Intrusion detection and prevention of web service attacks for software as a service:Fuzzy association rules vs fuzzy associative patterns

Cloud computing inherits all the systems, networks as well asWeb Services’ security vulnerabilities, in particular for software as a service (SaaS), where business applications or services are provided over the Cloud as Web Service (WS). Hence, WS-based applications must be protected against loss of integrity, confidentiality and availability when they are deployed over to the Cloud environment. Many existing IDP systems address only attacks mostly occurring at PaaS and IaaS. In this paper, we present our fuzzy association rule-based (FAR) and fuzzy associative pattern-based (FAP) intrusion detection and prevention (IDP) systems in defending against WS attacks at the SaaS level. Our experimental results have validated the capabilities of these two IDP systems in terms of detection of known attacks and prediction of newvariant attacks with accuracy close to 100%. For each transaction transacted over the Cloud platform, detection, prevention or prediction is carried out in less than five seconds. For load and volume testing on the SaaS where the system is under stress (at a work load of 5000 concurrent users submitting normal, suspicious and malicious transactions over a time interval of 300 seconds), the FAR IDP system provides close to 95% service availability to normal transactions. Future work involves determining more quality attributes besides service availability, such as latency, throughput and accountability for a more trustworthy SaaS

Privacy Violation and Detection Using Pattern Mining Techniques

Privacy, its violations and techniques to bypass privacy violation have grabbed the centre-stage of both academia and industry in recent months. Corporations worldwide have become conscious of the implications of privacy violation and its impact on them and to other stakeholders. Moreover, nations across the world are coming out with privacy protecting legislations to prevent data privacy violations. Such legislations however expose organizations to the issues of intentional or unintentional violation of privacy data. A violation by either malicious external hackers or by internal employees can expose the organizations to costly litigations. In this paper, we propose PRIVDAM; a data mining based intelligent architecture of a Privacy Violation Detection and Monitoring system whose purpose is to detect possible privacy violations and to prevent them in the future. Experimental evaluations show that our approach is scalable and robust and that it can detect privacy violations or chances of violations quite accurately. Please contact the author for full text at [email protected]

A Text Mining-Based Anomaly aZDetection Model in Network Security

Anomaly detection systems are extensively used security tools to detect cyber-threats and attack activities in computer systems and networks. In this paper, we present Text Mining-Based Anomaly Detection (TMAD) model. We discuss n-gram text categorization and focus our attention on a main contribution of method TF-IDF (Term frequency, inverse document frequency), which enhance the performance commonly term weighting schemes are used, where the weights reflect the importance of a word in a specific document of the considered collection. Mahalanobis Distances Map (MDM) and Support Vector Machine (SVM) are used to discover hidden correlations between the features and among the packet payloads. Experiments have been accomplished to estimate the performance of TMAD against ISCX dataset 2012 intrusion detection evaluation dataset. The results show TMAD has good accuracy

O-ADPI: Online Adaptive Deep-Packet Inspector Using Mahalanobis Distance Map for Web Service Attacks Classification

Most active research in Host and Network Intrusion Detection Systems are only able to detect attacks of the computer systems and attacks at the network layer, which are not sufficient to counteract SOAP/REST or XML/JSON-related attacks. In dealing with the problem of anomaly detection in web service message datasets, this paper roposes an anomaly detection system called the Online Adaptive DeepPacket Inspector (O-ADPI) for web service message attacks classification. The proposed approach relies on multiple statistical methods which use Unigram-based Weighting Scheme (UWS) that combines text mining techniques with a set of different statistical criteria for Feature Selection Engine (FSE) to effectively and efficiently explore optimal subspaces in detecting anomalies embedded deep in the high dimensional feature subspaces. We utilize a supervised intrusion detection algorithm based on mahalanobis distance map classifier. As web service attacks can be classified into anomaly and normal, the task of anomaly detection can be modeled as a classification problem. The O-ADPI model was assessed for F-value, true positive rate (TPR), and false positive rate (FPR) in order to evaluate the detectionx performance of OADPI against different type of feature selections engines with corresponding PCs for each service messagespecific. The experiments were performed using the REST-IDS Dataset 2015 and the results demonstrated that the proposed O-ADPI model achieved the best results in each message-specific service

Security analyses for detecting deserialisation vulnerabilities : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Palmerston North, New Zealand

An important task in software security is to identify potential vulnerabilities. Attackers exploit security vulnerabilities in systems to obtain confidential information, to breach system integrity, and to make systems unavailable to legitimate users. In recent years, particularly 2012, there has been a rise in reported Java vulnerabilities. One type of vulnerability involves (de)serialisation, a commonly used feature to store objects or data structures to an external format and restore them. In 2015, a deserialisation vulnerability was reported involving Apache Commons Collections, a popular Java library, which affected numerous Java applications. Another major deserialisation-related vulnerability that affected 55\% of Android devices was reported in 2015. Both of these vulnerabilities allowed arbitrary code execution on vulnerable systems by malicious users, a serious risk, and this came as a call for the Java community to issue patches to fix serialisation related vulnerabilities in both the Java Development Kit and libraries. Despite attention to coding guidelines and defensive strategies, deserialisation remains a risky feature and a potential weakness in object-oriented applications. In fact, deserialisation related vulnerabilities (both denial-of-service and remote code execution) continue to be reported for Java applications. Further, deserialisation is a case of parsing where external data is parsed from their external representation to a program's internal data structures and hence, potentially similar vulnerabilities can be present in parsers for file formats and serialisation languages. The problem is, given a software package, to detect either injection or denial-of-service vulnerabilities and propose strategies to prevent attacks that exploit them. The research reported in this thesis casts detecting deserialisation related vulnerabilities as a program analysis task. The goal is to automatically discover this class of vulnerabilities using program analysis techniques, and to experimentally evaluate the efficiency and effectiveness of the proposed methods on real-world software. We use multiple techniques to detect reachability to sensitive methods and taint analysis to detect if untrusted user-input can result in security violations. Challenges in using program analysis for detecting deserialisation vulnerabilities include addressing soundness issues in analysing dynamic features in Java (e.g., native code). Another hurdle is that available techniques mostly target the analysis of applications rather than library code. In this thesis, we develop techniques to address soundness issues related to analysing Java code that uses serialisation, and we adapt dynamic techniques such as fuzzing to address precision issues in the results of our analysis. We also use the results from our analysis to study libraries in other languages, and check if they are vulnerable to deserialisation-type attacks. We then provide a discussion on mitigation measures for engineers to protect their software against such vulnerabilities. In our experiments, we show that we can find unreported vulnerabilities in Java code; and how these vulnerabilities are also present in widely-used serialisers for popular languages such as JavaScript, PHP and Rust. In our study, we discovered previously unknown denial-of-service security bugs in applications/libraries that parse external data formats such as YAML, PDF and SVG

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation

Cyber Security and Critical Infrastructures

This book contains the manuscripts that were accepted for publication in the MDPI Special Topic "Cyber Security and Critical Infrastructure" after a rigorous peer-review process. Authors from academia, government and industry contributed their innovative solutions, consistent with the interdisciplinary nature of cybersecurity. The book contains 16 articles: an editorial explaining current challenges, innovative solutions, real-world experiences including critical infrastructure, 15 original papers that present state-of-the-art innovative solutions to attacks on critical systems, and a review of cloud, edge computing, and fog's security and privacy issues

Security Enhanced Applications for Information Systems

Every day, more users access services and electronically transmit information which is usually disseminated over insecure networks and processed by websites and databases, which lack proper security protection mechanisms and tools. This may have an impact on both the users’ trust as well as the reputation of the system’s stakeholders. Designing and implementing security enhanced systems is of vital importance. Therefore, this book aims to present a number of innovative security enhanced applications. It is titled “Security Enhanced Applications for Information Systems” and includes 11 chapters. This book is a quality guide for teaching purposes as well as for young researchers since it presents leading innovative contributions on security enhanced applications on various Information Systems. It involves cases based on the standalone, network and Cloud environments