Biblio

The Web today is a growing universe of pages and applications teeming with interactive content. The security of such applications is of the utmost importance, as exploits can have a devastating impact on personal and economic levels. The number one programming language in Web applications is PHP, powering more than 80% of the top ten million websites. Yet it was not designed with security in mind and, today, bears a patchwork of fixes and inconsistently designed functions with often unexpected and hardly predictable behavior that typically yield a large attack surface. Consequently, it is prone to different types of vulnerabilities, such as SQL Injection or Cross-Site Scripting. In this paper, we present an interprocedural analysis technique for PHP applications based on code property graphs that scales well to large amounts of code and is highly adaptable in its nature. We implement our prototype using the latest features of PHP 7, leverage an efficient graph database to store code property graphs for PHP, and subsequently identify different types of Web application vulnerabilities by means of programmable graph traversals. We show the efficacy and the scalability of our approach by reporting on an analysis of 1,854 popular open-source projects, comprising almost 80 million lines of code.

Social media plays an integral part in individual's everyday lives as well as for companies. Social media brings numerous benefits in people's lives such as to keep in touch with close ones and specially with relatives who are overseas, to make new friends, buy products, share information and much more. Unfortunately, several threats also accompany the countless advantages of social media. The rapid growth of the online social networking sites provides more scope for criminals and cyber-criminals to carry out their illegal activities. Hackers have found different ways of exploiting these platform for their malicious gains. This research englobes some of the common threats on social media such as spam, malware, Trojan horse, cross-site scripting, industry espionage, cyber-bullying, cyber-stalking, social engineering attacks. The main purpose of the study to elaborates on phishing, malware and click-jacking attacks. The main purpose of the research, there is no particular research available on the forensic investigation for Facebook. There is no particular forensic investigation methodology and forensic tools available which can follow on the Facebook. There are several tools available to extract digital data but it's not properly tested for Facebook. Forensics investigation tool is used to extract evidence to determine what, when, where, who is responsible. This information is required to ensure that the sufficient evidence to take legal action against criminals.

Software systems nowadays communicate via a number of complex languages. This is often the cause of security vulnerabilities like arbitrary code execution, or injections. Whereby injections such as cross-site scripting are widely known from textual languages such as HTML and JSON that constantly gain more popularity. These systems use parsers to read input and unparsers write output, where these security vulnerabilities arise. Therefore correct parsing and unparsing of messages is of the utmost importance when developing secure and reliable systems. Part of the challenge developers face is to correctly encode data during unparsing and decode it during parsing. This paper presents McHammerCoder, an (un)parser and encoding generator supporting textual and binary languages. Those (un)parsers automatically apply the generated encoding, that is derived from the language's grammar. Therefore manually defining and applying encoding is not required to effectively prevent injections when using McHammerCoder. By specifying the communication language within a grammar, McHammerCoder provides developers with correct input and output handling code for their custom language.

Web Application becomes the leading solution for the utilization of systems that need access globally, distributed, cost-effective, as well as the diversity of the content that can run on this technology. At the same time web application security have always been a major issue that must be considered due to the fact that 60% of Internet attacks targeting web application platform. One of the biggest impacts on this technology is Cross Site Scripting (XSS) attack, the most frequently occurred and are always in the TOP 10 list of Open Web Application Security Project (OWASP). Vulnerabilities in this attack occur in the absence of checking, testing, and the attention about secure coding practices. There are several alternatives to prevent the attacks that associated with this threat. Network Intrusion Detection System can be used as one solution to prevent the influence of XSS Attack. This paper investigates the XSS attack recognition and detection using regular expression pattern matching and a preprocessing method. Experiments are conducted on a testbed with the aim to reveal the behaviour of the attack.

In recent years, with the advances in JavaScript engines and the adoption of HTML5 APIs, web applications begin to show a tendency to shift their functionality from the server side towards the client side, resulting in dense and complex interactions with HTML documents using the Document Object Model (DOM). As a consequence, client-side vulnerabilities become more and more prevalent. In this paper, we focus on DOM-sourced Cross-site Scripting (XSS), which is a kind of severe but not well-studied vulnerability appearing in browser extensions. Comparing with conventional DOM-based XSS, a new attack surface is introduced by DOM-sourced XSS where the DOM could become a vulnerable source as well besides common sources such as URLs and form inputs. To discover such vulnerability, we propose a detecting framework employing hybrid analysis with two phases. The first phase is the lightweight static analysis consisting of a text filter and an abstract syntax tree parser, which produces potential vulnerable candidates. The second phase is the dynamic symbolic execution with an additional component named shadow DOM, generating a document as a proof-of-concept exploit. In our large-scale real-world experiment, 58 previously unknown DOM-sourced XSS vulnerabilities were discovered in user scripts of the popular browser extension Greasemonkey.

Taint analysis has been used in numerous scripting languages such as Perl and Ruby to defend against various form of code injection attacks, such as cross-site scripting (XSS) and SQL-injection. However, most taint analysis systems simply fail when tainted information is used in a possibly unsafe manner. In this paper, we explore how precise taint tracking can be used in order to secure web content. Rather than simply crashing, we propose that a library-writer defined sanitization function can instead be used on the tainted portions of a string. With this approach, library writers or framework developers can design their tools to be resilient, even if inexperienced developers misuse these libraries in unsafe ways. In other words, developer mistakes do not have to result in system crashes to guarantee security. We implement both coarse-grained and precise taint tracking in JavaScript, and show how our precise taint tracking API can be used to defend against SQL injection and XSS attacks. We further evaluate the performance of this approach, showing that precise taint tracking involves an overhead of approximately 22%.

JavaScript is the source of many security problems, including cross-site scripting attacks and malicious advertising code. Central to these problems is the fact that code from untrusted sources runs with full privileges. Information flow controls help prevent violations of data confidentiality and integrity. This article explores faceted values, a mechanism for providing information flow security in a dynamic manner that avoids the stuck executions of some prior approaches, such as the no-sensitive-upgrade technique. Faceted values simultaneously simulate multiple executions for different security levels to guarantee termination-insensitive noninterference. We also explore the interaction of faceted values with exceptions, declassification, and clearance.

This paper presents principles of Defensive Programming and examines the growing concern that these principles are not effectively incorporated into Computer Science and related computing degree programs' curricula. To support this concern, Defensive Programming principles are applied to a case study - Cross-site Scripting cybersecurity attacks. This paper concludes that Defensive Programming plays an important role in preventing these attacks and should thus be more aggressively integrated into CS courses such as Programming, Algorithms, Databases, Computer Architecture and Organization, and Computer Networks.

Cross-Site Scripting (XSS) is an unremitting problem for the Web. Since its initial public documentation in 2000 until now, XSS has been continuously on top of the vulnerability statistics. Even though there has been a considerable amount of research and developer education to address XSS on the source code level, the overall number of discovered XSS problems remains high. Because of this, various approaches to mitigate XSS have been proposed as a second line of defense, with HTML sanitizers, Web Application Firewalls, browser-based XSS filters, and the Content Security Policy being some prominent examples. Most of these mechanisms focus on script tags and event handlers, either by removing them from user-provided content or by preventing their script code from executing. In this paper, we demonstrate that this approach is no longer sufficient for modern applications: We describe a novel Web attack that can circumvent all of theses currently existing XSS mitigation techniques. In this attack, the attacker abuses so called script gadgets (legitimate JavaScript fragments within an application's legitimate code base) to execute JavaScript. In most cases, these gadgets utilize DOM selectors to interact with elements in the Web document. Through an initial injection point, the attacker can inject benign-looking HTML elements which are ignored by these mitigation techniques but match the selector of the gadget. This way, the attacker can hijack the input of a gadget and cause processing of his input, which in turn leads to code execution of attacker-controlled values. We demonstrate that these gadgets are omnipresent in almost all modern JavaScript frameworks and present an empirical study showing the prevalence of script gadgets in productive code. As a result, we assume most mitigation techniques in web applications written today can be bypassed.

Hybrid mobile applications are coded in both standard web languages and native language. The including of web technologies results in that Hybrid applications introduce more security risks than the traditional web applications, which have more possible channels to inject malicious codes to gain much more powerful privileges. In this paper, Cross-site Scripting attacks specific to Android Hybrid apps developed with PhoneGap framework are investigated. We find out that the XSS vulnerability on Hybrid apps makes it possible for attackers to bypass the access control policies of WebView and WebKit to run malicious codes into victim's WebView. With the PhoneGap plugins, the malicious codes can steal user's private information and destroy user's file system, which are more damaging than cookie stealing.

Undeterred by numerous efforts deployed by antivirus software that shields users from various security threats, ransomware is constantly evolving as technology advances. The impact includes hackers hindering the user's accessibility to their data, and the user will pay ransom to retrieve their data. Ransomware also targets multimillion-dollar organizations, and it can cause colossal data loss. The organizations could face catastrophic consequences, and business operations could be ceased. This research contributes by spreading awareness of ransomware to alert people to tackle ransomware. The solution of this research is the conceptual development of a browser extension that provides assistance to warn users of plausible dangers while surfing the Internet. It allows the users to surf the web safely. Since the contribution of this research is conceptual, we can assume that technology users will adopt the proposed idea to prevent ransomware attacks on their personal computers once the solution is fully implemented in future research.

Motivated by the need to automatically generate behavior-based security challenges to improve user authentication for web services, we consider the problem of large-scale construction of realistic-looking names to serve as aliases for real individuals. We aim to use these names to construct security challenges, where users are asked to identify their real contacts among a presented pool of names. We seek these look-alike names to preserve name characteristics like gender, ethnicity, and popularity, while being unlinkable back to the source individual, thereby making the real contacts not easily guessable by attackers. To achive this, we introduce the technique of distributed name embeddings, representing names in a high-dimensional space such that distance between name components reflects the degree of cultural similarity between these strings. We present different approaches to construct name embeddings from contact lists observed at a large web-mail provider, and evaluate their cultural coherence. We demonstrate that name embeddings strongly encode gender and ethnicity, as well as name popularity. We applied this algorithm to generate imitation names in email contact list challenge. Our controlled user study verified that the proposed technique reduced the attacker's success rate to 26.08%, indistinguishable from random guessing, compared to a success rate of 62.16% from previous name generation algorithms. Finally, we use these embeddings to produce an open synthetic name resource of 1 million names for security applications, constructed to respect both cultural coherence and U.S. census name frequencies.

The purpose of this paper is to analyse the rules of the General Data Protection Regulation on automated decision making in the age of Big Data and to explore how to ensure transparency of such decisions, in particular those taken with the help of algorithms. The GDPR, in its Article 22, prohibits automated individual decision-making, including profiling. On the first impression, it seems that this provision strongly protects individuals and potentially even hampers the future development of AI in decision making. However, it can be argued that this prohibition, containing numerous limitations and exceptions, looks like a Swiss cheese with giant holes in it. Moreover, in case of automated decisions involving personal data of the data subject, the GDPR obliges the controller to provide the data subject with 'meaningful information about the logic involved' (Articles 13 and 14). If we link this information to the rights of data subject, we can see that the information about the logic involved needs to enable him/her to express his/her point of view and to contest the automated decision. While this requirement fits well within the broader framework of GDPR's quest for a high level of transparency, it also raises several queries particularly in cases where the decision is taken with the help of algorithms: What exactly needs to be revealed to the data subject? How can an algorithm-based decision be explained? Apart from technical obstacles, we are facing also intellectual property and state secrecy obstacles to this 'algorithmic transparency'.

Large-scale sensing and actuation infrastructures have allowed buildings to achieve significant energy savings; at the same time, these technologies introduce significant privacy risks that must be addressed. In this paper, we present a framework for modeling the trade-off between improved control performance and increased privacy risks due to occupancy sensing. More specifically, we consider occupancy-based HVAC control as the control objective and the location traces of individual occupants as the private variables. Previous studies have shown that individual location information can be inferred from occupancy measurements. To ensure privacy, we design an architecture that distorts the occupancy data in order to hide individual occupant location information while maintaining HVAC performance. Using mutual information between the individual's location trace and the reported occupancy measurement as a privacy metric, we are able to optimally design a scheme to minimize privacy risk subject to a control performance guarantee. We evaluate our framework using real-world occupancy data: first, we verify that our privacy metric accurately assesses the adversary's ability to infer private variables from the distorted sensor measurements; then, we show that control performance is maintained through simulations of building operations using these distorted occupancy readings.

We present OpenFace, our new open-source face recognition system that approaches state-of-the-art accuracy. Integrating OpenFace with inter-frame tracking, we build RTFace, a mechanism for denaturing video streams that selectively blurs faces according to specified policies at full frame rates. This enables privacy management for live video analytics while providing a secure approach for handling retrospective policy exceptions. Finally, we present a scalable, privacy-aware architecture for large camera networks using RTFace.

As teleoperated robot technology becomes cheaper, more powerful, and more reliable, remotely-operated telepresence robots will become more prevalent in homes and businesses, allowing visitors and business partners to be present without the need to travel. Hindering adoption is the issue of privacy: an Internet-connected telepresence robot has the ability to spy on its local area, either for the remote operator or a third party with access to the video data. Additionally, since the remote operator may move about and manipulate objects without local-user intervention, certain typical privacy-protecting techniques such as moving objects to a different room or putting them in a cabinet may prove insufficient. In this paper, we examine the effects of three whole-image filters on the remote operator's ability to discern details while completing a navigation task.

We consider the problem of privacy-sensitive anomaly detection - screening to detect individuals, behaviors, areas, or data samples of high interest. What defines an anomaly is context-specific; for example, a spoofed rather than genuine user attempting to log in to a web site, a fraudulent credit card transaction, or a suspicious traveler in an airport. The unifying assumption is that the number of anomalous points is quite small with respect to the population, so that deep screening of all individual data points would potentially be time-intensive, costly, and unnecessarily invasive of privacy. Such privacy violations can raise concerns due sensitive nature of data being used, raise fears about violations of data use agreements, and make people uncomfortable with anomaly detection methods. Anomaly detection is well studied, but methods to provide anomaly detection along with privacy are less well studied. Our overall goal in this research is to provide a framework for identifying anomalous data while guaranteeing quantifiable privacy in a rigorous sense. Once identified, such anomalies could warrant further data collection and investigation, depending on the context and relevant policies. In this research, we focus on privacy protection during the deployment of anomaly detection. Our main contribution is a differentially private access mechanism for finding anomalies using a search algorithm based on adaptive noisy group testing. To achieve this, we take as our starting point the notion of group testing [1], which was most famously used to screen US military draftees for syphilis during World War II. In group testing, individuals are tested in groups to limit the number of tests. Using multiple rounds of screenings, a small number of positive individuals can be detected very efficiently. Group testing has the added benefit of providing privacy to individuals through plausible deniability - since the group tests use aggregate data, individual contributions to the test are masked by the group. We follow on these concepts by demonstrating a search model utilizing adaptive queries on aggregated group data. Our work takes the first steps toward strengthening and formalizing these privacy concepts by achieving differential privacy [2]. Differential privacy is a statistical measure of disclosure risk that captures the intuition that an individual's privacy is protected if the results of a computation have at most a very small and quantifiable dependence on that individual's data. In the last decade, there hpractical adoption underway by high-profile companies such as Apple, Google, and Uber. In order to make differential privacy meaningful in the context of a task that seeks to specifically identify some (anomalous) individuals, we introduce the notion of anomaly-restricted differential privacy. Using ideas from information theory, we show that noise can be added to group query results in a way that provides differential privacy for non-anomalous individuals and still enables efficient and accurate detection of the anomalous individuals. Our method ensures that using differentially private aggregation of groups of points, providing privacy to individuals within the group while refining the group selection to the point that we can probabilistically narrow attention to a small numbers of individuals or samples for further attention. To summarize: We introduce a new notion of anomaly-restriction differential privacy, which may be of independent interest. We provide a noisy group-based search algorithm that satisfies the anomaly-restricted differential privacy definition. We provide both theoretical and empirical analysis of our noisy search algorithm, showing that it performs well in some cases, and exhibits the usual privacy/accuracy tradeoff of differentially private mechanisms. Potential anomaly detection applications for our work might include spatial search for outliers: this would rely on new sensing technologies that can perform queries in aggregate to reveal and isolate anomalous outliers. For example, this could lead to privacy-sensitive methods for searching for outlying cell phone activity patterns or Internet activity patterns in a geographic location.

In this paper, we present a differential privacy preserving approach, which extracts personality-based knowledge to serve privacy guarantee data analysis on personal sensitive data. Based on the approach, we further implement an end-to-end privacy guarantee system, KaPPA, to provide researchers iterative data analysis on sensitive data. The key challenge for differential privacy is determining a reasonable amount of privacy budget to balance privacy preserving and data utility. Most of the previous work applies unified privacy budget to all individual data, which leads to insufficient privacy protection for some individuals while over-protecting others. In KaPPA, the proposed personality-based privacy preserving approach automatically calculates privacy budget for each individual. Our experimental evaluations show a significant trade-off of sufficient privacy protection and data utility.

Lately, mobile operators experience threats from SMS grey routes which are used by fraudsters to evade SMS fees and to deny them millions in revenues. But more serious are the threats to the user's security and privacy and consequently the operator's reputation. Therefore, it is crucial for operators to have adequate solutions to protect both their network and their customers against this kind of fraud. Unfortunately, so far there is no sufficiently efficient countermeasure against grey routes. This paper proposes a near real time SMS grey traffic detection which makes use of Counting Bloom Filters combined with blacklist and whitelist to detect SMS grey traffic on the fly and to block them. The proposed detection has been implemented and proved to be quite efficient. The paper provides also comprehensive explanation of SMS grey routes and the challenges in their detection. The implementation and verification are also described thoroughly.

In machine learning, feature engineering has been a pivotal stage in building a high-quality predictor. Particularly, this work explores the multiple Kernel Discriminant Component Analysis (mKDCA) feature-map and its variants. However, seeking the right subset of kernels for mKDCA feature-map can be challenging. Therefore, we consider the problem of kernel selection, and propose an algorithm based on Differential Mutual Information (DMI) and incremental forward search. DMI serves as an effective metric for selecting kernels, as is theoretically supported by mutual information and Fisher's discriminant analysis. On the other hand, incremental forward search plays a role in removing redundancy among kernels. Finally, we illustrate the potential of the method via an application in privacy-aware classification, and show on three mobile-sensing datasets that selecting an effective set of kernels for mKDCA feature-maps can enhance the utility classification performance, while successfully preserve the data privacy. Specifically, the results show that the proposed DMI forward search method can perform better than the state-of-the-art, and, with much smaller computational cost, can perform as well as the optimal, yet computationally expensive, exhaustive search.

In this paper, we focus on developing a novel mechanism to preserve differential privacy in deep neural networks, such that: (1) The privacy budget consumption is totally independent of the number of training steps; (2) It has the ability to adaptively inject noise into features based on the contribution of each to the output; and (3) It could be applied in a variety of different deep neural networks. To achieve this, we figure out a way to perturb affine transformations of neurons, and loss functions used in deep neural networks. In addition, our mechanism intentionally adds "more noise" into features which are "less relevant" to the model output, and vice-versa. Our theoretical analysis further derives the sensitivities and error bounds of our mechanism. Rigorous experiments conducted on MNIST and CIFAR-10 datasets show that our mechanism is highly effective and outperforms existing solutions.

We propose a privacy-preserving framework for learning visual classifiers by leveraging distributed private image data. This framework is designed to aggregate multiple classifiers updated locally using private data and to ensure that no private information about the data is exposed during and after its learning procedure. We utilize a homomorphic cryptosystem that can aggregate the local classifiers while they are encrypted and thus kept secret. To overcome the high computational cost of homomorphic encryption of high-dimensional classifiers, we (1) impose sparsity constraints on local classifier updates and (2) propose a novel efficient encryption scheme named doublypermuted homomorphic encryption (DPHE) which is tailored to sparse high-dimensional data. DPHE (i) decomposes sparse data into its constituent non-zero values and their corresponding support indices, (ii) applies homomorphic encryption only to the non-zero values, and (iii) employs double permutations on the support indices to make them secret. Our experimental evaluation on several public datasets shows that the proposed approach achieves comparable performance against state-of-the-art visual recognition methods while preserving privacy and significantly outperforms other privacy-preserving methods.

Building lightweight security for low-cost pervasive devices is a major challenge considering the design requirements of a small footprint and low power consumption. Physical Unclonable Functions (PUFs) have emerged as a promising technology to provide a low-cost authentication for such devices. By exploiting intrinsic manufacturing process variations, PUFs are able to generate unique and apparently random chip identifiers. Strong-PUFs represent a variant of PUFs that have been suggested for lightweight authentication applications. Unfortunately, many of the Strong-PUFs have been shown to be susceptible to modelling attacks (i.e., using machine learning techniques) in which an adversary has access to challenge and response pairs. In this study, we propose an obfuscation technique during post-processing of Strong-PUF responses to increase the resilience against machine learning attacks. We conduct machine learning experiments using Support Vector Machines and Artificial Neural Networks on two Strong-PUFs: a 32-bit Arbiter-PUF and a 2-XOR 32-bit Arbiter-PUF. The predictability of the 32-bit Arbiter-PUF is reduced to $\approx$ 70% by using an obfuscation technique. Combining the obfuscation technique with 2-XOR 32-bit Arbiter-PUF helps to reduce the predictability to $\approx$ 64%. More reduction in predictability has been observed in an XOR Arbiter-PUF because this PUF architecture has a good uniformity. The area overhead with an obfuscation technique consumes only 788 and 1080 gate equivalents for the 32-bit Arbiter-PUF and 2-XOR 32-bit Arbiter-PUF, respectively.

The IoT (Internet of Things) is one of the primary reasons for the massive growth in the number of connected devices to the Internet, thus leading to an increased volume of traffic in the core network. Fog and edge computing are becoming a solution to handle IoT traffic by moving timesensitive processing to the edge of the network, while using the conventional cloud for historical analysis and long-term storage. Providing processing, storage, and network communication at the edge network are the aim of fog computing to reduce delay, network traffic, and decentralise computing. In this paper, we define a framework that realises fog computing that can be extended to install any service of choice. Our framework utilises fog nodes as an extension of the traditional switch to include processing, networking, and storage. The fog nodes act as local decision-making elements that interface with software-defined networking (SDN), to be able to push updates throughout the network. To test our framework, we develop an IP spoofing security application and ensure its correctness through multiple experiments.

Cyber-Physical Systems (CPS) such as Unmanned Aerial Systems (UAS) sense and actuate their environment in pursuit of a mission. The attack surface of these remotely located, sensing and communicating devices is both large, and exposed to adversarial actors, making mission assurance a challenging problem. While best-practice security policies should be followed, they are rarely enough to guarantee mission success as not all components in the system may be trusted and the properties of the environment (e.g., the RF environment) may be under the control of the attacker. CPS must thus be built with a high degree of resilience to mitigate threats that security cannot alleviate. In this paper, we describe the Agile and Resilient Embedded Systems (ARES) methodology and metric set. The ARES methodology pursues cyber security and resilience (CSR) as high level system properties to be developed in the context of the mission. An analytic process guides system developers in defining mission objectives, examining principal issues, applying CSR technologies, and understanding their interactions.