Practical Modbus flooding attack and detection

The Modicon Communication Bus (Modbus) protocol is one of the most commonly used protocols in industrial control systems. Modbus was not designed to provide security. This paper confirms that the Modbus protocol is vulnerable to flooding attacks. These attacks involve injection of commands that result in disrupting the normal operation of the control system. This paper describes a set of experiments that shows that an anomaly-based change detection algorithm and signature-based Snort threshold module are capable of detecting Modbus flooding attacks. In comparing these intrusion detection techniques, we find that the signature-based detection requires a carefully selected threshold value, and that the anomaly-based change detection algorithm may have a short delay before detecting the attacks depending on the parameters used. In addition, we also generate a network traffic dataset of flooding attacks on the Modbus control system protocol.

The power of hands-on exercises in SCADA cyber security education

For decades Supervisory Control and Data Acquisition (SCADA) and Industrial Control Systems (ICS) have used computers to monitor and control physical processes in many critical industries, including electricity generation, gas pipelines, water distribution, waste treatment, communications and transportation. Increasingly these systems are interconnected with corporate networks via the Internet, making them vulnerable and exposed to the same risks as those experiencing cyber-attacks on a conventional network. Very often SCADA networks services are viewed as a specialty subject, more relevant to engineers than standard IT personnel. Educators from two Australian universities have recognised these cultural issues and highlighted the gap between specialists with SCADA systems engineering skills and the specialists in network security with IT background. This paper describes a learning approach designed to help students to bridge this gap, gain theoretical knowledge of SCADA systems' vulnerabilities to cyber-attacks via experiential learning and acquire practical skills through actively participating in hands-on exercises.

Pattern-based topic models for information filtering

Topic modelling, such as Latent Dirichlet Allocation (LDA), was proposed to generate statistical models to represent multiple topics in a collection of documents, which has been widely utilized in the fields of machine learning and information retrieval, etc. But its effectiveness in information filtering is rarely known. Patterns are always thought to be more representative than single terms for representing documents. In this paper, a novel information filtering model, Pattern-based Topic Model(PBTM) , is proposed to represent the text documents not only using the topic distributions at general level but also using semantic pattern representations at detailed specific level, both of which contribute to the accurate document representation and document relevance ranking. Extensive experiments are conducted to evaluate the effectiveness of PBTM by using the TREC data collection Reuters Corpus Volume 1. The results show that the proposed model achieves outstanding performance.

Are we there yet? A step closer to theorizing information systems success

The need for native Information Systems (IS) theories has been discussed by several prominent scholars. Contributing to their conjectural discussion, this research moves towards theorizing IS success as a native theory for the discipline. Despite being one of the most cited scholarly works to-date, IS success of DeLone and McLean (1992) has been criticized by some for lacking focus on the theoretical approach. Following theory development frameworks, this study improves the theoretical standing of IS success by minimizing interaction and inconsistency. The empirical investigation of theorizing IS success includes 1396 respondents, gathered through six surveys and a case study. The respondents represent 70 organisations, multiple Information Systems, and both private and public sector organizations.

Information systems development outsourcing : the role of control configurations

The Control Theory has provided a useful theoretical foundation for Information Systems development outsourcing (ISD-outsourcing) to examine the co-ordination between the client and the vendor. Recent research identified two control mechanisms: structural (structure of the control mode) and process (the process through which the control mode is enacted). Yet, the Control Theory research to-date does not describe the ways in which the two control mechanisms can be combined to ensure project success. Grounded in case study data of eight ISD-outsourcing projects, we derive three ‘control configurations’; i) aligned, ii) negotiated, and 3) self-managed, which describe the combinative patterns of structural and process control mechanisms within and across control modes.

Consumer created reviews and ratings : the importance of word of mouth in information search

This study explored the creation, dissemination and exchange of electronic word of mouth, in the form of product reviews and ratings of digital technology products. Based on 43 in-depth interviews and 500 responses to an online survey, it reveals a new communication model describing consumers' info-active and info-passive information search styles. The study delivers an in-depth understanding of consumers' attitudes towards current advertising tools and user-generated content, and points to new marketing techniques emerging in the online environment.

Acquiring user information needs for recommender systems

Most recommender systems attempt to use collaborative filtering, content-based filtering or hybrid approach to recommend items to new users. Collaborative filtering recommends items to new users based on their similar neighbours, and content-based filtering approach tries to recommend items that are similar to new users' profiles. The fundamental issues include how to profile new users, and how to deal with the over-specialization in content-based recommender systems. Indeed, the terms used to describe items can be formed as a concept hierarchy. Therefore, we aim to describe user profiles or information needs by using concepts vectors. This paper presents a new method to acquire user information needs, which allows new users to describe their preferences on a concept hierarchy rather than rating items. It also develops a new ranking function to recommend items to new users based on their information needs. The proposed approach is evaluated on Amazon book datasets. The experimental results demonstrate that the proposed approach can largely improve the effectiveness of recommender systems.

Slicing big data - Twitter, gambling and time sensitive information

Big Data presents many challenges related to volume, whether one is interested in studying past datasets or, even more problematically, attempting to work with live streams of data. The most obvious challenge, in a ‘noisy’ environment such as contemporary social media, is to collect the pertinent information; be that information for a specific study, tweets which can inform emergency services or other responders to an ongoing crisis, or give an advantage to those involved in prediction markets. Often, such a process is iterative, with keywords and hashtags changing with the passage of time, and both collection and analytic methodologies need to be continually adapted to respond to this changing information. While many of the data sets collected and analyzed are preformed, that is they are built around a particular keyword, hashtag, or set of authors, they still contain a large volume of information, much of which is unnecessary for the current purpose and/or potentially useful for future projects. Accordingly, this panel considers methods for separating and combining data to optimize big data research and report findings to stakeholders. The first paper considers possible coding mechanisms for incoming tweets during a crisis, taking a large stream of incoming tweets and selecting which of those need to be immediately placed in front of responders, for manual filtering and possible action. The paper suggests two solutions for this, content analysis and user profiling. In the former case, aspects of the tweet are assigned a score to assess its likely relationship to the topic at hand, and the urgency of the information, whilst the latter attempts to identify those users who are either serving as amplifiers of information or are known as an authoritative source. Through these techniques, the information contained in a large dataset could be filtered down to match the expected capacity of emergency responders, and knowledge as to the core keywords or hashtags relating to the current event is constantly refined for future data collection. The second paper is also concerned with identifying significant tweets, but in this case tweets relevant to particular prediction market; tennis betting. As increasing numbers of professional sports men and women create Twitter accounts to communicate with their fans, information is being shared regarding injuries, form and emotions which have the potential to impact on future results. As has already been demonstrated with leading US sports, such information is extremely valuable. Tennis, as with American Football (NFL) and Baseball (MLB) has paid subscription services which manually filter incoming news sources, including tweets, for information valuable to gamblers, gambling operators, and fantasy sports players. However, whilst such services are still niche operations, much of the value of information is lost by the time it reaches one of these services. The paper thus considers how information could be filtered from twitter user lists and hash tag or keyword monitoring, assessing the value of the source, information, and the prediction markets to which it may relate. The third paper examines methods for collecting Twitter data and following changes in an ongoing, dynamic social movement, such as the Occupy Wall Street movement. It involves the development of technical infrastructure to collect and make the tweets available for exploration and analysis. A strategy to respond to changes in the social movement is also required or the resulting tweets will only reflect the discussions and strategies the movement used at the time the keyword list is created — in a way, keyword creation is part strategy and part art. In this paper we describe strategies for the creation of a social media archive, specifically tweets related to the Occupy Wall Street movement, and methods for continuing to adapt data collection strategies as the movement’s presence in Twitter changes over time. We also discuss the opportunities and methods to extract data smaller slices of data from an archive of social media data to support a multitude of research projects in multiple fields of study. The common theme amongst these papers is that of constructing a data set, filtering it for a specific purpose, and then using the resulting information to aid in future data collection. The intention is that through the papers presented, and subsequent discussion, the panel will inform the wider research community not only on the objectives and limitations of data collection, live analytics, and filtering, but also on current and in-development methodologies that could be adopted by those working with such datasets, and how such approaches could be customized depending on the project stakeholders.

Perceptions of climate change and trust in information providers in rural Australia

Disagreement within the global science community about the certainty and causes of climate change has led the general public to question what to believe and who to trust on matters related to this issue. This paper reports on qualitative research undertaken with Australian residents from two rural areas to explore their perceptions of climate change and trust in information providers. While overall, residents tended to agree that climate change is a reality, perceptions varied in terms of its causes and how best to address it. Politicians, government, and the media were described as untrustworthy sources of information about climate change, with independent scientists being the most trusted. The vested interests of information providers appeared to be a key reason for their distrust. The findings highlight the importance of improved transparency and consultation with the public when communicating information about climate change and related policies.

Strategies and solutions for housing sustainability : building information files and performance certificates

The overall aim of this research project was to provide a broader range of value propositions (beyond upfront traditional construction costs) that could transform both the demand side and supply side of the housing industry. The project involved gathering information about how building information is created, used and communicated and classifying building information, leading to the formation of an Information Flow Chart and Stakeholder Relationship Map. These were then tested via broad housing industry focus groups and surveys. The project revealed four key relationships that appear to operate in isolation to the whole housing sector and may have significant impact on the sustainability outcomes and life cycle costs of dwellings over their life cycle. It also found that although a lot of information about individual dwellings does already exist, this information is not coordinated or inventoried in any systematic manner and that national building information files of building passports would present value to a wide range of stakeholders.

Social engineering in social networking sites : phase-based and source-based models

Social networking sites (SNSs), with their large numbers of users and large information base, seem to be perfect breeding grounds for exploiting the vulnerabilities of people, the weakest link in security. Deceiving, persuading, or influencing people to provide information or to perform an action that will benefit the attacker is known as “social engineering.” While technology-based security has been addressed by research and may be well understood, social engineering is more challenging to understand and manage, especially in new environments such as SNSs, owing to some factors of SNSs that reduce the ability of users to detect the attack and increase the ability of attackers to launch it. This work will contribute to the knowledge of social engineering by presenting the first two conceptual models of social engineering attacks in SNSs. Phase-based and source-based models are presented, along with an intensive and comprehensive overview of different aspects of social engineering threats in SNSs.

Social engineering in social networking sites : affect-based model

While social engineering represents a real and ominous threat to many organizations, companies, governments, and individuals, social networking sites (SNSs), have been identified as among the most common means of social engineering attacks. Owing to factors that reduce the ability of users to detect social engineering tricks and increase the ability of attackers to launch them, SNSs seem to be perfect breeding ground for exploiting the vulnerabilities of people, and the weakest link in security. This work will contribute to the knowledge of social engineering by identifying different entities and subentities that affect social engineering based attacks in SNSs. Moreover, this paper includes an intensive and comprehensive overview of different aspects of social engineering threats in SNSs.

Toward understanding social engineering

There is no doubt that social engineering plays a vital role in compromising most security defenses, and in attacks on people, organizations, companies, or even governments. It is the art of deceiving and tricking people to reveal critical information or to perform an action that benefits the attacker in some way. Fraudulent and deceptive people have been using social engineering traps and tactics using information technology such as e-mails, social networks, web sites, and applications to trick victims into obeying them, accepting threats, and falling victim to various crimes and attacks such as phishing, sexual abuse, financial abuse, identity theft, impersonation, physical crime, and many other forms of attack. Although organizations, researchers, practitioners, and lawyers recognize the severe risk of social engineering-based threats, there is a severe lack of understanding and controlling of such threats. One side of the problem is perhaps the unclear concept of social engineering as well as the complexity of understand human behaviors in behaving toward, approaching, accepting, and failing to recognize threats or the deception behind them. The aim of this paper is to explain the definition of social engineering based on the related theories of the many related disciplines such as psychology, sociology, information technology, marketing, and behaviourism. We hope, by this work, to help researchers, practitioners, lawyers, and other decision makers to get a fuller picture of social engineering and, therefore, to open new directions of collaboration toward detecting and controlling it.

Pattern-based topics for document modelling in information filtering

Many mature term-based or pattern-based approaches have been used in the field of information filtering to generate users’ information needs from a collection of documents. A fundamental assumption for these approaches is that the documents in the collection are all about one topic. However, in reality users’ interests can be diverse and the documents in the collection often involve multiple topics. Topic modelling, such as Latent Dirichlet Allocation (LDA), was proposed to generate statistical models to represent multiple topics in a collection of documents, and this has been widely utilized in the fields of machine learning and information retrieval, etc. But its effectiveness in information filtering has not been so well explored. Patterns are always thought to be more discriminative than single terms for describing documents. However, the enormous amount of discovered patterns hinder them from being effectively and efficiently used in real applications, therefore, selection of the most discriminative and representative patterns from the huge amount of discovered patterns becomes crucial. To deal with the above mentioned limitations and problems, in this paper, a novel information filtering model, Maximum matched Pattern-based Topic Model (MPBTM), is proposed. The main distinctive features of the proposed model include: (1) user information needs are generated in terms of multiple topics; (2) each topic is represented by patterns; (3) patterns are generated from topic models and are organized in terms of their statistical and taxonomic features, and; (4) the most discriminative and representative patterns, called Maximum Matched Patterns, are proposed to estimate the document relevance to the user’s information needs in order to filter out irrelevant documents. Extensive experiments are conducted to evaluate the effectiveness of the proposed model by using the TREC data collection Reuters Corpus Volume 1. The results show that the proposed model significantly outperforms both state-of-the-art term-based models and pattern-based models

Did you see that? Dissociating advanced visual information and ball flight constrains perception and action processes during one-handed catching

The integration of separate, yet complimentary, cortical pathways appears to play a role in visual perception and action when intercepting objects. The ventral system is responsible for object recognition and identification, while the dorsal system facilitates continuous regulation of action. This dual-system model implies that empirically manipulating different visual information sources during performance of an interceptive action might lead to the emergence of distinct gaze and movement pattern profiles. To test this idea, we recorded hand kinematics and eye movements of participants as they attempted to catch balls projected from a novel apparatus that synchronised or de-synchronised accompanying video images of a throwing action and ball trajectory. Results revealed that ball catching performance was less successful when patterns of hand movements and gaze behaviours were constrained by the absence of advanced perceptual information from the thrower's actions. Under these task constraints, participants began tracking the ball later, followed less of its trajectory, and adapted their actions by initiating movements later and moving the hand faster. There were no performance differences when the throwing action image and ball speed were synchronised or de-synchronised since hand movements were closely linked to information from ball trajectory. Results are interpreted relative to the two-visual system hypothesis, demonstrating that accurate interception requires integration of advanced visual information from kinematics of the throwing action and from ball flight trajectory.