Software similarity and classification

Software similarity and classification is an emerging topic with wide applications. It is applicable to the areas of malware detection, software theft detection, plagiarism detection, and software clone detection. Extracting program features, processing those features into suitable representations, and constructing distance metrics to define similarity and dissimilarity are the key methods to identify software variants, clones, derivatives, and classes of software. Software Similarity and Classification reviews the literature of those core concepts, in addition to relevant literature in each application and demonstrates that considering these applied problems as a similarity and classification problem enables techniques to be shared between areas. Additionally, the authors present in-depth case studies using the software similarity and classification techniques developed throughout the book.

New developments in network forensics - tools and techniques

Network forensics is a branch of digital forensics which has evolved recently as a very important discipline used in monitoring and analysing network traffic-particularly for the purposes of tracing intrusions and attacks. This paper presents an analysis of the tools and techniques used in network forensic analysis. It further examines the application of network forensics to vital areas such as malware and network attack detection; IP traceback and honeypots; and intrusion detection. Further, the paper addresses new and emerging areas of network forensic development which include critical infrastructure forensics, wireless network forensics, as well as its application to social networking. © 2012 IEEE.

Enhancing RFID performance and security in networked environments

In this thesis we propose and present a number of methods by which the performance and security of networked RFID systems can be improved. These include a networked P2P RFID architecture, a comprehensive RFID security framework, a RFID security protocol and an RFID malware detection and Prevention technique.

Contrasting permission patterns between clean and malicious android applications

The Android platform uses a permission system model to allow users and developers to regulate access to private information and system resources required by applications. Permissions have been proved to be useful for inferring behaviors and characteristics of an application. In this paper, a novel method to extract contrasting permission patterns for clean and malicious applications is proposed. Contrary to existing work, both required and used permissions were considered when discovering the patterns. We evaluated our methodology on a clean and a malware dataset, each comprising of 1227 applications. Our empirical results suggest that our permission patterns can capture key differences between clean and malicious applications, which can assist in characterizing these two types of applications.

Malicious code detection architecture inspired by human immune system

Malicious code is a threat to computer systems globally. In this paper, we outline the evolution of malicious code attacks. The threat is evolving, leaving challenges for attackers to improve attack techniques and for researchers and security specialists to improve detection accuracy. We present a novel architecture for an effective defense against malicious code attack, inspired by the human immune system. We introduce two phases of program execution: Adolescent and Mature Phase. The first phase uses a malware profile matching mechanism, whereas the second phase uses a program profile matching mechanism. Both mechanisms are analogous to the innate immune system

An evaluation of API calls hooking performance

An open research question in malware detection is how to accurately and reliably distinguish a malware program from a benign one, running on the same machine. In contrast to code signatures, which are commonly used in commercial protection software, signatures derived from system calls have the potential to form the basis of a much more flexible defense mechanism. However, the performance degradation caused by monitoring systems calls could adversely impact the machine. In this paper we report our experimental experience in implementing API hooking to capture sequences of API calls. The loading time often common programs was benchmarked with three different settings: plain, computer with antivirus and computer with API hook. Results suggest that the performance of this technique is sufficient to provide a viable approach to distinguishing between benign and malware code execution

Security testing in Android networks : a practical case study

Android is a new generation of an open operating system directed at mobile devices that are carried every day. The openness of this architecture is leading to new applications and opportunities including a host of multimedia services, new interfaces and browsers, multitasking including support for wireless local, personal and wide area networking services. Security with mobility and wireless connectivity thus becomes even more important with all these exciting developments. Vital security issues such as leakage of private information, file stealing and spambots abound in networks in practice and Android networks continue to be subject to these same families of vulnerabilities. This paper provides a demonstration of such vulnerabilities in spite of the best efforts of designers and implementers. In particular it describes examples of data leakage and file stealing (address books, contact lists, SMS messages, pictures) as well as demonstrating how Android devices can create spambots. © 2013 IEEE.

Scalable RFID security framework and protocol supporting Internet of Things

Radio-frequency identification (RFID) is seen as one of the requirements for the implementation of the Internet-of-Things (IoT). However, an RFID system has to be equipped with a holistic security framework for a secure and scalable operation. Although much work has been done to provide privacy and anonymity, little focus has been given to performance, scalability and customizability issues to support robust implementation of IoT. Also, existing protocols suffer from a number of deficiencies such as insecure or inefficient identification techniques, throughput delay and inadaptability. In this paper, we propose a novel identification technique based on a hybrid approach (group-based approach and collaborative approach) and security check handoff (SCH) for RFID systems with mobility. The proposed protocol provides customizability and adaptability as well as ensuring the secure and scalable deployment of an RFID system to support a robust distributed structure such as the IoT. The protocol has an extra fold of protection against malware using an incorporated malware detection technique. We evaluated the protocol using a randomness battery test and the results show that the protocol offers better security, scalability and customizability than the existing protocols. © 2014 Elsevier B.V. All rights reserved.

Mining permission patterns for contrasting clean and malicious android applications

An Android application uses a permission system to regulate the access to system resources and users' privacy-relevant information. Existing works have demonstrated several techniques to study the required permissions declared by the developers, but little attention has been paid towards used permissions. Besides, no specific permission combination is identified to be effective for malware detection. To fill these gaps, we have proposed a novel pattern mining algorithm to identify a set of contrast permission patterns that aim to detect the difference between clean and malicious applications. A benchmark malware dataset and a dataset of 1227 clean applications has been collected by us to evaluate the performance of the proposed algorithm. Valuable findings are obtained by analyzing the returned contrast permission patterns. © 2013 Elsevier B.V. All rights reserved.

Propagation model of smartphone worms based on semi-Markov process and social relationship graph

Smartphone applications are getting more and more popular and pervasive in our daily life, and are also attractive to malware writers due to their limited computing source and vulnerabilities. At the same time, we possess limited understanding of our opponents in cyberspace. In this paper, we investigate the propagation model of SMS/MMS-based worms through integrating semi-Markov process and social relationship graph. In our modeling, we use semi-Markov process to characterize state transition among mobile nodes, and hire social network theory, a missing element in many previous works, to enhance the proposed mobile malware propagation model. In order to evaluate the proposed models, we have developed a specific software, and collected a large scale real-world data for this purpose. The extensive experiments indicate that the proposed models and algorithms are effective and practical. © 2014 Elsevier Ltd. All rights reserved.

Containing smartphone worm propagation with an influence maximization algorithm

In recent years, wide attention has been drawn to the problem of containing worm propagation in smartphones. Unlike existing containment models for worm propagation, we study how to prevent worm propagation through the immunization of key nodes (e.g.; the top k influential nodes). Thus, we propose a novel containment model based on an influence maximization algorithm. In this model, we introduce a social relation graph to evaluate the influence of nodes and an election mechanism to find the most influential nodes. Finally, this model provides a targeted immunization strategy to disable worm propagation by immunizing the top k influential nodes. The experimental results show that the model not only finds the most influential top k nodes quickly, but also effectively restrains and controls worm propagation.

Are the popular users always important for information dissemination in online social networks?

This article verifies the importance of popular users in OSNs. The results are counter-intuitive. First, for dissemination speed, a large amount of users can swiftly distribute information to the masses, but they are not highly-connected users. Second, for dissemination scale, many powerful forwarders in OSNs cannot be identified by the degree measure. Furthermore, to control dissemination, popular users cannot capture most bridges of social communities.

Spam Trapping System: Novel security framework to fight against spam botnets

In this paper, we inspire from two analogies: the warfare kill zone and the airport check-in system, to tackle the issue of spam botnet detection. We add a new line of defense to the defense-in-depth model called the third line. This line is represented by a security framework, named the Spam Trapping System (STS) and adopts the prevent-then-detect approach to fight against spam botnets. The framework exploits the application sandboxing principle to prevent the spam from going out of the host and detect the corresponding malware bot. We show that the proposed framework can ensure better security against malware bots. In addition, an analytical study demonstrates that the framework offers optimal performance in terms of detection time and computational cost in comparison to intrusion detection systems based on static and dynamic analysis. © 2014 IEEE.

Security and privacy of users' personal Information on smartphones

 This research investigated the proliferation of malicious applications on smartphones and a framework that can efficiently detect and classify such applications based on behavioural patterns was proposed. Additionally the causes and impact of unauthorised disclosure of personal information by clean applications were examined and countermeasures to protect smartphone users’ privacy were proposed.

Context-sensitive analysis without calling-context

Since Sharir and Pnueli, algorithms for context-sensitivity have been defined in terms of 'valid' paths in an interprocedural flow graph. The definition of valid paths requires atomic call and ret statements, and encapsulated procedures. Thus, the resulting algorithms are not directly applicable when behavior similar to call and ret instructions may be realized using non-atomic statements, or when procedures do not have rigid boundaries, such as with programs in low level languages like assembly or RTL. We present a framework for context-sensitive analysis that requires neither atomic call and ret instructions, nor encapsulated procedures. The framework presented decouples the transfer of control semantics and the context manipulation semantics of statements. A new definition of context-sensitivity, called stack contexts, is developed. A stack context, which is defined using trace semantics, is more general than Sharir and Pnueli's interprocedural path based calling-context. An abstract interpretation based framework is developed to reason about stack-contexts and to derive analogues of calling-context based algorithms using stack-context. The framework presented is suitable for deriving algorithms for analyzing binary programs, such as malware, that employ obfuscations with the deliberate intent of defeating automated analysis. The framework is used to create a context-sensitive version of Venable et al.'s algorithm for analyzing x86 binaries without requiring that a binary conforms to a standard compilation model for maintaining procedures, calls, and returns. Experimental results show that a context-sensitive analysis using stack-context performs just as well for programs where the use of Sharir and Pnueli's calling-context produces correct approximations. However, if those programs are transformed to use call obfuscations, a contextsensitive analysis using stack-context still provides the same, correct results and without any additional overhead. © Springer Science+Business Media, LLC 2011.