Biblio

The IETF has developed protocols that promote a healthy IPv4 and IPv6 co-existence. The Happy Eyeballs (HE) algorithm, for instance, prevents bad user experience in situations where IPv6 connectivity is broken. Using an active test (happy) that measures TCP connection establishment times, we evaluate the effects of the HE algorithm. The happy test measures against ALEXA top 10K websites from 80 SamKnows probes connected to dual-stacked networks representing 58 different ASes. Using a 3-years long (2013 - 2016) dataset, we show that TCP connect times to popular websites over IPv6 have considerably improved over time. As of May 2016, 18% of these websites are faster over IPv6 with 91% of the rest at most 1 ms slower. The historical trend shows that only around 1% of the TCP connect times over IPv6 were ever above the HE timer value (300 ms), which leaves around 2% chance for IPv4 to win a HE race towards these websites. As such, 99% of these websites prefer IPv6 connections more than 98% of the time. We show that although absolute TCP connect times (in ms) are not that far apart in both address families, HE with a 300 ms timer value tends to prefer slower IPv6 connections in around 90% of the cases. We show that lowering the HE timer value to 150 ms gives us a margin benefit of 10% while retaining same preference levels over IPv6.

Millions of users worldwide resort to mobile VPN clients to either circumvent censorship or to access geo-blocked content, and more generally for privacy and security purposes. In practice, however, users have little if any guarantees about the corresponding security and privacy settings, and perhaps no practical knowledge about the entities accessing their mobile traffic. In this paper we provide a first comprehensive analysis of 283 Android apps that use the Android VPN permission, which we extracted from a corpus of more than 1.4 million apps on the Google Play store. We perform a number of passive and active measurements designed to investigate a wide range of security and privacy features and to study the behavior of each VPN-based app. Our analysis includes investigation of possible malware presence, third-party library embedding, and traffic manipulation, as well as gauging user perception of the security and privacy of such apps. Our experiments reveal several instances of VPN apps that expose users to serious privacy and security vulnerabilities, such as use of insecure VPN tunneling protocols, as well as IPv6 and DNS traffic leakage. We also report on a number of apps actively performing TLS interception. Of particular concern are instances of apps that inject JavaScript programs for tracking, advertising, and for redirecting e-commerce traffic to external partners.

In today's enterprise networks, there are many ways for a determined attacker to obtain a foothold, bypass current protection technologies, and attack the intended target. Over several years we have developed the Self-shielding Dynamic Network Architecture (SDNA) technology, which prevents an attacker from targeting, entering, or spreading through an enterprise network by adding dynamics that present a changing view of the network over space and time. SDNA was developed with the support of government sponsored research and development and corporate internal resources. The SDNA technology was purchased by Cryptonite, LLC in 2015 and has been developed into a robust product offering called Cryptonite NXT. In this paper, we describe the journey and lessons learned along the course of feasibility demonstration, technology development, security testing, productization, and deployment in a production network.

This paper outlines a set of 10 cyber security concerns associated with Industrial Control Systems (ICS). The concerns address software and hardware development, implementation, and maintenance practices, supply chain assurance, the need for cyber forensics in ICS, a lack of awareness and training, and finally, a need for test beds which can be used to address the first 9 cited concerns. The concerns documented in this paper were developed based on the authors' combined experience conducting research in this field for the US Department of Homeland Security, the National Science Foundation, and the Department of Defense. The second half of this paper documents a virtual test bed platform which is offered as a tool to address the concerns listed in the first half of the paper. The paper discusses various types of test beds proposed in literature for ICS research, provides an overview of the virtual test bed platform developed by the authors, and lists future works required to extend the existing test beds to serve as a development platform.

A major component of modern vehicles is the infotainment system, which interfaces with its drivers and passengers. Other mobile devices, such as handheld phones and laptops, can relay information to the embedded infotainment system through Bluetooth and vehicle WiFi. The ability to extract information from these systems would help forensic analysts determine the general contents that is stored in an infotainment system. Based off the data that is extracted, this would help determine what stored information is relevant to law enforcement agencies and what information is non-essential when it comes to solving criminal activities relating to the vehicle itself. This would overall solidify the Intelligent Transport System and Vehicular Ad Hoc Network infrastructure in combating crime through the use of vehicle forensics. Additionally, determining the content of these systems will allow forensic analysts to know if they can determine anything about the end-user directly and/or indirectly.

Modern OS kernels including Windows, Linux, and Mac OS all have adopted kernel Address Space Layout Randomization (ASLR), which shifts the base address of kernel code and data into different locations in different runs. Consequently, when performing introspection or forensic analysis of kernel memory, we cannot use any pre-determined addresses to interpret the kernel events. Instead, we must derandomize the address space layout and use the new addresses. However, few efforts have been made to derandomize the kernel address space and yet there are many questions left such as which approach is more efficient and robust. Therefore, we present the first systematic study of how to derandomize a kernel when given a memory snapshot of a running kernel instance. Unlike the derandomization approaches used in traditional memory exploits in which only remote access is available, with introspection and forensics applications, we can use all the information available in kernel memory to generate signatures and derandomize the ASLR. In other words, there exists a large volume of solutions for this problem. As such, in this paper we examine a number of typical approaches to generate strong signatures from both kernel code and data based on the insight of how kernel code and data is updated, and compare them from efficiency (in terms of simplicity, speed etc.) and robustness (e.g., whether the approach is hard to be evaded or forged) perspective. In particular, we have designed four approaches including brute-force code scanning, patched code signature generation, unpatched code signature generation, and read-only pointer based approach, according to the intrinsic behavior of kernel code and data with respect to kernel ASLR. We have gained encouraging results for each of these approaches and the corresponding experimental results are reported in this paper.

Social Networking is fundamentally shifting the way we communicate, sharing idea and form opinions. All people try to use social media for there need, people from every age group are involved in social media site or e-commerce site. Nowadays almost every illegal activity is happened using the social network and instant messages. It means that present system is not capable to found all suspicious words. In this paper, we provided a brief description of problem and review on the different framework developed so far. Propose a better system which can be indentify criminal activity through social networking more efficiently. Use Ontology Based Information Extraction (OBIE) technique to identify domain of word and Association Rule mining to generate rules. Heuristic method checks in user database for malicious users according to predefine elements and Naïve Bayes method is use to identify the context behind the message or post. The experimental result is used for further action on victim by cyber crime department.

With the outgrowth of video editing tools, video information trustworthiness becomes a hypersensitive field. Today many devices have the capability of capturing digital videos such as CCTV, digital cameras and mobile phones and these videos may transmitted over the Internet or any other non secure channel. As digital video can be used to as supporting evidence, it has to be protected against manipulation or tampering. As most video authentication techniques are based on watermarking and digital signatures, these techniques are effectively used in copyright purposes but difficult to implement in other cases such as video surveillance or in videos captured by consumer's cameras. In this paper we propose an intelligent technique for video authentication which uses the video local information which makes it useful for real world applications. The proposed algorithm relies on the video's statistical local information which was applied on a dataset of videos captured by a range of consumer video cameras. The results show that the proposed algorithm has potential to be a reliable intelligent technique in digital video authentication without the need to use for SVM classifier which makes it faster and less computationally expensive in comparing with other intelligent techniques.

Intrusive multi-step attacks, such as Advanced Persistent Threat (APT) attacks, have plagued enterprises with significant financial losses and are the top reason for enterprises to increase their security budgets. Since these attacks are sophisticated and stealthy, they can remain undetected for years if individual steps are buried in background "noise." Thus, enterprises are seeking solutions to "connect the suspicious dots" across multiple activities. This requires ubiquitous system auditing for long periods of time, which in turn causes overwhelmingly large amount of system audit events. Given a limited system budget, how to efficiently handle ever-increasing system audit logs is a great challenge. This paper proposes a new approach that exploits the dependency among system events to reduce the number of log entries while still supporting high-quality forensic analysis. In particular, we first propose an aggregation algorithm that preserves the dependency of events during data reduction to ensure the high quality of forensic analysis. Then we propose an aggressive reduction algorithm and exploit domain knowledge for further data reduction. To validate the efficacy of our proposed approach, we conduct a comprehensive evaluation on real-world auditing systems using log traces of more than one month. Our evaluation results demonstrate that our approach can significantly reduce the size of system logs and improve the efficiency of forensic analysis without losing accuracy.

We address the known problem of detecting a previous compression in JPEG images, focusing on the challenging case of high and very high quality factors (textgreater= 90) as well as repeated compression with identical or nearly identical quality factors. We first revisit the approaches based on Benford–Fourier analysis in the DCT domain and block convergence analysis in the spatial domain. Both were originally conceived for specific scenarios. Leveraging decision tree theory, we design a combined approach complementing the discriminatory capabilities. We obtain a set of novel detectors targeted to high quality grayscale JPEG images.

There has been growing interest in using convolutional neural networks (CNNs) in the fields of image forensics and steganalysis, and some promising results have been reported recently. These works mainly focus on the architectural design of CNNs, usually, a single CNN model is trained and then tested in experiments. It is known that, neural networks, including CNNs, are suitable to form ensembles. From this perspective, in this paper, we employ CNNs as base learners and test several different ensemble strategies. In our study, at first, a recently proposed CNN architecture is adopted to build a group of CNNs, each of them is trained on a random subsample of the training dataset. The output probabilities, or some intermediate feature representations, of each CNN, are then extracted from the original data and pooled together to form new features ready for the second level of classification. To make best use of the trained CNN models, we manage to partially recover the lost information due to spatial subsampling in the pooling layers when forming feature vectors. Performance of the ensemble methods are evaluated on BOSSbase by detecting S-UNIWARD at 0.4 bpp embedding rate. Results have indicated that both the recovery of the lost information, and learning from intermediate representation in CNNs instead of output probabilities, have led to performance improvement.

This paper proposes a forensic method for identifying whether an image was previously compressed by JPEG and also proposes an improved anti-forensics method to enhance the quality of noise added image. Stamm and Liu's anti-forensics method disable the detection capabilities of various forensics methods proposed in the literature, used for identifying the compressed images. However, it also degrades the quality of the image. First, we analyze the anti-forensics method and then use the decimal histogram of the coefficients to distinguish the never compressed images from the previously compressed; even the compressed image processed anti-forensically. After analyzing the noise distribution in the AF image, we propose a method to remove the Gaussian noise caused by image dithering which in turn enhances the image quality. The paper is organized in the following manner: Section I is the introduction, containing previous literature. Section II briefs Anti-forensic method proposed by Stamm et al. In section III, we have proposed a forensic approach and section IV comprises of improved anti-forensic approach. Section V covers details of experimentation followed by the conclusion.

The usage of Information and Communication Technologies (ICTs) pervades everyday's life. If it is true that ICT contributed to improve the quality of our life, it is also true that new forms of (cyber)crime have emerged in this setting. The diversity and amount of information forensic investigators need to cope with, when tackling a cyber-crime case, call for tools and techniques where knowledge is the main actor. Current approaches leave to the investigator the chore of integrating the diverse sources of evidence relevant for a case thus hindering the automatic generation of reusable knowledge. This paper describes an architecture that lifts the classical phases of a digital forensic investigation to a knowledge-driven setting. We discuss how the usage of languages and technologies originating from the Semantic Web proposal can complement digital forensics tools so that knowledge becomes a first-class citizen. Our architecture enables to perform in an integrated way complex forensic investigations and, as a by-product, build a knowledge base that can be consulted to gain insights from previous cases. Our proposal has been inspired by real-world scenarios emerging in the context of an Italian research project about cyber security.

Given a stream of heterogeneous graphs containing different types of nodes and edges, how can we spot anomalous ones in real-time while consuming bounded memory? This problem is motivated by and generalizes from its application in security to host-level advanced persistent threat (APT) detection. We propose StreamSpot, a clustering based anomaly detection approach that addresses challenges in two key fronts: (1) heterogeneity, and (2) streaming nature. We introduce a new similarity function for heterogeneous graphs that compares two graphs based on their relative frequency of local substructures, represented as short strings. This function lends itself to a vector representation of a graph, which is (a) fast to compute, and (b) amenable to a sketched version with bounded size that preserves similarity. StreamSpot exhibits desirable properties that a streaming application requires: it is (i) fully-streaming; processing the stream one edge at a time as it arrives, (ii) memory-efficient; requiring constant space for the sketches and the clustering, (iii) fast; taking constant time to update the graph sketches and the cluster summaries that can process over 100,000 edges per second, and (iv) online; scoring and flagging anomalies in real time. Experiments on datasets containing simulated system-call flow graphs from normal browser activity and various attack scenarios (ground truth) show that StreamSpot is high-performance; achieving above 95% detection accuracy with small delay, as well as competitive time and memory usage.  

One step involved in the security engineering process is threat modeling. Threat modeling involves understanding the complexity of the system and identifying all of the possible threats, regardless of whether or not they can be exploited. Proper identification of threats and appropriate selection of countermeasures reduces the ability of attackers to misuse the system. This paper presents a quantitative, integrated threat modeling approach that merges software and attack centric threat modeling techniques. The threat model is composed of a system model representing the physical and network infrastructure layout, as well as a component model illustrating component specific threats. Component attack trees allow for modeling specific component contained attack vectors, while system attack graphs illustrate multi-component, multi-step attack vectors across the system. The Common Vulnerability Scoring System (CVSS) is leveraged to provide a standardized method of quantifying the low level vulnerabilities in the attack trees. As a case study, a railway communication network is used, and the respective results using a threat modeling software tool are presented.

We present D-ForenRIA, a distributed forensic tool to automatically reconstruct user-sessions in Rich Internet Applications (RIAs), using solely the full HTTP traces of the sessions as input. D-ForenRIA recovers automatically each browser state, reconstructs the DOMs and re-creates screenshots of what was displayed to the user. The tool also recovers every action taken by the user on each state, including the user-input data. Our application domain is security forensics, where sometimes months-old sessions must be quickly reconstructed for immediate inspection. We will demonstrate our tool on a series of RIAs, including a vulnerable banking application created by IBM Security for testing purposes. In that case study, the attacker visits the vulnerable web site, and exploits several vulnerabilities (SQL-injections, XSS...) to gain access to private information and to perform unauthorized transactions. D-ForenRIA can reconstruct the session, including screenshots of all pages seen by the hacker, DOM of each page and the steps taken for unauthorized login and the inputs hacker exploited for the SQL-injection attack. D-ForenRIA is made efficient by applying advanced reconstruction techniques and by using several browsers concurrently to speed up the reconstruction process. Although we developed D-ForenRIA in the context of security forensics, the tool can also be useful in other contexts such as aided RIAs debugging and automated RIAs scanning.

After more than a decade of research, web application security continues to be a challenge and the backend database the most appetizing target. The paper proposes preventing injection attacks against the database management system (DBMS) behind web applications by embedding protections in the DBMS itself. The motivation is twofold. First, the approach of embedding protections in operating systems and applications running on top of them has been effective to protect this software. Second, there is a semantic mismatch between how SQL queries are believed to be executed by the DBMS and how they are actually executed, leading to subtle vulnerabilities in prevention mechanisms. The approach – SEPTIC – was implemented in MySQL and evaluated experimentally with web applications written in PHP and Java/Spring. In the evaluation SEPTIC has shown neither false negatives nor false positives, on the contrary of alternative approaches, causing also a low performance overhead in the order of 2.2%.

In this paper, we have mentioned a method to find the performance of projectwhich detects various web - attacks. The project is capable to identifying and preventing attacks like SQL Injection, Cross – Site Scripting, URL rewriting, Web server 400 error code etc. The performance of system is detected using the system attributes that are mentioned in this paper. This is also used to determine efficiency of the system.

As web applications is becoming more prominent due to the ubiquity of web services, web applications have become main targets for attackers. In order to steal or leak sensitive user data managed by web applications, attackers exploit a wide range of input validation vulnerabilities such as SQL injection, path traversal (or directory traversal), cross-site scripting (XSS), etc. This paper propose a technique that can verify input values of Java-based web applications using static bytecode instrumentation and runtime input validation. The technique searches for target methods or object constructors in compiled Java class files, and statically inserts bytecode modules. At runtime, the instrumented bytecode modules validate input values of the targets, and take countermeasure against malicious inputs. The proposed technique can mitigate the input validation vulnerabilities in Java-based web applications without source codes. To evaluate the effectiveness of the proposed technique, experiments are carried out with an insecure web application maintained by OWASP WebGoat Project. The experimental results show that the proposed technique successfully mitigates input validation vulnerabilities such as SQL injection and path traversal.

Security testing is a pivotal activity in engineering secure software. It consists of two phases: generating attack inputs to test the system, and assessing whether test executions expose any vulnerabilities. The latter phase is known as the security oracle problem. In this work, we present SOFIA, a Security Oracle for SQL-Injection Vulnerabilities. SOFIA is programming-language and source-code independent, and can be used with various attack generation tools. Moreover, because it does not rely on known attacks for learning, SOFIA is meant to also detect types of SQLi attacks that might be unknown at learning time. The oracle challenge is recast as a one-class classification problem where we learn to characterise legitimate SQL statements to accurately distinguish them from SQLi attack statements. We have carried out an experimental validation on six applications, among which two are large and widely-used. SOFIA was used to detect real SQLi vulnerabilities with inputs generated by three attack generation tools. The obtained results show that SOFIA is computationally fast and achieves a recall rate of 100% (i.e., missing no attacks) with a low false positive rate (0.6%).

The combination of (1) hard to eradicate low-level vulnerabilities, (2) a large trusted computing base written in a memory-unsafe language and (3) a desperate need to provide strong software security guarantees, led to the development of protected-module architectures. Such architectures provide strong isolation of protected modules: Security of code and data depends only on a module's own implementation. In this paper we discuss how such protected modules should be written. From an academic perspective it is clear that the future lies with memory-safe languages. Unfortunately, from a business and management perspective, that is a risky path and will remain so in the near future. The use of well-known but memory-unsafe languages such as C and C++ seem inevitable. We argue that the academic world should take another look at the automatic hardening of software written in such languages to mitigate low-level security vulnerabilities. This is a well-studied topic for full applications, but protected-module architectures introduce a new, and much more challenging environment. Porting existing security measures to a protected-module setting without a thorough security analysis may even harm security of the protected modules they try to protect.

All modern web browsers –- Internet Explorer, Firefox, Chrome, Opera, and Safari –- have a core rendering engine written in C++. This language choice was made because it affords the systems programmer complete control of the underlying hardware features and memory in use, and it provides a transparent compilation model. Unfortunately, this language is complex (especially to new contributors!), challenging to write correct parallel code in, and highly susceptible to memory safety issues that potentially lead to security holes. Servo is a project started at Mozilla Research to build a new web browser engine that preserves the capabilities of these other browser engines but also both takes advantage of the recent trends in parallel hardware and is more memory-safe. We use a new language, Rust, that provides us a similar level of control of the underlying system to C++ but which statically prevents many memory safety issues and provides direct support for parallelism and concurrency. In this paper, we show how a language with an advanced type system can address many of the most common security issues and software engineering challenges in other browser engines, while still producing code that has the same performance and memory profile. This language is also quite accessible to new open source contributors and employees, even those without a background in C++ or systems programming. We also outline several pitfalls encountered along the way and describe some potential areas for future improvement.

We explore the use of a new way to log into a web service, such as email or social media. Using on-demand biometrics, users sign in from a browser on a computer using just their name, which sends a request to their phone for approval. Users approve this request by authenticating on their phone using their fingerprint, which completes the login in the browser. On-demand biometrics thus replace passwords or temporary access codes found in two-step verification with the ease of use of biometrics. We present the results of an interview study on the use of on-demand biometrics with a live login backend. Participants perceived our system as convenient and fast to use and also expressed their trust in fingerprint authentication to keep their accounts safe. We motivate the design of on-demand biometrics, present an analysis of participants' use and responses around general account security and authentication, and conclude with implications for designing fast and easy cross-device authentication.

Past generations of software developers were well on the way to building a software engineering mindset/gestalt, preferring tools and techniques that concentrated on safety, security, reliability, and code re-usability. Computing education reflected these priorities and was, to a great extent organized around these themes, providing beginning software developers a basis for professional practice. In more recent times, economic and deadline pressures and the de-professionalism of practitioners have combined to drive a development agenda that retains little respect for quality considerations. As a result, we are now deep into a new and severe software crisis. Scarcely a day passes without news of either a debilitating data or website hack, or the failure of a mega-software project. Vendors, individual developers, and possibly educators can anticipate an equally destructive flood of malpractice litigation, for the argument that they systematically and recklessly ignored known best development practice of long standing is irrefutable. Yet we continue to instruct using methods and to employ development tools we know, or ought to know, are inherently insecure, unreliable, and unsafe, and that produce software of like ilk. The authors call for a renewed professional and educational focus on software quality, focusing on redesigned tools that enable and encourage known best practice, combined with reformed educational practices that emphasize writing human readable, safe, secure, and reliable software. Practitioners can only deploy sound management techniques, appropriate tool choice, and best practice development methodologies such as thorough planning and specification, scope management, factorization, modularity, safety, appropriate team and testing strategies, if those ideas and techniques are embedded in the curriculum from the beginning. The authors have instantiated their ideas in the form of their highly disciplined new version of Niklaus Wirth's 1980s Modula-2 programming notation under the working moniker Modula-2 R10. They are now working on an implementation that will be released under a liberal open source license in the hope that it will assist in reforming the CS curriculum around a best practices core so as to empower would-be professionals with the intellectual and practical mindset to begin resolving the software crisis. They acknowledge there is no single software engineering silver bullet, but assert that professional techniques can be inculcated throughout a student's four-year university tenure, and if implemented in the workplace, these can greatly reduce the likelihood of multiplied IT failures at the hands of our graduates. The authors maintain that professional excellence is a necessary mindset, a habit of self-discipline that must be intentionally embedded in all aspects of one's education, and subsequently drive all aspects of one's practice, including, but by no means limited to, the choice and use of programming tools.

We present a code- and input-sensitive sanitization synthesis approach for repairing string vulnerabilities that are common in web applications. The synthesized sanitization patch modifies the user input in an optimal way while guaranteeing that the repaired web application is not vulnerable. Given a web application, an input pattern and an attack pattern, we use automata-based static string analysis techniques to compute a sanitization signature that characterizes safe input values that obey the given input pattern and are safe with respect to the given attack pattern. Using the sanitization signature, we synthesize an optimal sanitization patch that converts malicious user inputs to benign ones with minimal editing. When the generated patch is added to the web application, it is guaranteed that the repaired web application is no longer vulnerable. We present refinements to previous sanitization synthesis algorithms that reduce the runtime sanitization cost significantly. We evaluate our approach on open source web applications using common input and attack patterns, demonstrating the effectiveness of our approach.