Biblio

In this work, we develop an underwater relay network model for an unmanned cruise system. By introducing the underwater cruise, we analyze end-to-end outage performance for collecting data from a sensor node. Based on theoretical derivation of the outage probability, we further analyze the optimized location and data rate for relaying.

In recent years, the rapid development of virtualization and container technology brings unprecedented impact on traditional IT architecture. Trusted Computing devotes to provide a solution to protect the integrity of the target platform and introduces a virtual TPM to adapt to the challenges that virtualization brings. However, the traditional integrity measurement solution and remote attestation has limitations due to the challenges such as large of measurement and attestation cost and overexposure of configurations details. In this paper, we propose the Partial Attestation Model. The basic idea of Partial Attestation Model is to reconstruct the Chain of Trust by dividing them into several separated ones. Our model therefore enables the challenger to attest the specified security requirements of the target platform, instead of acquiring and verifying the complete detailed configurations. By ignoring components not related to the target requirements, our model reduces the attestation costs. In addition, we further implement an attestation protocol to prevent overexposure of the target platform's configuration details. We build a use case to illustrate the implementation of our model, and the evaluations on our prototype show that our model achieves better efficiency than the existing remote attestation scheme.

Commercial trusted third parties (TTPs) may increase their bottom line by watering down their validation procedures because they assume no liability for lapses of judgement. Consumers bear the risk of misplaced trust. Reputation loss is a weak deterrent for TTPs because consumers do not choose them - web shops and browser vendors do. At the same time, consumers are the source of income of these parties. Hence, risks and rewards are not well-aligned. Towards a better alignment, we explore the brokering of connection insurances and transaction insurances, where consumers get to choose their insurer. We lay out the principal idea how such a brokerage might work at a technical level with minimal interference with existing protocols and mechanisms, we analyze the security requirements and we propose techniques to meet these requirements.

In this work, a bi-directional transceiver with a maximum throughput of 24 kbps is presented. The spatio-temporal shallow water channel characteristics between a projector and a hydrophone array are analyzed in a seawater tank, and a methodology to maintain a 10−4 probability of bit error with prior knowledge of the channel statistics is proposed. Also, it is found that flow generated in the sea water provides a realistic representation of time-varying propagation conditions, particularly for the reverse link communication link at 22.5 kHz.

In this paper, we present the notion of recipient-revocable identity-based broadcast encryption scheme. In this notion, a content provider will produce encrypted content and send them to a third party (which is a broadcaster). This third party will be able to revoke some identities from the ciphertext. We present a security model to capture these requirements, as well as a concrete construction. The ciphertext consists of k+3 group elements, assuming that the maximum number of revocation identities is k. That is, the ciphertext size is linear in the maximal size of R, where R is the revocation identity set. However, we say that the additional elements compared to that from an IBBE scheme are only for the revocation but not for decryption. Therefore, the ciphertext sent to the users for decryption will be of constant size (i.e.,3 group elements). Finally, we present the proof of security of our construction.

With the emerging Science and Technology, network security has become a major concern. Researchers have proposed new theories and applications to eradicate the unethical access to the secret message. This paper presents a new algorithm on Symmetric Key Cryptography. The algorithm comprises of a bitwise shifting operation, folding logic along with simple mathematical operations. The fundamental security of the algorithm lies in the dual-layered encryption and decryption processes which divide the entire method into various phases. The algorithm implements a ciphered array key which itself hides the actual secret key to increase the integrity of the cryptosystem. The algorithm has been experimentally tested and the test results are promising.

As Industries and Data Center plan to implement Software Defined Networking (SDN), the main concern is the anxiety about security. The Industries and Data Centers are curious to know how a SDN product will support them that their data, supporting applications and built in infrastructure are not vulnerable to threats. The initiation of SDN, will demand new pathways for securing control plane traffic. The traditional networks usually trust switching intelligence to implement various defense mechanisms besides known attacks. Many attacks which distress traditional networks also affect SDNs, partially due to SDN architecture complexities and most prominent among them is DoS. This paper identifies the pathways of threats to SDN systems and discuss methods to ways to mitigate them.

We measure the prevalence and uses of TLS proxies using a Flash tool deployed with a Google AdWords campaign. We generate 2.9 million certificate tests and find that 1 in 250 TLS connections are TLS-proxied. The majority of these proxies appear to be benevolent, however we identify over 1,000 cases where three malware products are using this technology nefariously. We also find numerous instances of negligent, duplicitous, and suspicious behavior, some of which degrade security for users without their knowledge. Distinguishing these types of practices is challenging in practice, indicating a need for transparency and user awareness.

Active Cyber Defense (ACD) reconfigures cyber systems (networks and hosts) in timely manner in order to automatically respond to cyber incidents and mitigate potential risks or attacks. However, to launch a successful cyber defense, ACD strategies need to be proven effective in neutralizing the threats and enforceable under the current state and capabilities of the network. In this paper, we present a bounded model checking framework based on SMT to verify that the network can support the given ACD strategies accurately and safely without jeopardizing cyber mission invariants. We abstract the ACD strategies as sets of serializable reconfigurations and provide user interfaces to define cyber mission invariants as reachability, security, and QoS properties. We then verify the satisfaction of these invariants under the given strategies. We implemented this system on OpenFlow-based Software Defined Networks and we evaluated the time complexity for verifying ACD strategies on OpenFlow networks of over two thousand nodes and thousands of rules.

The Earth is a water planet. The ocean is used for nature resource exploitation, fishery, etc., and it also plays critical roles in global climate regulation and transportation. Consequently, it is extremely important to keep track of its condition. And thus ocean observation systems have received increasing attentions.

The threat from insiders is an ever-growing concern for organisations, and in recent years the harm that insiders pose has been widely demonstrated. This paper describes our recent work into how we might support insider threat detection when actions are taken which can be immediately determined as of concern because they fall into one of two categories: they violate a policy which is specifically crafted to describe behaviours that are highly likely to be of concern if they are exhibited, or they exhibit behaviours which follow a pattern of a known insider threat attack. In particular, we view these concerning actions as something that we can design and implement tripwires within a system to detect. We then orchestrate these tripwires in conjunction with an anomaly detection system and present an approach to formalising tripwires of both categories. Our intention being that by having a single framework for describing them, alongside a library of existing tripwires in use, we can provide the community of practitioners and researchers with the basis to document and evolve this common understanding of tripwires.

We present a new approach to authentication using Trusted Execution Environments (TEEs), by changing the location of authentication from a remote device (e.g. remote authentication server) to user device(s) that are TEE enabled. The authentication takes place locally on the user device and only the outcome is sent back to the remote device. Our approach uses existing features and capabilities of TEEs to enhance the security of user authentication. We reverse the way traditional authentication schemes work: instead of the user presenting their authentication data to a remote device, we request the remote device to send the stored authentication template (s) to the local device. Almost paradoxically, this enhances security of authentication data by supplying it only to a trusted device, and so enabling users to authenticate the intended remote entity. This addresses issues related with bad SSL certificates on local devices, DNS poisoning, and counteracts certain threats posed by the presence of malware. We present a protocol to implement such authentication system discussing its strengths and limitations, before identifying available technologies to implement the architecture.

In Android, communications between apps and system services are supported by a transaction-based Inter-Process Communication (IPC) mechanism. Binder, as the cornerstone of this IPC mechanism, separates two communicating parties as client and server. As with any client-server model, the server should not make any assumption on the validity (sanity) of client-side transaction. To our surprise, we find this principle has frequently been overlooked in the implementation of Android system services. In this paper, we try to answer why developers keep making this seemingly simple mistake by studying more than 100 vulnerabilities on this attack surface. We analyzed these vulnerabilities to find that most of them are rooted at a common confusion of where the actual security boundary is among system developers. We thus highlight the deficiency of testing only on client-side public APIs and argue for the necessity of testing and protection on the Binder interface — the actual security boundary. Specifically, we design and implement BinderCracker, an automatic testing framework that supports context-aware fuzzing and actively manages the dependency between transactions. It does not require the source codes of the component under test, is compatible with services in different layers, and performs much more effectively than simple black-box fuzzing. We also call attention to the attack attribution problem for IPC-based attacks. The lack of OS-level support makes it very difficult to identify the culprit apps even for developers with adb access. We address this issue by providing an informative runtime diagnostic tool that tracks the origin, schema, content, and parsing details of each failed transaction. This brings transparency into the IPC process and provides an essential step for other in-depth analysis or forensics.

One important goal of black-box complexity theory is the development of complexity models allowing to derive meaningful lower bounds for whole classes of randomized search heuristics. Complementing classical runtime analysis, black-box models help us understand how algorithmic choices such as the population size, the variation operators, or the selection rules influence the optimization time. One example for such a result is the Ω(n log n) lower bound for unary unbiased algorithms on functions with a unique global optimum [Lehre/Witt, GECCO 2010], which tells us that higher arity operators or biased sampling strategies are needed when trying to beat this bound. In lack of analyzing techniques, almost no non-trivial bounds are known for other restricted models. Proving such bounds therefore remains to be one of the main challenges in black-box complexity theory. With this paper we contribute to our technical toolbox for lower bound computations by proposing a new type of information-theoretic argument. We regard the permutation- and bit-invariant version of LeadingOnes and prove that its (1+1) elitist black-box complexity is Ω(n2), a bound that is matched by (1+1)-type evolutionary algorithms. The (1+1) elitist complexity of LeadingOnes is thus considerably larger than its unrestricted one, which is known to be of order n log log n [Afshani et al., 2013].

Ransomwares have become a growing threat since 2012, and the situation continues to worsen until now. The lack of security mechanisms and security awareness are pushing the systems into mire of ransomware attacks. In this paper, a new framework called 2entFOX' is proposed in order to detect high survivable ransomwares (HSR). To our knowledge this framework can be considered as one of the first frameworks in ransomware detection because of little publicly-available research in this field. We analyzed Windows ransomwares' behaviour and we tried to find appropriate features which are particular useful in detecting this type of malwares with high detection accuracy and low false positive rate. After hard experimental analysis we extracted 20 effective features which due to two highly efficient ones we could achieve an appropriate set for HSRs detection. After proposing architecture based on Bayesian belief network, the final evaluation is done on some known ransomware samples and unknown ones based on six different scenarios. The result of this evaluations shows the high accuracy of 2entFox in detection of HSRs.

Deep packet inspection (DPI) is widely used in content-aware network applications to detect string features. It is of vital importance to improve the DPI performance due to the ever-increasing link speed. In this demo, we propose a novel DPI architecture with a hierarchy memory structure and parallel matching engines based on memory-centric FPGA. The implemented DPI prototype is able to provide up to 60Gbps full-text string matching throughput and fast rules update speed.

Abnormality detection is useful in reducing the amount of data to be processed manually by directing attention to the specific portion of data. However, selections of suitable features are important for the success of an abnormality detection system. Designing and selecting appropriate features are time-consuming, requires expensive domain knowledge and human labor. Further, it is very challenging to represent high-level concepts of abnormality in terms of raw input. Most of the existing abnormality detection system use handcrafted feature detector and are based on shallow architecture. In this work, we explore Deep Belief Network for abnormality detection and simultaneously, compared the performance of classic neural network in terms of features learned and accuracy of detecting the abnormality. Further, we explore the set of features learn by each layer of the deep architecture. We also provide a simple and fast mechanism to visualize the feature at the higher layer. Further, the effect of different activation function on abnormality detection is also compared. We observed that deep learning based approach can be used for detecting an abnormality. It has better performance compare to classical neural network in separating distinct as well as almost similar data.

We propose an interactive approach where analysts reason about the security of a system using an abstraction of its runtime structure, as opposed to looking at the code. They interactively refine a hierarchical object graph, set security properties on abstract objects or edges, query the graph, and investigate the results by studying highlighted objects or edges or tracing to the code. Behind the scenes, an inference analysis and an extraction analysis maintain the soundness of the graph with respect to the code.

Machine learning is enabling a myriad innovations, including new algorithms for cancer diagnosis and self-driving cars. The broad use of machine learning makes it important to understand the extent to which machine-learning algorithms are subject to attack, particularly when used in applications where physical security or safety is at risk. In this paper, we focus on facial biometric systems, which are widely used in surveillance and access control. We define and investigate a novel class of attacks: attacks that are physically realizable and inconspicuous, and allow an attacker to evade recognition or impersonate another individual. We develop a systematic method to automatically generate such attacks, which are realized through printing a pair of eyeglass frames. When worn by the attacker whose image is supplied to a state-of-the-art face-recognition algorithm, the eyeglasses allow her to evade being recognized or to impersonate another individual. Our investigation focuses on white-box face-recognition systems, but we also demonstrate how similar techniques can be used in black-box scenarios, as well as to avoid face detection.

There is growing evidence that spear phishing campaigns are increasingly pervasive, sophisticated, and remain the starting points of more advanced attacks. Current campaign identification and attribution process heavily relies on manual efforts and is inefficient in gathering intelligence in a timely manner. It is ideal that we can automatically attribute spear phishing emails to known campaigns and achieve early detection of new campaigns using limited labelled emails as the seeds. In this paper, we introduce four categories of email profiling features that capture various characteristics of spear phishing emails. Building on these features, we implement and evaluate an affinity graph based semi-supervised learning model for campaign attribution and detection. We demonstrate that our system, using only 25 labelled emails, achieves 0.9 F1 score with a 0.01 false positive rate in known campaign attribution, and is able to detect previously unknown spear phishing campaigns, achieving 100% 'darkmoon', over 97% of 'samkams' and 91% of 'bisrala' campaign detection using 246 labelled emails in our experiments.

Modern Industrial Control Systems (ICS) rely on enterprise to plant floor connectivity. Where the size, diversity, and therefore complexity of ICS increase, operational requirements, goals, and challenges defined by users across various sub-systems follow. Recent trends in Information Technology (IT) and Operational Technology (OT) convergence may cause operators to lose a comprehensive understanding of end-to-end data flow requirements. This presents a risk to system security and resilience. Sensors were once solely applied for operational process use, but now act as inputs supporting a diverse set of organisational requirements. If these are not fully understood, incomplete risk assessment, and inappropriate implementation of security controls could occur. In search of a solution, operators may turn to standards and guidelines. This paper reviews popular standards and guidelines, prior to the presentation of a case study and conceptual tool, highlighting the importance of data flows, critical data processing points, and system-to-user relationships. The proposed approach forms a basis for risk assessment and security control implementation, aiding the evolution of ICS security and resilience.

Today, outsourcing query processing tasks to remote cloud servers becomes a viable option; such outsourcing calls for encrypting data stored at the server so as to render it secure against eavesdropping adversaries and/or an honest-but-curious server itself. At the same time, to be efficiently managed, outsourced data should be indexed, and even adaptively so, as a side-effect of query processing. Computationally heavy encryption schemes render such outsourcing unattractive; an alternative, Order-Preserving Encryption Scheme (OPES), intentionally preserves and reveals the order in the data, hence is unattractive from the security viewpoint. In this paper, we propose and analyze a scheme for lightweight and indexable encryption, based on linear-algebra operations. Our scheme provides higher security than OPES and allows for range and point queries to be efficiently evaluated over encrypted numeric data, with decryption performed at the client side. We implement a prototype that performs incremental, query-triggered adaptive indexing over encrypted numeric data based on this scheme, without leaking order information in advance, and without prohibitive overhead, as our extensive experimental study demonstrates.

Security in Mobile Ad Hoc networks is still ongoing research in the scientific community and it is difficult bring an overall security solution. In this paper we assess feasibility of distributed firewall solutions in the Mobile Ad Hoc Networks. Attention is also focused on different security solutions in the Ad Hoc networks. We propose a security architecture which secures network on the several layers and is the most secured solution out of analyzed materials. For this purpose we use distributed public key infrastructure, distributed firewall and intrusion detection system. Our architecture is using both symmetric and asymmetric cryptography and in this paper we present performance measurements and the security analysis of our solution.

As more and more cyber security incident data ranging from systems logs to vulnerability scan results are collected, manually analyzing these collected data to detect important cyber security events become impossible. Hence, data mining techniques are becoming an essential tool for real-world cyber security applications. For example, a report from Gartner [gartner12] claims that "Information security is becoming a big data analytics problem, where massive amounts of data will be correlated, analyzed and mined for meaningful patterns". Of course, data mining/analytics is a means to an end where the ultimate goal is to provide cyber security analysts with prioritized actionable insights derived from big data. This raises the question, can we directly apply existing techniques to cyber security applications? One of the most important differences between data mining for cyber security and many other data mining applications is the existence of malicious adversaries that continuously adapt their behavior to hide their actions and to make the data mining models ineffective. Unfortunately, traditional data mining techniques are insufficient to handle such adversarial problems directly. The adversaries adapt to the data miner's reactions, and data mining algorithms constructed based on a training dataset degrades quickly. To address these concerns, over the last couple of years new and novel data mining techniques which is more resilient to such adversarial behavior are being developed in machine learning and data mining community. We believe that lessons learned as a part of this research direction would be beneficial for cyber security researchers who are increasingly applying machine learning and data mining techniques in practice. To give an overview of recent developments in adversarial data mining, in this three hour long tutorial, we introduce the foundations, the techniques, and the applications of adversarial data mining to cyber security applications. We first introduce various approaches proposed in the past to defend against active adversaries, such as a minimax approach to minimize the worst case error through a zero-sum game. We then discuss a game theoretic framework to model the sequential actions of the adversary and the data miner, while both parties try to maximize their utilities. We also introduce a modified support vector machine method and a relevance vector machine method to defend against active adversaries. Intrusion detection and malware detection are two important application areas for adversarial data mining models that will be discussed in details during the tutorial. Finally, we discuss some practical guidelines on how to use adversarial data mining ideas in generic cyber security applications and how to leverage existing big data management tools for building data mining algorithms for cyber security.