Biblio

Current software platforms for service composition are based on orchestration, choreography or hierarchical orchestration. However, such approaches for service composition only support partial compositionality; thereby, increasing the complexity of SOA development. In this paper, we propose DX-MAN, a platform that supports total compositionality. We describe the main concepts of DX-MAN with the help of a case study based on the popular MusicCorp.

Dependency injection frameworks such as the Spring framework rely on dynamic language features of Java. Errors arising from the improper usage of these features bypass the compile-time checks of the Java compiler. This paper discusses the application of static code analysis as a means to restore compile-time checking for Spring-related configuration errors. First, possible errors in the configuration of Spring are identified and classified. Attributed grammars are applied in order to formally detect the errors and a prototypical compiler extension is implemented based on Java's pluggable annotation processing API.

Dynamic software updating (DSU) is an extremely useful feature to be used during software evolution. It can be used to reduce down-time costs, for security enhancements, profiling and testing new functionalities. There are many studies and solutions on dynamic software updating regarding diverse problems introduced by the topic, but there is a lack of research which compares various approaches concerning supported changes and demands on resources. In this paper, we are comparing currently available concepts for Java programming language that deal with dynamically applied changes and measuring the impact of those changes on computer resource demands.

Many popular web applications incorporate end-toend secure messaging protocols, which seek to ensure that messages sent between users are kept confidential and authenticated, even if the web application's servers are broken into or otherwise compelled into releasing all their data. Protocols that promise such strong security guarantees should be held up to rigorous analysis, since protocol flaws and implementations bugs can easily lead to real-world attacks. We propose a novel methodology that allows protocol designers, implementers, and security analysts to collaboratively verify a protocol using automated tools. The protocol is implemented in ProScript, a new domain-specific language that is designed for writing cryptographic protocol code that can both be executed within JavaScript programs and automatically translated to a readable model in the applied pi calculus. This model can then be analyzed symbolically using ProVerif to find attacks in a variety of threat models. The model can also be used as the basis of a computational proof using CryptoVerif, which reduces the security of the protocol to standard cryptographic assumptions. If ProVerif finds an attack, or if the CryptoVerif proof reveals a weakness, the protocol designer modifies the ProScript protocol code and regenerates the model to enable a new analysis. We demonstrate our methodology by implementing and analyzing a variant of the popular Signal Protocol with only minor differences. We use ProVerif and CryptoVerif to find new and previously-known weaknesses in the protocol and suggest practical countermeasures. Our ProScript protocol code is incorporated within the current release of Cryptocat, a desktop secure messenger application written in JavaScript. Our results indicate that, with disciplined programming and some verification expertise, the systematic analysis of complex cryptographic web applications is now becoming practical.

Data security has become an issue of increasing importance, especially for Web applications and distributed databases. One solution is using cryptographic algorithms whose improvement has become a constant concern. The increasing complexity of these algorithms involves higher execution times, leading to an application performance decrease. This paper presents a comparison of execution times for three algorithms using asymmetric keys, depending on the size of the encryption/decryption keys: RSA, ElGamal, and ECIES. For this algorithms comparison, a benchmark using Java APIs and an application for testing them on a test database was created.

The growing popularity of Android applications makes them vulnerable to security threats. There exist several studies that focus on the analysis of the behaviour of Android applications to detect the repackaged and malicious ones. These techniques use a variety of features to model the application's behaviour, among which the calls to Android API, made by the application components, are shown to be the most reliable. To generate the APIs that an application calls is not an easy task. This is because most malicious applications are obfuscated and do not come with the source code. This makes the problem of identifying the API methods invoked by an application an interesting research issue. In this paper, we present HyDroid, a hybrid approach that combines static and dynamic analysis to generate API call traces from the execution of an application's services. We focus on services because they contain key characteristics that allure attackers to misuse them. We show that HyDroid can be used to extract API call trace signatures of several malware families.

Ensuring software security is essential for developing a reliable software. A software can suffer from security problems due to the weakness in code constructs during software development. Our goal is to relate software security with different code constructs so that developers can be aware very early of their coding weaknesses that might be related to a software vulnerability. In this study, we chose Java nano-patterns as code constructs that are method-level patterns defined on the attributes of Java methods. This study aims to find out the correlation between software vulnerability and method-level structural code constructs known as nano-patterns. We found the vulnerable methods from 39 versions of three major releases of Apache Tomcat for our first case study. We extracted nano-patterns from the affected methods of these releases. We also extracted nano-patterns from the non-vulnerable methods of Apache Tomcat, and for this, we selected the last version of three major releases (6.0.45 for release 6, 7.0.69 for release 7 and 8.0.33 for release 8) as the non-vulnerable versions. Then, we compared the nano-pattern distributions in vulnerable versus non-vulnerable methods. In our second case study, we extracted nano-patterns from the affected methods of three vulnerable J2EE web applications: Blueblog 1.0, Personalblog 1.2.6 and Roller 0.9.9, all of which were deliberately made vulnerable for testing purpose. We found that some nano-patterns such as objCreator, staticFieldReader, typeManipulator, looper, exceptions, localWriter, arrReader are more prevalent in affected methods whereas some such as straightLine are more vivid in non-affected methods. We conclude that nano-patterns can be used as the indicator of vulnerability-proneness of code.

Cryptography is the fascinating science that deals with constructing and destructing the secret codes. The evolving digitization in this modern era possesses cryptography as one of its backbones to perform the transactions with confidentiality and security wherever the authentication is required. With the modern technology that has evolved, the use of codes has exploded, enriching cryptology and empowering citizens. One of the most important things that encryption provides anyone using any kind of computing device is `privacy'. There is no way to have true privacy with strong security, the method with which we are dealing with is to make the cipher text more robust to be by-passed. In current work, the well known and renowned Caesar cipher and Rail fence cipher techniques are combined with a cross language cipher technique and the detailed comparative analysis amongst them is carried out. The simulations have been carried out on Eclipse Juno version IDE for executions and Java, an open source language has been used to implement these said techniques.

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.

Security protocols are critical components for the construction of secure and dependable distributed applications, but their implementation is challenging and error prone. Therefore, tools for formal modelling and analysis of security protocols can be potentially very useful to support software engineers. However, despite such tools have been available for a long time, their adoption outside the research community has been very limited. In fact, most practitioners find such applications too complex and hardly usable for their daily work. In this paper, we present an Integrated Development Environment for the design, verification and implementation of security protocols, aimed at lowering the adoption barrier of formal methods tools for security. In the spirit of Model Driven Development, the environment supports the user in the specification of the model using the simple and intuitive language AnB (and its extension AnBx). Moreover, it provides a push-button solution for the formal verification of the abstract and concrete models, and for the automatic generation of Java implementation. This Eclipse-based IDE leverages on existing languages and tools for modelling and verification of security protocols, such as the AnBx Compiler and Code Generator, the model checker OFMC and the protocol verifier ProVerif.

Software security is an important aspect of ensuring software quality. Early detection of vulnerable code during development is essential for the developers to make cost and time effective software testing. The traditional software metrics are used for early detection of software vulnerability, but they are not directly related to code constructs and do not specify any particular granularity level. The goal of this study is to help developers evaluate software security using class-level traceable patterns called micro patterns to reduce security risks. The concept of micro patterns is similar to design patterns, but they can be automatically recognized and mined from source code. If micro patterns can better predict vulnerable classes compared to traditional software metrics, they can be used in developing a vulnerability prediction model. This study explores the performance of class-level patterns in vulnerability prediction and compares them with traditional class-level software metrics. We studied security vulnerabilities as reported for one major release of Apache Tomcat, Apache Camel and three stand-alone Java web applications. We used machine learning techniques for predicting vulnerabilities using micro patterns and class-level metrics as features. We found that micro patterns have higher recall in detecting vulnerable classes than the software metrics.

The inherent nature of unattended sensors makes these devices most vulnerable to detection, exploitation, and denial in contested environments. Physical access is often cited as the easiest way to compromise any device or network. A new mechanism for mitigating these types of attacks developed under the Assistant Secretary of Defense for Research and Engineering, ASD(R&E) project, “Smoke Screen in Cyberspace”, was previously demonstrated in a live, over-the-air experiment. Smoke Screen encrypts, slices up, and disburses redundant fragments of files throughout the network. This paper describes enhancements to the disbursement of the file fragments routing improving the efficiency and time to completion of fragment distribution by defining the exact route, fragments should take to the destination. This is the first step in defining a custom protocol for the discovery of participating nodes and the efficient distribution of fragments in a mobile network. Future work will focus on the movement of fragments to avoid traffic analysis and avoid the collection of the entire fragment set that would enable an adversary to reconstruct the original piece of data.

The best practice to prevent Cross Site Scripting (XSS) attacks is to apply encoders to sanitize untrusted data. To balance security and functionality, encoders should be applied to match the web page context, such as HTML body, JavaScript, and style sheets. A common programming error is the use of a wrong encoder to sanitize untrusted data, leaving the application vulnerable. We present a security unit testing approach to detect XSS vulnerabilities caused by improper encoding of untrusted data. Unit tests for the XSS vulnerability are automatically constructed out of each web page and then evaluated by a unit test execution framework. A grammar-based attack generator is used to automatically generate test inputs. We evaluate our approach on a large open source medical records application, demonstrating that we can detect many 0-day XSS vulnerabilities with very low false positives, and that the grammar-based attack generator has better test coverage than industry best practices.

A cyber-attack detection system issues alerts when an attacker attempts to coerce a trusted software application to perform unsafe actions on the attacker's behalf. One way of issuing such alerts is to create an application-agnostic cyber- attack detection system that responds to prevalent software vulnerabilities. The creation of such an autonomic alert system, however, is impeded by the disparity between implementation language, function, quality-of-service (QoS) requirements, and architectural patterns present in applications, all of which contribute to the rapidly changing threat landscape presented by modern heterogeneous software systems. This paper evaluates the feasibility of creating an autonomic cyber-attack detection system and applying it to several exemplar web-based applications using program transformation and machine learning techniques. Specifically, we examine whether it is possible to detect cyber-attacks (1) online, i.e., as they occur using lightweight structures derived from a call graph and (2) offline, i.e., using machine learning techniques trained with features extracted from a trace of application execution. In both cases, we first characterize normal application behavior using supervised training with the test suites created for an application as part of the software development process. We then intentionally perturb our test applications so they are vulnerable to common attack vectors and then evaluate the effectiveness of various feature extraction and learning strategies on the perturbed applications. Our results show that both lightweight on-line models based on control flow of execution path and application specific off-line models can successfully and efficiently detect in-process cyber-attacks against web applications.

Software components, which are vulnerable to being exploited, need to be identified and patched. Employing any prevention techniques designed for the purpose of detecting vulnerable software components in early stages can reduce the expenses associated with the software testing process significantly and thus help building a more reliable and robust software system. Although previous studies have demonstrated the effectiveness of adapting prediction techniques in vulnerability detection, the feasibility of those techniques is limited mainly because of insufficient training data sets. This paper proposes a prediction technique targeting at early identification of potentially vulnerable software components. In the proposed scheme, the potentially vulnerable components are viewed as mislabeled data that may contain true but not yet observed vulnerabilities. The proposed hybrid technique combines the supports vector machine algorithm and ensemble learning strategy to better identify potential vulnerable components. The proposed vulnerability detection scheme is evaluated using some Java Android applications. The results demonstrated that the proposed hybrid technique could identify potentially vulnerable classes with high precision and relatively acceptable accuracy and recall.

Internet of Things is gaining research attention as one of the important fields that will affect our daily life vastly. Today, around us this revolutionary technology is growing and evolving day by day. This technology offers certain benefits like automatic processing, improved logistics and device communication that would help us to improve our social life, health, living standards and infrastructure. However, due to their simple architecture and presence on wide variety of fields they pose serious concern to security. Due to the low end architecture there are many security issues associated with IoT network devices. In this paper, we try to address the security issue by proposing JavaScript sandbox as a method to execute IoT program. Using this sandbox we also implement the strategy to control the execution of the sandbox while the program is being executed on it.

Android is currently the most widely used mobile environment. This trend encourages malware writers to develop specific attacks targeting this platform with threats designed to covertly collect data or financially extort victims, the so-called ransomware. In this paper we use formal methods, in particular model checking, to automatically dissect ransomware samples. Starting from manual inspection of few samples, we define a set of rule in order to check whether the behaviours we find are representative of ransomware functionalities.

An exploratory experiment found that sorting arrays of random integers using Java 8's parallel sort required only 50%-70% of the time taken using the parallel sort of the Parallel Colt library. Factors considered responsible for the performance advantage include the use of a dual-pivot quicksort on locally held data at certain phases of execution and work-stealing by threads, a feature of the fork-join framework. The default performance of Parallel Colt's parallel sort was found to degrade dramatically for small array sizes due to unnecessary thread creation.

We created detailed profiles of the energy consumed by common operations done on Java List, Map, and Set abstractions. The results show that the alternative data types for these abstractions differ significantly in terms of energy consumption depending on the operations. For example, an ArrayList consumes less energy than a LinkedList if items are inserted at the middle or at the end, but consumes more energy than a LinkedList if items are inserted at the start of the list. To explain the results, we explored the memory usage and the bytecode executed during an operation. Expensive computation tasks in the analyzed bytecode traces appeared to have an energy impact, but memory usage did not contribute. We evaluated our profiles by using them to selectively replace Collections types used in six applications and libraries. We found that choosing the wrong Collections type, as indicated by our profiles, can cost even 300% more energy than the most efficient choice. Our work shows that the usage context of a data structure and our measured energy profiles can be used to decide between alternative Collections implementations.

Cross Site Scripting attack (XSS) is the computer security threat which allows the attacker to get access over the sensitive information, when the javaScript, VBScript, ActiveX, Flash or HTML which is embedded in the malicious XSS link gets executed. In this paper, we authors have discussed about various impacts of XSS, types of XSS, checked whether the site is vulnerable towards the XSS or not, discussed about various tools for examining the XSS vulnerability and summarizes the preventive measures against XSS.

Content Security Policy (CSP) is powerful client-side security layer that helps in mitigating and detecting wide ranges of Web attacks including cross-site scripting (XSS). However, utilizing CSP by site administrators is a fallible process and may require significant changes in web application code. In this paper, we propose an approach to help site administers to overcome these limitations in order to utilize the full benefits of CSP mechanism which leads to more immune sites from XSS. The algorithm is implemented as a plugin. It does not interfere with the Web application original code. The plugin can be “installed” on any other web application with minimum efforts. The algorithm can be implemented as part of Web Server layer, not as part of the business logic layer. It can be extended to support generating CSP for contents that are modified by JavaScript after loading. Current approach inspects the static contents of URLs.

Cross Site Scripting (XSS) and clickjacking have been ranked among the top web application threats in recent times. This paper introduces XBuster - our client-side defence against XSS, implemented as an extension to the Mozilla Firefox browser. XBuster splits each HTTP request parameter into HTML and JavaScript contexts and stores them separately. It searches for both contexts in the HTTP response and handles each context type differently. It defends against all XSS attack vectors including partial script injection, attribute injection and HTML injection. Also, existing XSS filters may inadvertently disable frame busting code used in web pages as a defence against clickjacking. However, XBuster has been designed to detect and neutralize such attempts.

Novice programmers exhibit a repertoire of affective states over time when they are learning computer programming. The modeling of frustration is important as it informs on the need for pedagogical intervention of the student who may otherwise lose confidence and interest in the learning. In this paper, contextual and keystroke features of the students within a Java tutoring system are used to detect frustration of student within a programming exercise session. As compared to psychological sensors used in other studies, the use of contextual and keystroke logs are less obtrusive and the equipment used (keyboard) is ubiquitous in most learning environment. The technique of logistic regression with lasso regularization is utilized for the modeling to prevent over-fitting. The results showed that a model that uses only contextual and keystroke features achieved a prediction accuracy level of 0.67 and a recall measure of 0.833. Thus, we conclude that it is possible to detect frustration of a student from distilling both the contextual and keystroke logs within the tutoring system with an adequate level of accuracy.

As mobile devices increasingly become bigger in terms of display and reliable in delivering paid entertainment and video content, we also see a rise in the presence of mobile applications that attempt to profit by streaming pirated content to unsuspected end-users. These applications are both paid and free and in the case of free applications, the source of funding appears to be advertisements that are displayed while the content is streamed to the device. In this paper, we assess the extent of content copyright infringement for mobile markets that span multiple platforms (iOS, Android, and Windows Mobile) and cover both official and unofficial mobile markets located across the world. Using a set of search keywords that point to titles of paid streaming content, we discovered 8,592 Android, 5,550 iOS, and 3,910 Windows mobile applications that matched our search criteria. Out of those applications, hundreds had links to either locally or remotely stored pirated content and were not developed, endorsed, or, in many cases, known to the owners of the copyrighted contents. We also revealed the network locations of 856,717 Uniform Resource Locators (URLs) pointing to back-end servers and cyber-lockers used to communicate the pirated content to the mobile application.

Increased use of Android devices and its open source development framework has attracted many digital crime groups to use Android devices as one of the key attack surfaces. Due to the extensive connectivity and multiple sources of network connections, Android devices are most suitable to botnet based malware attacks. The research focuses on developing a cloud-based Android botnet malware detection system. A prototype of the proposed system is deployed which provides a runtime Android malware analysis. The paper explains architectural implementation of the developed system using a botnet detection learning dataset and multi-layered algorithm used to predict botnet family of a particular application.