Biblio

Security evaluation of diverse SDN frameworks is of significant importance to design resilient systems and deal with attacks. Focused on SDN scenarios, a game-theoretic model is proposed to analyze their security performance in existing SDN architectures. The model can describe specific traits in different structures, represent several types of information of players (attacker and defender) and quantitatively calculate systems' reliability. Simulation results illustrate dynamic SDN structures have distinct security improvement over static ones. Besides, effective dynamic scheduling mechanisms adopted in dynamic systems can enhance their security further.

Efficient management and control of modern and next-gen networks is of paramount importance as networks have to maintain highly reliable service quality whilst supporting rapid growth in traffic demand and new application services. Rapid mitigation of network service degradations is a key factor in delivering high service quality. Automation is vital to achieving rapid mitigation of issues, particularly at the network edge where the scale and diversity is the greatest. This automation involves the rapid detection, localization and (where possible) repair of service-impacting faults and performance impairments. However, the most significant challenge here is knowing what events to detect, how to correlate events to localize an issue and what mitigation actions should be performed in response to the identified issues. These are defined as policies to systems such as ECOMP. In this paper, we present AESOP, a data-driven intelligent system to facilitate automatic learning of policies and rules for triggering remedial actions in networks. AESOP combines best operational practices (domain knowledge) with a variety of measurement data to learn and validate operational policies to mitigate service issues in networks. AESOP's design addresses the following key challenges: (i) learning from high-dimensional noisy data, (ii) capturing multiple fault models, (iii) modeling the high service-cost of false positives, and (iv) accounting for the evolving network infrastructure. We present the design of our system and show results from our ongoing experiments to show the effectiveness of our policy leaning framework.

Code reuse detection is a key technique in reverse engineering. However, existing source code similarity comparison techniques are not applicable to binary code. Moreover, compilers have made this problem even more difficult due to the fact that different assembly code and control flow structures can be generated by the compilers even when implementing the same functionality. To address this problem, we present a fuzzy matching approach to compare two functions. We first obtain an initial mapping between basic blocks by leveraging the concept of longest common subsequence on the basic block level and execution path level. We then extend the achieved mapping using neighborhood exploration. To make our approach applicable to large data sets, we designed an effective filtering process using Minhashing. Based on the proposed approach, we implemented a tool named BinSequence and conducted extensive experiments with it. Our results show that given a large assembly code repository with millions of functions, BinSequence is efficient and can attain high quality similarity ranking of assembly functions with an accuracy of above 90%. We also present several practical use cases including patch analysis, malware analysis and bug search.

Security challenges are the most important obstacles for the advancement of IT-based on-demand services and cloud computing as an emerging technology. Lack of coincidence in identity management models based on defined policies and various security levels in different cloud servers is one of the most challenging issues in clouds. In this paper, a policy- based user authentication model has been presented to provide a reliable and scalable identity management and to map cloud users' access requests with defined polices of cloud servers. In the proposed schema several components are provided to define access policies by cloud servers, to apply policies based on a structural and reliable ontology, to manage user identities and to semantically map access requests by cloud users with defined polices. Finally, the reliability and efficiency of this policy-based authentication schema have been evaluated by scientific performance, security and competitive analysis. Overall, the results show that this model has met defined demands of the research to enhance the reliability and efficiency of identity management in cloud computing environments.

Cloud computing is significantly reshaping the computing industry built around core concepts such as virtualization, processing power, connectivity and elasticity to store and share IT resources via a broad network. It has emerged as the key technology that unleashes the potency of Big Data, Internet of Things, Mobile and Web Applications, and other related technologies; but it also comes with its challenges - such as governance, security, and privacy. This paper is focused on the security and privacy challenges of cloud computing with specific reference to user authentication and access management for cloud SaaS applications. The suggested model uses a framework that harnesses the stateless and secure nature of JWT for client authentication and session management. Furthermore, authorized access to protected cloud SaaS resources have been efficiently managed. Accordingly, a Policy Match Gate (PMG) component and a Policy Activity Monitor (PAM) component have been introduced. In addition, other subcomponents such as a Policy Validation Unit (PVU) and a Policy Proxy DB (PPDB) have also been established for optimized service delivery. A theoretical analysis of the proposed model portrays a system that is secure, lightweight and highly scalable for improved cloud resource security and management.

In the multi-cloud tenancy environments, Web Service offers an standard approach for discovering and using capabilities in an environment that transcends ownership domains. This brings into concern the ownership and security related to Web Service governance. Our approach for this issue involves an ESB-integrated middleware for security criteria regulation on Clouds. It uses an attribute-based security policy model for the exhibition of assets consumers' security profiles and deducing service accessing decision. Assets represent computing power/functionality and information/data provided by entities. Experiments show the middleware to bring minor governance burdens on the hardware aspect, as well as better performance with colosum scaling property, dealing well with cumbersome policy files, which is probably the situation of complex composite service scenarios.

With the coming of the age of big data, efforts to institutionalize de-identification of personal information to protect privacy but also at the same time, to allow the use of personal information, have been actively carried out and already, many countries are in the stage of implementing and establishing de-identification policies quite actively. But even with such efforts to protect and use personal information at the same time, the danger posed by re-identification based on de-identified information is real enough to warrant serious consideration for a management mechanism of such risks as well as a mechanism for distributing the responsibilities and liabilities that follow these risks in the event of accidents and incidents involving the invasion of privacy. So far, most countries implementing the de-identification policies are focusing on defining what de-identification is and the exemption requirements to allow free use of de-identified personal information; in fact, it seems that there is a lack of discussion and consideration on how to distribute the responsibility of the risks and liabilities involved in the process of de-identification of personal information. This study proposes to take a look at the various de-identification policies worldwide and contemplate on these policies in the perspective of risk-liability theory. Also, the constituencies of the de-identification policies will be identified in order to analyze the roles and responsibilities of each of these constituencies thereby providing the theoretical basis on which to initiate the discussions on the distribution of burden and responsibilities arising from the de-identification policies.

Logic locking has been conceived as a promising proactive defense strategy against intellectual property (IP) piracy, counterfeiting, hardware Trojans, reverse engineering, and overbuilding attacks. Yet, various attacks that use a working chip as an oracle have been launched on logic locking to successfully retrieve its secret key, undermining the defense of all existing locking techniques. In this paper, we propose stripped-functionality logic locking (SFLL), which strips some of the functionality of the design and hides it in the form of a secret key(s), thereby rendering on-chip implementation functionally different from the original one. When loaded onto an on-chip memory, the secret keys restore the original functionality of the design. Through security-aware synthesis that creates a controllable mismatch between the reverse-engineered netlist and original design, SFLL provides a quantifiable and provable resilience trade-off between all known and anticipated attacks. We demonstrate the application of SFLL to large designs (textgreater100K gates) using a computer-aided design (CAD) framework that ensures attaining the desired security level at minimal implementation cost, 8%, 5%, and 0.5% for area, power, and delay, respectively. In addition to theoretical proofs and simulation confirmation of SFLL's security, we also report results from the silicon implementation of SFLL on an ARM Cortex-M0 microprocessor in 65nm technology.

Logic locking is an intellectual property (IP) protection technique that prevents IP piracy, reverse engineering and overbuilding attacks by the untrusted foundry or end-users. Existing logic locking techniques are all based on locking the functionality; the design/chip is nonfunctional unless the secret key has been loaded. Existing techniques are vulnerable to various attacks, such as sensitization, key-pruning, and signal skew analysis enabled removal attacks. In this paper, we propose a tenacious and traceless logic locking technique, TTlock, that locks functionality and provably withstands all known attacks, such as SAT-based, sensitization, removal, etc. TTLock protects a secret input pattern; the output of a logic cone is flipped for that pattern, where this flip is restored only when the correct key is applied. Experimental results confirm our theoretical expectations that the computational complexity of attacks launched on TTLock grows exponentially with increasing key-size, while the area, power, and delay overhead increases only linearly. In this paper, we also coin ``parametric locking," where the design/chip behaves as per its specifications (performance, power, reliability, etc.) only with the secret key in place, and an incorrect key downgrades its parametric characteristics. We discuss objectives and challenges in parametric locking.

The globalization of integrated circuit (IC) supply chain and the emergence of threats, such as intellectual property (IP) piracy, reverse engineering, and hardware Trojans, have forced semiconductor companies to revisit the trust in the supply chain. Logic locking is emerging as a popular and effective countermeasure against these threats. Over the years, multiple logic techniques have been developed. Moreover, a number of attacks have been proposed that expose the security vulnerabilities of these techniques. This paper highlights the key developments in the logic locking research and presents a comprehensive literature review of logic locking.

Logic locking is an intellectual property (IP) protection technique that prevents IP piracy, reverse engineering and overbuilding attacks by the untrusted foundry or endusers. Existing logic locking techniques are all vulnerable to various attacks, such as sensitization, key-pruning and signal skew analysis enabled removal attacks. In this paper, we propose TTLock that provably withstands all known attacks. TTLock protects a designer-specified number of input patterns, enabling a controlled and provably-secure trade-off between key-pruning attack resilience and removal attack resilience. All the key-bits converge on a single signal, creating maximal interference and thus resisting sensitization attacks. And, obfuscation is performed by modifying the design IP in a secret and traceless way, thwarting signal skew analysis and the removal attack it enables. Experimental results confirm our theoretical expectations that the computational complexity of attacks launched on TTLock grows exponentially with increasing key-size, while the area, power, and delay overhead increases only linearly.

The size of counterfeiting activities is increasing day by day. These activities are encountered especially in electronics market. In this paper, a countermeasure against counterfeiting on intellectual properties (IP) on Field-Programmable Gate Arrays (FPGA) is proposed. FPGA vendors provide bitstream ciphering as an IP security solution such as battery-backed or non-volatile FPGAs. However, these solutions are secure as long as they can keep decryption key away from third parties. Key storage and key transfer over unsecure channels expose risks for these solutions. In this work, physical unclonable functions (PUFs) have been used for key generation. Generating a key from a circuit in the device solves key transfer problem. Proposed system goes through different phases when it operates. Therefore, partial reconfiguration feature of FPGAs is essential for feasibility of proposed system.

Due to the globalization of Integrated Circuit (IC) design in the semiconductor industry and the outsourcing of chip manufacturing, third Party Intellectual Properties (3PIPs) become vulnerable to IP piracy, reverse engineering, counterfeit IC, and hardware trojans. A designer has to employ a strong technique to thwart such attacks, e.g. using Strong Logic Locking method [1]. But, such technique cannot be used to protect some circuits since the inserted key-gates rely on the topology of the circuit. Also, it requires higher power, delay, and area overheads compared to other techniques. In this paper, we present the use of spintronic devices to help protect ICs with less performance overhead. We then evaluate the proposed design based on security metric and performance overhead. One of the best spintronic device candidates is the All Spin Logic due to its unique properties: small area, no spin-charge signal conversion, and its compatibility with conventional CMOS technology.

Internet of Things (IoT) is an integral part of application domains such as smart-home and digital healthcare. Various standard public key cryptography techniques (e.g., key exchange, public key encryption, signature) are available to provide fundamental security services for IoTs. However, despite their pervasiveness and well-proven security, they also have been shown to be highly energy costly for embedded devices. Hence, it is a critical task to improve the energy efficiency of standard cryptographic services, while preserving their desirable properties simultaneously. In this paper, we exploit synergies among various cryptographic primitives with algorithmic optimizations to substantially reduce the energy consumption of standard cryptographic techniques on embedded devices. Our contributions are: (i) We harness special precomputation techniques, which have not been considered for some important cryptographic standards to boost the performance of key exchange, integrated encryption, and hybrid constructions. (ii) We provide self-certification for these techniques to push their performance to the edge. (iii) We implemented our techniques and their counterparts on 8-bit AVR ATmega 2560 and evaluated their performance. We used microECC library and made the implementations on NIST-recommended secp192 curve, due to its standardization. Our experiments confirmed significant improvements on the battery life (up to 7x) while preserving the desirable properties of standard techniques. Moreover, to the best of our knowledge, we provide the first open-source framework including such set of optimizations on low-end devices.

Cyber-physical-social systems (CPSS), an emerging computing paradigm, have attracted intensive attentions from the research community and industry. We are facing various challenges in designing secure, reliable, and user-satisfied CPSS. In this article, we consider these design issues as a whole and propose a system-level design optimization framework for CPSS design where energy consumption, security-level, and user satisfaction requirements can be fulfilled while satisfying constraints for system reliability. Specifically, we model the constraints (energy efficiency, security, and reliability) as the penalty functions to be incorporated into the corresponding objective functions for the optimization problem. A smart office application is presented to demonstrate the feasibility and effectiveness of our proposed design optimization approach.

Communication networks can be the targets of organized and distributed attacks such as flooding-type DDOS attack in which malicious users aim to cripple a network server or a network domain. For the attack to have a major effect on the network, malicious users must act in a coordinated and time correlated manner. For instance, the members of the flooding attack increase their message transmission rates rapidly but also synchronously. Even though detection and prevention of the flooding attacks are well studied at network and transport layers, the emergence and wide deployment of new systems such as VoIP (Voice over IP) have turned flooding attacks at the session layer into a new defense challenge. In this study a structured sparsity based group anomaly detection system is proposed that not only can detect synchronized attacks, but also identify the malicious groups from normal users by jointly estimating their members, structure, starting and end points. Although we mainly focus on security on SIP (Session Initiation Protocol) servers/proxies which are widely used for signaling in VoIP systems, the proposed scheme can be easily adapted for any type of communication network system at any layer.

In this paper, the correctness of the routing algorithm for the distributed key-value store based on order preserving linear hashing and Skip Graph is proved. In this system, data are divided by linear hashing and Skip Graph is used for overlay network. The routing table of this system is very uniform. Then, short detours can exist in the route of forwarding. By using these detours, the number of hops for the query forwarding is reduced.

Compared to other remote attestation methods, the binary-based approach is the most direct and complete one, but privacy protection has become an important problem. In this paper, we presented an Extended Hash Algorithm (EHA) for privacy protection based on remote attestation method. Based on the traditional Merkle Hash Tree, EHA altered the algorithm of node connection. The new algorithm could ensure the same result in any measure order. The security key is added when the node connection calculation is performed, which ensures the security of the value calculated by the Merkle node. By the final analysis, we can see that the remote attestation using EHA has better privacy protection and execution performance compared to other methods.

Recently, deep neural networks based hashing methods have greatly improved the multimedia retrieval performance by simultaneously learning feature representations and binary hash functions. Inspired by the latest advance in the asymmetric hashing scheme, in this work, we propose a novel Deep Asymmetric Pairwise Hashing approach (DAPH) for supervised hashing. The core idea is that two deep convolutional models are jointly trained such that their output codes for a pair of images can well reveal the similarity indicated by their semantic labels. A pairwise loss is elaborately designed to preserve the pairwise similarities between images as well as incorporating the independence and balance hash code learning criteria. By taking advantage of the flexibility of asymmetric hash functions, we devise an efficient alternating algorithm to optimize the asymmetric deep hash functions and high-quality binary code jointly. Experiments on three image benchmarks show that DAPH achieves the state-of-the-art performance on large-scale image retrieval.

This paper proposes a novel recursive hashing scheme, in contrast to conventional "one-off" based hashing algorithms. Inspired by human's "nonsalient-to-salient" perception path, the proposed hashing scheme generates a series of binary codes based on progressively expanded salient regions. Built on a recurrent deep network, i.e., LSTM structure, the binary codes generated from later output nodes naturally inherit information aggregated from previously codes while explore novel information from the extended salient region, and therefore it possesses good scalability property. The proposed deep hashing network is trained via minimizing a triplet ranking loss, which is end-to-end trainable. Extensive experimental results on several image retrieval benchmarks demonstrate good performance gain over state-of-the-art image retrieval methods and its scalability property.

Concise is a Forwarding information base (FIB) design that uses very little memory to support fast query of a large number of dynamic network names or flow IDs. Concise makes use of minimal perfect hashing and the SDN framework to design and implement the data structure, protocols, and system. Experimental results show that Concise uses significantly smaller memory to achieve faster query speed compared to existing FIB solutions and it can be updated very efficiently.

Computer security tools usually provide universal solutions without taking user characteristics (origin, income level, ...) into account. In this paper, we test the validity of using such universal security defenses, with a particular focus on culture. We apply the previously proposed Security Behavior Intentions Scale (SeBIS) to 3,500 participants from seven countries. We first translate the scale into seven languages while preserving its reliability and structure validity. We then build a regression model to study which factors affect participants' security behavior. We find that participants from different countries exhibit different behavior. For instance, participants from Asian countries, and especially Japan, tend to exhibit less secure behavior. Surprisingly to us, we also find that actual knowledge influences user behavior much less than user self-confidence in their computer security knowledge. Stated differently, what people think they know affects their security behavior more than what they do know.

The increasingly connected world has catalyzed the fusion of networks from different domains, which facilitates the emergence of a new network model—multi-layered networks. Examples of such kind of network systems include critical infrastructure networks, biological systems, organization-level collaborations, cross-platform e-commerce, and so forth. One crucial structure that distances multi-layered network from other network models is its cross-layer dependency, which describes the associations between the nodes from different layers. Needless to say, the cross-layer dependency in the network plays an essential role in many data mining applications like system robustness analysis and complex network control. However, it remains a daunting task to know the exact dependency relationships due to noise, limited accessibility, and so forth. In this article, we tackle the cross-layer dependency inference problem by modeling it as a collective collaborative filtering problem. Based on this idea, we propose an effective algorithm F\textbackslashtextlessscp;\textbackslashtextgreaterascinate\textbackslashtextless/scp;\textbackslashtextgreater that can reveal unobserved dependencies with linear complexity. Moreover, we derive F\textbackslashtextlessscp;\textbackslashtextgreaterascinate\textbackslashtextless/scp;\textbackslashtextgreater-ZERO, an online variant of F\textbackslashtextlessscp;\textbackslashtextgreaterascinate\textbackslashtextless/scp;\textbackslashtextgreater that can respond to a newly added node timely by checking its neighborhood dependencies. We perform extensive evaluations on real datasets to substantiate the superiority of our proposed approaches.

In this work, Automatic-Repeat-Request (ARQ) and Maximal Ratio Combination (MRC), have been jointly exploited to enhance the confidentiality of wireless services requested by a legitimate user (Bob) against an eavesdropper (Eve). The obtained security performance is analyzed using Packet Error Rate (PER), where the exact PER gap between Bob and Eve is determined. PER is proposed as a new practical security metric in cross layers (Physical/MAC) security design since it reflects the influence of upper layers mechanisms, and it can be linked with Quality of Service (QoS) requirements for various digital services such as voice and video. Exact PER formulas for both Eve and Bob in i.i.d Rayleigh fading channel are derived. The simulation and theoretical results show that the employment of ARQ mechanism and MRC on a signal level basis before demodulation can significantly enhance data security for certain services at specific SNRs. However, to increase and ensure the security of a specific service at any SNR, adaptive modulation is proposed to be used along with the aforementioned scheme. Analytical and simulation studies demonstrate orders of magnitude difference in PER performance between eavesdroppers and intended receivers.

The wide-spreading mobile malware has become a dreadful issue in the increasingly popular mobile networks. Most of the mobile malware relies on network interface to coordinate operations, steal users' private information, and launch attack activities. In this paper, we propose TextDroid, an effective and automated malware detection method combining natural language processing and machine learning. TextDroid can extract distinguishable features (n-gram sequences) to characterize malware samples. A malware detection model is then developed to detect mobile malware using a Support Vector Machine (SVM) classifier. The trained SVM model presents a superior performance on two different data sets, with the malware detection rate reaching 96.36% in the test set and 76.99% in an app set captured in the wild, respectively. In addition, we also design a flow header visualization method to visualize the highlighted texts generated during the apps' network interactions, which assists security researchers in understanding the apps' complex network activities.