Biblio

The United States, including the Department of Defense, relies heavily on information systems and networking technologies to efficiently conduct a wide variety of missions across the globe. With the ever-increasing rate of cyber attacks, this dependency places the nation at risk of a loss of confidentiality, integrity, and availability of its critical information resources; degrading its ability to complete the mission. In this paper, we introduce the operational data classes for establishing situational awareness in cyberspace. A system effectively using our key information components will be able to provide the nation's leadership timely and accurate information to gain an understanding of the operational cyber environment to enable strategic, operational, and tactical decision-making. In doing so, we present, define and provide examples of our key classes of operational data for cyber situational awareness and present a hypothetical case study demonstrating how they must be consolidated to provide a clear and relevant picture to a commander. In addition, current organizational and technical challenges are discussed, and areas for future research are addressed.
 

Complexity is ever increasing within our information environment and organisations, as interdependent dynamic relationships within sociotechnical systems result in high variety and uncertainty from a lack of information or control. A net-centric approach is a strategy to improve information value, to enable stakeholders to extend their reach to additional data sources, share Situational Awareness (SA), synchronise effort and optimise resource use to deliver maximum (or proportionate) effect in support of goals. This paper takes a systems perspective to understand the dynamics within a net-centric information system. This paper presents the first stages of the Soft Systems Methodology (SSM), to develop a conceptual model of the human activity system and develop a system dynamics model to represent system behaviour, that will inform future research into a net-centric approach with information security. Our model supports the net-centric hypothesis that participation within a information sharing community extends information reach, improves organisation SA allowing proactive action to mitigate vulnerabilities and reduce overall risk within the community. The system dynamics model provides organisations with tools to better understand the value of a net-centric approach, a framework to determine their own maturity and evaluate strategic relationships with collaborative communities.
 

With the rise in the underground Internet economy, automated malicious programs popularly known as malwares have become a major threat to computers and information systems connected to the internet. Properties such as self healing, self hiding and ability to deceive the security devices make these software hard to detect and mitigate. Therefore, the detection and the mitigation of such malicious software is a major challenge for researchers and security personals. The conventional systems for the detection and mitigation of such threats are mostly signature based systems. Major drawback of such systems are their inability to detect malware samples for which there is no signature available in their signature database. Such malwares are known as zero day malware. Moreover, more and more malware writers uses obfuscation technology such as polymorphic and metamorphic, packing, encryption, to avoid being detected by antivirus. Therefore, the traditional signature based detection system is neither effective nor efficient for the detection of zero-day malware. Hence to improve the effectiveness and efficiency of malware detection system we are using classification method based on structural information and behavioral specifications. In this paper we have used both static and dynamic analysis approaches. In static analysis we are extracting the features of an executable file followed by classification. In dynamic analysis we are taking the traces of executable files using NtTrace within controlled atmosphere. Experimental results obtained from our algorithm indicate that our proposed algorithm is effective in extracting malicious behavior of executables. Further it can also be used to detect malware variants.

Resilience in the information sciences is notoriously difficult to define much less to measure. But in mechanical engineering, the resilience of a substance is mathematically well-defined as an area under the stress-strain curve. We combined inspiration from mechanics of materials and axioms from queuing theory in an attempt to define resilience precisely for information systems. We first examine the meaning of resilience in linguistic and engineering terms and then translate these definitions to information sciences. As a general assessment of our approach's fitness, we quantify how resilience may be measured in a simple queuing system. By using a very simple model we allow clear application of established theory while being flexible enough to apply to many other engineering contexts in information science and cyber security. We tested our definitions of resilience via simulation and analysis of networked queuing systems. We conclude with a discussion of the results and make recommendations for future work.
 

Since the massive deployment of Cyber-Physical Systems (CPSs) calls for long-range and reliable communication services with manageable cost, it has been believed to be an inevitable trend to relay a significant portion of CPS traffic through existing networking infrastructures such as the Internet. Adversaries who have access to networking infrastructures can therefore eavesdrop network traffic and then perform traffic analysis attacks in order to identify CPS sessions and subsequently launch various attacks. As we can hardly prevent all adversaries from accessing network infrastructures, thwarting traffic analysis attacks becomes indispensable. Traffic morphing serves as an effective means towards this direction. In this paper, a novel traffic morphing algorithm, CPSMorph, is proposed to protect CPS sessions. CPSMorph maintains a number of network sessions whose distributions of inter-packet delays are statistically indistinguishable from those of typical network sessions. A CPS message will be sent through one of these sessions with assured satisfaction of its time constraint. CPSMorph strives to minimize the overhead by dynamically adjusting the morphing process. It is characterized by low complexity as well as high adaptivity to changing dynamics of CPS sessions. Experimental results have shown that CPSMorph can effectively performing traffic morphing for real-time CPS messages with moderate overhead.
 

Due to deep automation, the configuration of many Cloud infrastructures is static and homogeneous, which, while easing administration, significantly decreases a potential attacker's uncertainty on a deployed Cloud-based service and hence increases the chance of the service being compromised. Moving-target defense (MTD) is a promising solution to the configuration staticity and homogeneity problem. This paper presents our findings on whether and to what extent MTD is effective in protecting a Cloud-based service with heterogeneous and dynamic attack surfaces - these attributes, which match the reality of current Cloud infrastructures, have not been investigated together in previous works on MTD in general network settings. We 1) formulate a Cloud-based service security model that incorporates Cloud-specific features such as VM migration/snapshotting and the diversity/compatibility of migration, 2) consider the accumulative effect of the attacker's intelligence on the target service's attack surface, 3) model the heterogeneity and dynamics of the service's attack surfaces, as defined by the (dynamic) probability of the service being compromised, as an S-shaped generalized logistic function, and 4) propose a probabilistic MTD service deployment strategy that exploits the dynamics and heterogeneity of attack surfaces for protecting the service against attackers. Through simulation, we identify the conditions and extent of the proposed MTD strategy's effectiveness in protecting Cloud-based services. Namely, 1) MTD is more effective when the service deployment is dense in the replacement pool and/or when the attack is strong, and 2) attack-surface heterogeneity-and-dynamics awareness helps in improving MTD's effectiveness.

Address shuffling is a type of moving target defense that prevents an attacker from reliably contacting a system by periodically remapping network addresses. Although limited testing has demonstrated it to be effective, little research has been conducted to examine the theoretical limits of address shuffling. As a result, it is difficult to understand how effective shuffling is and under what circumstances it is a viable moving target defense. This paper introduces probabilistic models that can provide insight into the performance of address shuffling. These models quantify the probability of attacker success in terms of network size, quantity of addresses scanned, quantity of vulnerable systems, and the frequency of shuffling. Theoretical analysis shows that shuffling is an acceptable defense if there is a small population of vulnerable systems within a large network address space, however shuffling has a cost for legitimate users. These results will also be shown empirically using simulation and actual traffic traces.