacy. Given the widespread use of these systems, an important issue has arisen regarding user privacy and security. Collecting personal information from recommended systems increases the risk of unwanted exposure to that information. As a result of this paper, the reader will be aware of the functioning of Recommended systems, the way they receive and use their information, and will also discuss privacy protection techniques against Recommended systems.

Context:

The ‘as code’ suffix in infrastructure as code (IaC) refers to applying software engineering activities, such as version control, to maintain IaC scripts. Without the application of these activities, defects that can have serious consequences may be introduced in IaC scripts. A systematic investigation of the development anti-patterns for IaC scripts can guide practitioners in identifying activities to avoid defects in IaC scripts. Development anti-patterns are recurring development activities that relate with defective IaC scripts.

Goal:

The goal of this paper is to help practitioners improve the quality of infrastructure as code (IaC) scripts by identifying development activities that relate with defective IaC scripts.

Methodology:

We identify development anti-patterns by adopting a mixed-methods approach, where we apply quantitative analysis with 2,138 open source IaC scripts and conduct a survey with 51 practitioners.

Findings:

We observe five development activities to be related with defective IaC scripts from our quantitative analysis. We identify five development anti-patterns namely, ‘boss is not around’, ‘many cooks spoil’, ‘minors are spoiler’, ‘silos’, and ‘unfocused contribution’.

Conclusion:

Our identified development anti-patterns suggest the importance of ‘as code’ activities in IaC because these activities are related to quality of IaC scripts.

Hardware implementation of many of today's applications such as those in automotive, telecommunication, bio, and security, require heavy repeated computations, and concurrency in the execution of these computations. These requirements are not easily satisfied by existing embedded systems. This paper proposes an embedded system architecture that is enhanced by an array of accelerators, and a bussing system that enables concurrency in operation of accelerators. This architecture is statically configurable to configure it for performing a specific application. The embedded system architecture and architecture of the configurable accelerators are discussed in this paper. A case study examines an automotive application running on our proposed system.

This article discusses existing approaches to building regional scale networks. Authors offer a mathematical model of network growth process, on the basis of which simulation is performed. The availability characteristic is used as criterion for measuring optimality. This report describes the mechanism for measuring network availability and contains propositions to make changes to the procedure for designing of regional networks, which can improve its qualitative characteristics. The efficiency of changes is confirmed by simulation.

Affective Computing is a rapidly growing field spurred by advancements in artificial intelligence, but often, held back by the inability to translate psychological theories of emotion into tractable computational models. To address this, we propose a probabilistic programming approach to affective computing, which models psychological-grounded theories as generative models of emotion, and implements them as stochastic, executable computer programs. We first review probabilistic approaches that integrate reasoning about emotions with reasoning about other latent mental states (e.g., beliefs, desires) in context. Recently-developed probabilistic programming languages offer several key desidarata over previous approaches, such as: (i) flexibility in representing emotions and emotional processes; (ii) modularity and compositionality; (iii) integration with deep learning libraries that facilitate efficient inference and learning from large, naturalistic data; and (iv) ease of adoption. Furthermore, using a probabilistic programming framework allows a standardized platform for theory-building and experimentation: Competing theories (e.g., of appraisal or other emotional processes) can be easily compared via modular substitution of code followed by model comparison. To jumpstart adoption, we illustrate our points with executable code that researchers can easily modify for their own models. We end with a discussion of applications and future directions of the probabilistic programming approach

Neural architectures are the foundation for improving performance of deep neural networks (DNNs). This paper presents deep compositional grammatical architectures which harness the best of two worlds: grammar models and DNNs. The proposed architectures integrate compositionality and reconfigurability of the former and the capability of learning rich features of the latter in a principled way. We utilize AND-OR Grammar (AOG) as network generator in this paper and call the resulting networks AOGNets. An AOGNet consists of a number of stages each of which is composed of a number of AOG building blocks. An AOG building block splits its input feature map into N groups along feature channels and then treat it as a sentence of N words. It then jointly realizes a phrase structure grammar and a dependency grammar in bottom-up parsing the “sentence” for better feature exploration and reuse. It provides a unified framework for the best practices developed in state-of-the-art DNNs. In experiments, AOGNet is tested in the ImageNet-1K classification benchmark and the MS-COCO object detection and segmentation benchmark. In ImageNet-1K, AOGNet obtains better performance than ResNet and most of its variants, ResNeXt and its attention based variants such as SENet, DenseNet and DualPathNet. AOGNet also obtains the best model interpretability score using network dissection. AOGNet further shows better potential in adversarial defense. In MS-COCO, AOGNet obtains better performance than the ResNet and ResNeXt backbones in Mask R-CNN.

A key question for characterising a system's vulnerability against timing attacks is whether or not it allows an adversary to aggregate information about a secret over multiple timing measurements. Existing approaches for reasoning about this aggregate information rely on strong assumptions about the capabilities of the adversary in terms of measurement and computation, which is why they fall short in modelling, explaining, or synthesising real-world attacks against cryptosystems such as RSA or AES. In this paper we present a novel model for reasoning about information aggregation in timing attacks. The model is based on a novel abstraction of timing measurements that better captures the capabilities of real-world adversaries, and a notion of compositionality of programs that explains attacks by divide-and-conquer. Our model thus lifts important limiting assumptions made in prior work and enables us to give the first uniform explanation of high-profile timing attacks in the language of information-flow analysis.

The US Federal Communications Commission (FCC) has recently mandated the database-driven dynamic spectrum access where unlicensed secondary users search for idle bands and use them opportunistically. The database-driven dynamic spectrum access approach is regarded for minimizing any harmful interference to licensed primary users caused by RF channel sensing uncertainties. However, when several secondary users (or several malicious users) query the RF spectrum database at the same time, spectrum server could experience denial of service (DoS) attack. In this paper, we investigate the Aggregated-Query-as-a-Secure-Service (AQaaSS) for querying RF spectrum database by secondary users for opportunistic wireless communications where selected number of secondary users aka grid leaders, query the database on behalf of all other secondary users, aka grid followers and relay the idle channel information to grid followers. Furthermore, the grid leaders are selected based on their both reputation or trust level and location in the network for the integrity of the information that grid followers receive. Grid followers also use the weighted majority voting to filter out comprised information about the idle channels. The performance of the proposed approach is evaluated using numerical results. The proposed approach gives lower latency (or same latency) to the secondary users and lower load (or same load) to the RF spectrum database server when more number of secondary users (or less number of secondary users) query than that of the server capacity.

In the Web 2.0 era, user interaction makes Web application more diverse, but brings threats, among which XSS vulnerability is the common and pernicious one. In order to promote the efficiency of XSS detection, this paper investigates the parameter characteristics of malicious XSS attacks. We identify whether a parameter is malicious or not through detecting user input parameters with SVM algorithm. The original malicious XSS parameters are deformed by DQN algorithm for reinforcement learning for rule-based WAF to be anti-anti-virus. Based on this method, we can identify whether a specific WAF is secure. The above model creates a more efficient automatic XSS detection tool and a more targeted automatic anti-anti-virus payload generation tool. This paper also explores the automatic generation of XSS attack codes with RNN LSTM algorithm.

XSS is one of the common vulnerabilities in web applications. Many black-box testing tools may collect a large number of payloads and traverse them to find a payload that can be successfully injected, but they are not very efficient. And previous research has paid less attention to how to improve the efficiency of black-box testing to detect XSS vulnerability. To improve the efficiency of testing, we develop an XSS testing tool. It collects 6128 payloads and uses a headless browser to detect XSS vulnerability. The tool can discover XSS vulnerability quickly with the ART(Adaptive Random Testing) method. We conduct an experiment using 3 extensively adopted open source vulnerable benchmarks and 2 actual websites to evaluate the ART method. The experimental results indicate that the ART method can effectively improve the fuzzing method by more than 27.1% in reducing the number of attempts before accomplishing a successful injection.

In order to effectively do network security protection, detecting system vulnerabilities becomes an indispensable process. Here, the vulnerability detection module with three functions is assembled into a device, and a composite detection tool with multiple functions is proposed to deal with some frequent vulnerabilities. The tool includes a total of three types of vulnerability detection, including cross-site scripting attacks, SQL injection, and directory traversal. First, let's first introduce the principle of each type of vulnerability; then, introduce the detection method of each type of vulnerability; finally, detail the defenses of each type of vulnerability. The benefits are: first, the cost of manual testing is eliminated; second, the work efficiency is greatly improved; and third, the network is safely operated in the first time.

Many web applications are vulnerable to Cross Site Scripting (XSS) attacks enabling attackers to steal sensitive information and commit frauds. Much research in this area have focused on detecting vulnerable web pages using static and dynamic program analysis. The best practice to prevent XSS vulnerabilities is to encode untrusted dynamic content. However, a common programming error is the use of a wrong type of encoder to sanitize untrusted data, leaving the application vulnerable. We propose a new approach that can automatically fix this common type of XSS vulnerability in many situations. This approach is integrated into the software maintenance life cycle through unit testing. Vulnerable codes are refactored to reflect the suggested encoder and then verified using an attack evaluating mechanism to find a proper repair. Evaluation of this approach has been conducted on an open source medical record application with over 200 web pages written in JSP.

With the advancement in technology, inclusion of Information and Communication Technology (ICT) in the conventional Electrical Power Grid has become evident. The combination of communication system with physical system makes it cyber-physical system (CPS). Though the advantages of this improvement in technology are numerous, there exist certain issues with the system. Security and privacy concerns of a CPS are a major field and research and the insight of which is content of this paper.

One critical challenge of today's cloud services is how to provide an effective search service while preserving user privacy. In this paper, we propose a wildcard-based multi-keyword fuzzy search (WMFS) scheme over the encrypted data, which tolerates keyword misspellings by exploiting the indecomposable property of primes. Compared with existing secure fuzzy search schemes, our WMFS scheme has the following merits: 1) Efficiency. It eliminates the requirement of a predefined dictionary and thus supports updates efficiently. 2) High accuracy. It eliminates the false positive and false negative introduced by specific data structures and thus allows the user to retrieve files as accurate as possible. 3) Flexibility. It gives the user great flexibility to specify different search patterns including keyword and substring matching. Extensive experiments on a real data set demonstrate the effectiveness and efficiency of our scheme.

The global Electronic Health Record (EHR) market is growing dramatically and expected to reach \$39.7 billions by 2022. To safe-guard security and privacy of EHR, access control is an essential mechanism for managing EHR data. This paper proposes a hybrid architecture to facilitate access control of EHR data by using both blockchain and edge node. Within the architecture, a blockchain-based controller manages identity and access control policies and serves as a tamper-proof log of access events. In addition, off-chain edge nodes store the EHR data and apply policies specified in Abbreviated Language For Authorization (ALFA) to enforce attribute-based access control on EHR data in collaboration with the blockchain-based access control logs. We evaluate the proposed hybrid architecture by utilizing Hyperledger Composer Fabric blockchain to measure the performance of executing smart contracts and ACL policies in terms of transaction processing time and response time against unauthorized data retrieval.

Software has become an integral part of every industry and organization. Due to improvement in technology and lack of expertise in coding techniques, software vulnerabilities are increasing day-by-day in the software development sector. The time gap between the identification of the vulnerabilities and their automated exploit attack is decreasing. This gives rise to the need for detection and prevention of security risks and development of secure software. Earlier the security risk is identified and corrected the better it is. Developers needs a framework which can report the security flaws in their system and reduce the chances of exploitation of these flaws by some malicious user. Common Vector Scoring System (CVSS) is a De facto metrics system used to assess the exploitability of vulnerabilities. CVSS exploitability measures use subjective values based on the views of experts. It considers mainly two factors, Access Vector (AV) and Authentication (AU). CVSS does not specify on what basis the third-factor Access Complexity (AC) is measured, whether or not it considers software properties. Our objective is to come up with a framework that automates the process of identifying vulnerabilities using software structural properties. These properties could be attack entry points, vulnerability locations, presence of dangerous system calls, and reachability analysis. This framework has been tested on two open source softwares - Apache HTTP server and Mozilla Firefox.

Today, connectivity is demanded in almost every domain, e.g., the smart home domain and its connected smart household devices like TVs and fridges, or the industrial automation domain, connecting plants, controllers and sensors to the internet for purposes like condition monitoring. This trend amplifies the need for secure applications that can protect their sensitive data against manipulation and leaks. However, many applications are still built without considering security in its design phase, often it is perceived as too complicated and time consuming. This is a major oversight, as fixing vulnerabilities after release is often not feasible when major architecture redesigns are necessary. Therefore, the software developer has to make sure that the developed software architecture is secure. Today, there are some tools available to help the software developer in identifying potential security weaknesses of their architecture. However, easy and fast to use tools that support the software developer in improving their architecture's security are lacking. The goal of my thesis is to make security improvements easily applicable by non-security and non-architecture experts by proposing systematic, easy to use and automated techniques that will help the software developer in designing secure software architectures. To achieve this goal, I propose a method that enables the software developer to automatically find flaws and weaknesses, as well as appropriate improvements in their given software architecture during the design phase. For this method, I adopt Model-Based Development techniques by extending and creating Domain-Specific Languages (DSL) for specifying the architecture itself and possible architectural improvements. Using these DSLs, my approach automatically suggests security enhancing improvements for the architecture, promoting increased security of software architectures and as such for the developed applications as a whole.

With the popularity of Internet applications and rapid development, data using and sharing process may lead to the sensitive information divulgence. To deal with the privacy protection issue more effectively, in this paper, we propose the associated data sets protection model based on game theory from the point of view of realizing benefits from the access of privacy is about happen, quantify the extent to which visitors gain sensitive information, then compares the tolerance of the sensitive information owner and finally decides whether to allow the visitor to make an access request.

The massive adoption of mobile devices by both individuals and companies is raising many security concerns. The fact that such devices are handling sensitive data makes them a target for attackers. Many attack prevention mechanisms are deployed with a last line of defense that focuses on the containment principle. Currently, iOS treats each 3rd party application alike which may lead to security flaws. We propose a framework in which each application has a custom sandboxed environment. We investigated the current confinement architecture used by Apple and built a solution on top of it.

In the deep learning tasks, we can design different network models to address different tasks (classification, detection, segmentation). But traditional deep learning networks simply increase the depth and breadth of the network. This leads to a higher complexity of the model. We propose Adaptively Weighted Channel Feature Network of Mixed Convolution Kernel(SKENet). SKENet extract features from different kernels, then mixed those features by elementwise, lastly do sigmoid operator on channel features to get adaptive weightings. We did a simple classification test on the CIFAR10 amd CIFAR100 dataset. The results show that SKENet can achieve a better result in a shorter time. After that, we did an object detection experiment on the VOC dataset. The experimental results show that SKENet is far ahead of the SKNet[20] in terms of speed and accuracy.

The rise in the number of devices joining the Internet of Things (IoT) has created a huge potential for distributed denial of service (DDoS) attacks, especially due to the lack of security in these computationally limited devices. Malicious actors have realized that and managed to turn large sets of IoT devices into botnets under their control. Given this scenario, this work studies botnet architectures identified so far and assesses how they are considered in the few recent defense proposals that consider botnet architectures.

Most serious occurrence of modern malware is Botnet. Botnet is a rapidly evolving problem that is still not well understood and studied. One of the main goals for modern network security is to create adequate techniques for the detection and eventual termination of Botnet threats. The article presents an approach for implementing a host-based Intrusion Detection System for Botnet attack detection. The approach is based on a variation of a genetic algorithm to detect anomalies in a case of attacks. An implementation of the approach and experimental results are presented.