Biblio

The base station (BS) is the main device in a wireless sensor network (WSN) and used to collect data from all the sensor nodes. The information of the whole network is stored in the BS and hence it is always targeted by the adversaries who want to interrupt the operation of the network. The nodes transmit their data to the BS using multi-hop technique and hence form an eminent traffic pattern that can be easily observed by a remote adversary. The presented research aims to increase the anonymity of the BS. The proposed scheme uses a mobile BS and ring nodes to complete the above mentioned objective. The simulation results show that the proposed scheme has superior outcomes as compared to the existing techniques.

We present, VIP, an approach to boosting the precision of Virtual call Integrity Protection for large-scale real-world C++ programs (e.g., Chrome) by using pointer analysis for the first time. VIP introduces two new techniques: (1) a sound and scalable partial pointer analysis for discovering statically the sets of legitimate targets at virtual callsites from separately compiled C++ modules and (2) a lightweight instrumentation technique for performing (virtual call) integrity checks at runtime. VIP raises the bar against vtable hijacking attacks by providing stronger security guarantees than the CHA-based approach with comparable performance overhead. VIP is implemented in LLVM-3.8.0 and evaluated using SPEC programs and Chrome. Statically, VIP protects virtual calls more effectively than CHA by significantly reducing the sets of legitimate targets permitted at 20.3% of the virtual callsites per program, on average. Dynamically, VIP incurs an average (maximum) instrumentation overhead of 0.7% (3.3%), making it practically deployable as part of a compiler tool chain.

We show that the basic semidefinite programming relaxation value of any constraint satisfaction problem can be computed in NC; that is, in parallel polylogarithmic time and polynomial work. As a complexity-theoretic consequence we get that $\backslash$MIPone[k,c,s] $\backslash$subseteq $\backslash$PSPACE provided s/c $\backslash$leq (.62-o(1))k/2textasciicircumk, resolving a question of Austrin, H$\backslash$aa stad, and Pass. Here $\backslash$MIPone[k,c,s] is the class of languages decidable with completeness c and soundness s by an interactive proof system with k provers, each constrained to communicate just 1 bit.

Public-key cryptography (PKC), widely used to protect communication in the Internet of Things (IoT), is the basis for establishing secured communication channels between multiple parties. The foreseeable breakthrough of quantum computers represents a risk for many PKC ecosystems. Almost all approaches in use today rely on the hardness of factoring large integers or computing (elliptic-curve) discrete logarithms. It is known that cryptography based on these problems can be broken in polynomial time by Shors algorithm, once a large enough quantum computer is built. In order to prepare for such an event, the integration of quantum-resistant cryptography on devices operating in the IoT is mandatory to achieve long-term security. Due to their limited resources, tight performance requirements and long-term life-cycles, this is especially challenging for Multi-Processor System-on-Chips (MPSoCs) operating in this context. At the same time, it must be provided that well-known implementation attacks, such as those targeting a cipher's execution time or its use of the processor cache, are inhibited, as they've successfully been used to attack cryptosystems in the pre-quantum era. Hence, this work presents an analysis of the security-critical polynomial multiplication routine within the NTRU algorithm and its susceptibility to timing and cache attacks. We also propose two different countermeasures to harden systems with or without caches against said attacks, and include the evaluation of the respective overheads. We demonstrate that security against timing and cache attacks can be achieved with reasonable overheads depending on the chosen parameters of NTRU.

Wireless mesh network (WMN) consists of mesh gateways, mesh routers and mesh clients. In hybrid WMN, both backbone mesh network and client mesh network are mesh connected. Capacity analysis of multi-hop wireless networks has proven to be an interesting and challenging research topic. The capacity of hybrid WMN depends on several factors such as traffic model, topology, scheduling strategy and bandwidth allocation strategy, etc. In this paper, the capacity of hybrid WMN is studied according to the traffic model and bandwidth allocation. The traffic of hybrid WMN is categorized into internal and external traffic. Then the capacity of each mesh client is deduced according to appropriate bandwidth allocation. The analytical results show that hybrid WMN achieves lower capacity than infrastructure WMN. The results and conclusions can guide for the construction of hybrid WMN.

Detection and prevention of data breaches in corporate networks is one of the most important security problems of today's world. The techniques and applications proposed for solution are not successful when attackers attempt to steal data using steganography. Steganography is the art of storing data in a file called cover, such as picture, sound and video. The concealed data cannot be directly recognized in the cover. Steganalysis is the process of revealing the presence of embedded messages in these files. There are many statistical and signature based steganalysis algorithms. In this work, the detection of steganographic images with steganalysis techniques is reviewed and a system has been developed which automatically detects steganographic images in network traffic by using open source tools.

Secure by design is an approach to developing secure software systems from the ground up. In such approach, the alternate security tactics are first thought, among them, the best are selected and enforced by the architecture design, and then used as guiding principles for developers. Thus, design flaws in the architecture of a software system mean that successful attacks could result in enormous consequences. Therefore, secure by design shifts the main focus of software assurance from finding security bugs to identifying architectural flaws in the design. Current research in software security has been neglecting vulnerabilities which are caused by flaws in a software architecture design and/or deteriorations of the implementation of the architectural decisions. In this paper, we present the concept of Common Architectural Weakness Enumeration (CAWE), a catalog which enumerates common types of vulnerabilities rooted in the architecture of a software and provides mitigation techniques to address them. The CAWE catalog organizes the architectural flaws according to known security tactics. We developed an interactive web-based solution which helps designers and developers explore this catalog based on architectural choices made in their project. CAWE catalog contains 224 weaknesses related to security architecture. Through this catalog, we aim to promote the awareness of security architectural flaws and stimulate the security design thinking of developers, software engineers, and architects.

Trusted routing is a hot spot in network security. Lots of efforts have been made on trusted routing validation for Interior Gateway Protocols (IGP), e.g., using Public Key Infrastructure (PKI) to enhance the security of protocols, or routing monitoring systems. However, the former is limited by further deployment in the practical Internet, the latter depends on a complete, accurate, and fresh knowledge base-this is still a big challenge (Internet Service Providers (ISPs) are not willing to leak their routing policies). In this paper, inspired by the idea of centrally controlling in Software Defined Network (SDN), we propose a CENtrally Trusted Routing vAlidation framework, named CENTRA, which can automated collect routing information, centrally detect anomaly and deliver secure routing policy. We implement the proposed framework using NETCONF as the communication protocol and YANG as the data model. The experimental results reveal that CENTRA can detect and block anomalous routing in real time. Comparing to existing secure routing mechanism, CENTRA improves the detection efficiency and real-time significantly.

Digitally signed malware can bypass system protection mechanisms that install or launch only programs with valid signatures. It can also evade anti-virus programs, which often forego scanning signed binaries. Known from advanced threats such as Stuxnet and Flame, this type of abuse has not been measured systematically in the broader malware landscape. In particular, the methods, effectiveness window, and security implications of code-signing PKI abuse are not well understood. We propose a threat model that highlights three types of weaknesses in the code-signing PKI. We overcome challenges specific to code-signing measurements by introducing techniques for prioritizing the collection of code signing certificates that are likely abusive. We also introduce an algorithm for distinguishing among different types of threats. These techniques allow us to study threats that breach the trust encoded in the Windows code signing PKI. The threats include stealing the private keys associated with benign certificates and using them to sign malware or by impersonating legitimate companies that do not develop software and, hence, do not own code-signing certificates. Finally, we discuss the actionable implications of our findings and propose concrete steps for improving the security of the code-signing ecosystem.

This paper considers the security problem of outsourcing storage from user devices to the cloud. A secure searchable encryption scheme is presented to enable searching of encrypted user data in the cloud. The scheme simultaneously supports fuzzy keyword searching and matched results ranking, which are two important factors in facilitating practical searchable encryption. A chaotic fuzzy transformation method is proposed to support secure fuzzy keyword indexing, storage and query. A secure posting list is also created to rank the matched results while maintaining the privacy and confidentiality of the user data, and saving the resources of the user mobile devices. Comprehensive tests have been performed and the experimental results show that the proposed scheme is efficient and suitable for a secure searchable cloud storage system.

The MgO-based magnetic tunnel junction (MTJ) is the basis of modern hard disk drives' magnetic read sensors. Within its operating bandwidth, the sensor's performance is significantly affected by nonlinear and oscillating behavior arising from the MTJ's magnetization dynamics at microwave frequencies. Static I-V curve measurements are commonly used to characterize sensor's nonlinear effects. Unfortunately, these do not sufficiently capture the MTJ's magnetization dynamics. In this paper, we demonstrate the use of the two-tone measurement technique for full treatment of the sensor's nonlinear effects in conjunction with dynamic ones. This approach is new in the field of magnetism and magnetic materials, and it has its challenges due to the nature of the device. Nevertheless, the experimental results demonstrate how the two-tone measurement technique can be used to characterize magnetic sensor nonlinear properties.

Industrial Control Systems (ICS) are widely deployed in mission critical infrastructures such as manufacturing, energy, and transportation. The mission critical nature of ICS devices poses important security challenges for ICS vendors and asset owners. In particular, the patching of ICS devices is usually deferred to scheduled production outages so as to prevent potential operational disruption of critical systems. In this paper, we present the results from our longitudinal measurement and characterization study of ICS patching behavior. Our analysis of more than 100 thousand Internet-exposed ICS devices reveals that fewer than 30% upgrade to newer patched versions within 60 days of a vulnerability disclosure. Based on our measurement and analysis, we further propose a model to forecast the patching behavior of ICS devices.

This tutorial will present a systematic overview of \$\backslash$em kleptography\: stealing information subliminally from black-box cryptographic implementations; and \$\backslash$em cliptography\: defending mechanisms that clip the power of kleptographic attacks via specification re-designs (without altering the underlying algorithms). Despite the laudatory history of development of modern cryptography, applying cryptographic tools to reliably provide security and privacy in practice is notoriously difficult. One fundamental practical challenge, guaranteeing security and privacy without explicit trust in the algorithms and implementations that underlie basic security infrastructure, remains. While the dangers of entertaining adversarial implementation of cryptographic primitives seem obvious, the ramifications of such attacks are surprisingly dire: it turns out that – in wide generality – adversarial implementations of cryptographic (both deterministic and randomized) algorithms may leak private information while producing output that is statistically indistinguishable from that of a faithful implementation. Such attacks were formally studied in Kleptography. Snowden revelations has shown us how security and privacy can be lost at a very large scale even when traditional cryptography seems to be used to protect Internet communication, when Kleptography was not taken into consideration. We will first explain how the above-mentioned Kleptographic attacks can be carried out in various settings. We will then introduce several simple but rigorous immunizing strategies that were inspired by folklore practical wisdoms to protect different algorithms from implementation subversion. Those strategies can be applied to ensure security of most of the fundamental cryptographic primitives such as PRG, digital signatures, public key encryptions against kleptographic attacks when they are implemented accordingly. Our new design principles may suggest new standardization methods that help reducing the threats of subverted implementation. We also hope our tutorial to stimulate a community-wise efforts to further tackle the fundamental challenge mentioned at the beginning.

Cloud computing is the expansion of parallel computing, distributed computing. The technology of cloud computing becomes more and more widely used, and one of the fundamental issues in this cloud environment is related to task scheduling. However, scheduling in Cloud environments represents a difficult issue since it is basically NP-complete. Thus, many variants based on approximation techniques, especially those inspired by Swarm Intelligence (SI) have been proposed. This paper proposes a machine learning algorithm to guide the cloud choose the scheduling technique by using multi criteria decision to optimize the performance. The main contribution of our work is to minimize the makespan of a given task set. The new strategy is simulated using the CloudSim toolkit package where the impact of the algorithm is checked with different numbers of VMs varying from 2 to 50, and different task sizes between 30 bytes and 2700 bytes. Experiment results show that the proposed algorithm minimizes the execution time and the makespan between 7% and 75%, and improves the performance of the load balancing scheduling.

Recent architectures for the advanced metering infrastructure (AMI) have incorporated several back-end systems that handle billing and other smart grid control operations. The non-availability of metering data when needed or the untimely delivery of data needed for control operations will undermine the activities of these back-end systems. Unfortunately, there are concerns that cyber attacks such as distributed denial of service (DDoS) will manifest in magnitude and complexity in a smart grid AMI network. Such attacks will range from a delay in the availability of end user's metering data to complete denial in the case of a grounded network. This paper proposes a cloud-based (IaaS) firewall for the mitigation of DDoS attacks in a smart grid AMI network. The proposed firewall has the ability of not only mitigating the effects of DDoS attack but can prevent the attack before they are launched. Our proposed firewall system leverages on cloud computing technology which has an added advantage of reducing the burden of data computations and storage for smart grid AMI back-end systems. The openflow firewall proposed in this study is a better security solution with regards to the traditional on-premises DoS solutions which cannot cope with the wide range of new attacks targeting the smart grid AMI network infrastructure. Simulation results generated from the study show that our model can guarantee the availability of metering/control data and could be used to improve the QoS of the smart grid AMI network under a DDoS attack scenario.

Organizations face the issue of how to best allocate their security resources. Thus, they need an accurate method for assessing how many new vulnerabilities will be reported for the operating systems (OSs) they use in a given time period. Our approach consists of clustering vulnerabilities by leveraging the text information within vulnerability records, and then simulating the mean value function of vulnerabilities by relaxing the monotonic intensity function assumption, which is prevalent among the studies that use software reliability models (SRMs) and nonhomogeneous Poisson process (NHPP) in modeling. We applied our approach to the vulnerabilities of four OSs: Windows, Mac, IOS, and Linux. For the OSs analyzed in terms of curve fitting and prediction capability, our results, compared to a power-law model without clustering issued from a family of SRMs, are more accurate in all cases we analyzed.

In this paper, we propose and implement CommunityGuard, a system which comprises of intelligent Guardian Nodes that learn and prevent malicious traffic from coming into and going out of a user's personal area network. In the CommunityGuard model, each Guardian Node tells others about emerging threats, blocking these threats for all users as soon as they begin. Furthermore, Guardian Nodes regularly update themselves with latest threat models to provide effective security against new and emerging threats. Our evaluation proves that CommunityGuard provides immunity against a range of incoming and outgoing attacks at all points of time with an acceptable impact on network performance. Oftentimes, the sources of DDoS attack traffic are personal devices that have been compromised without the owner's knowledge. We have modeled CommunityGuard to prevent such outgoing DDoS traffic on a wide scale which can hamstring the otherwise very frightening prospects of crippling DDoS attacks.

In the era of globalized Integrated Circuit (IC) design and manufacturing flow, a rising issue to the silicon industry is various attacks on hardware intellectual property (IP). As a measure to ensure security along the supply chain against IP piracy, tampering and reverse engineering, hardware obfuscation is considered a reliable defense mechanism. Sequential and combinational obfuscations are the primary classes of obfuscation, and multiple methods have been proposed in each type in recent years. This paper presents an overview of obfuscation techniques and a qualitative comparison of the two major types.

This paper investigates the possibility of using ensemble learning methods to improve the performance of intrusion detection systems. We compare an ensemble of three ensemble learning methods, boosting, bagging and stacking in order to improve the detection rate and to reduce the false alarm rate. These ensemble methods use well-known and different base classification algorithms, J48 (decision tree), NB (Naïve Bayes), MLP (Neural Network) and REPTree. The comparison experiments are applied on UNSW-NB15 data set a recent public data set for network intrusion detection systems. Results show that using boosting, bagging can achieve higher accuracy than single classifier but stacking performs better than other ensemble learning methods.

Mobility and multihoming have become the norm in Internet access, e.g. smartphones with Wi-Fi and LTE, and connected vehicles with LTE and DSRC links that change rapidly. Mobility creates challenges for active session continuity when provider-aggregatable locators are used, while multihoming brings opportunities for improving resiliency and allocative efficiency. This paper proposes a novel migration protocol, in the context of the eXpressive Internet Architecture (XIA), the XIA Migration Protocol. We compare it with Mobile IPv6, with respect to handoff latency and overhead, flow migration support, and defense against spoofing and replay of protocol messages. Handoff latencies of the XIA Migration Protocol and Mobile IPv6 Enhanced Route Optimization are comparable and neither protocol opens up avenues for spoofing or replay attacks. However, XIA requires no mobility anchor point to support client mobility while Mobile IPv6 always depends on a home agent. We show that XIA has significant advantage over IPv6 for multihomed hosts and networks in terms of resiliency, scalability, load balancing and allocative efficiency. IPv6 multihoming solutions either forgo scalability (BGP-based) or sacrifice resiliency (NAT-based), while XIA's fallback-based multihoming provides fault tolerance without a heavy-weight protocol. XIA also allows fine-grained incoming load-balancing and QoS-matching by supporting flow migration. Flow migration is not possible using Mobile IPv6 when a single IPv6 address is associated with multiple flows. From a protocol design and architectural perspective, the key enablers of these benefits are flow-level migration, XIA's DAG-based locators and self-certifying identifiers.

Private record linkage (PRL) is the problem of identifying pairs of records that are similar as per an input matching rule from databases held by two parties that do not trust one another. We identify three key desiderata that a PRL solution must ensure: (1) perfect precision and high recall of matching pairs, (2) a proof of end-to-end privacy, and (3) communication and computational costs that scale subquadratically in the number of input records. We show that all of the existing solutions for PRL? including secure 2-party computation (S2PC), and their variants that use non-private or differentially private (DP) blocking to ensure subquadratic cost – violate at least one of the three desiderata. In particular, S2PC techniques guarantee end-to-end privacy but have either low recall or quadratic cost. In contrast, no end-to-end privacy guarantee has been formalized for solutions that achieve subquadratic cost. This is true even for solutions that compose DP and S2PC: DP does not permit the release of any exact information about the databases, while S2PC algorithms for PRL allow the release of matching records. In light of this deficiency, we propose a novel privacy model, called output constrained differential privacy, that shares the strong privacy protection of DP, but allows for the truthful release of the output of a certain function applied to the data. We apply this to PRL, and show that protocols satisfying this privacy model permit the disclosure of the true matching records, but their execution is insensitive to the presence or absence of a single non-matching record. We find that prior work that combine DP and S2PC techniques even fail to satisfy this end-to-end privacy model. Hence, we develop novel protocols that provably achieve this end-to-end privacy guarantee, together with the other two desiderata of PRL. Our empirical evaluation also shows that our protocols obtain high recall, scale near linearly in the size of the input databases and the output set of matching pairs, and have communication and computational costs that are at least 2 orders of magnitude smaller than S2PC baselines.

Differential privacy is a rigorous privacy standard that has been applied to a range of data analysis tasks. To broaden the application scenarios of differential privacy when data records have dependencies, the notion of Bayesian differential privacy has been recently proposed. However, it is unknown whether Bayesian differential privacy preserves three nice properties of differential privacy: sequential composability, parallel composability, and post-processing. In this paper, we provide an affirmative answer to this question; i.e., Bayesian differential privacy still have these properties. The idea behind sequential composability is that if we have m algorithms Y1, Y2,łdots, Ym, where Y$\mathscrl$ is independently $ε\mathscrl$-Bayesian differential private for $\mathscrl$ = 1,2,łdots, m, then by feeding the result of Y1 into Y2, the result of Y2 into Y3, and so on, we will finally have an $Σ$m$\mathscrl$=;1 $ε\mathscrl$-Bayesian differential private algorithm. For parallel composability, we consider the situation where a database is partitioned into m disjoint subsets. The $\mathscrl$-th subset is input to a Bayesian differential private algorithm Y$\mathscrl$, for $\mathscrl$= 1, 2,łdots, m. Then the parallel composition of Y1, Y2,łdots, Ym will be maxm$\mathscrl$=;1=1 $ε\mathscrl$-Bayesian differential private. The postprocessing property means that a data analyst, without additional knowledge abo- t the private database, cannot compute a function of the output of a Bayesian differential private algorithm and reduce its privacy guarantee.

Based on the feature analysis of image content, this paper proposes a novel steganalytic method for grayscale images in spatial domain. In this work, we firstly investigates directional lifting wavelet transform (DLWT) as a sparse representation in compressive sensing (CS) domain. Then a block CS (BCS) measurement matrix is designed by using the generalized Gaussian distribution (GGD) model, in which the measurement matrix can be used to sense the DLWT coefficients of images to reflect the feature residual introduced by steganography. Extensive experiments are showed that proposed scheme CS-based is feasible and universal for detecting stegography in spatial domain.

Power grid operations rely on the trustworthy operation of critical control center functionalities, including the so-called Economic Dispatch (ED) problem. The ED problem is a large-scale optimization problem that is periodically solved by the system operator to ensure the balance of supply and load while maintaining reliability constraints. In this paper, we propose a semantics-based attack generation and implementation approach to study the security of the ED problem.1 Firstly, we generate optimal attack vectors to transmission line ratings to induce maximum congestion in the critical lines, resulting in the violation of capacity limits. We formulate a bilevel optimization problem in which the attacker chooses manipulations of line capacity ratings to maximinimize the percentage line capacity violations under linear power flows. We reformulate the bilevel problem as a mixed integer linear program that can be solved efficiently. Secondly, we describe how the optimal attack vectors can be implemented in commercial energy management systems (EMSs). The attack explores the dynamic memory space of the EMS, and replaces the true line capacity ratings stored in data regions with the optimal attack vectors. In contrast to the well-known false data injection attacks to control systems that require compromising distributed sensors, our approach directly implements attacks to the control center server. Our experimental results on benchmark power systems and five widely utilized EMSs show the practical feasibility of our attack generation and implementation approach.

Power grid operations rely on the trustworthy operation of critical control center functionalities, including the so-called Economic Dispatch (ED) problem. The ED problem is a large-scale optimization problem that is periodically solved by the system operator to ensure the balance of supply and load while maintaining reliability constraints. In this paper, we propose a semantics-based attack generation and implementation approach to study the security of the ED problem.1 Firstly, we generate optimal attack vectors to transmission line ratings to induce maximum congestion in the critical lines, resulting in the violation of capacity limits. We formulate a bilevel optimization problem in which the attacker chooses manipulations of line capacity ratings to maximinimize the percentage line capacity violations under linear power flows. We reformulate the bilevel problem as a mixed integer linear program that can be solved efficiently. Secondly, we describe how the optimal attack vectors can be implemented in commercial energy management systems (EMSs). The attack explores the dynamic memory space of the EMS, and replaces the true line capacity ratings stored in data regions with the optimal attack vectors. In contrast to the well-known false data injection attacks to control systems that require compromising distributed sensors, our approach directly implements attacks to the control center server. Our experimental results on benchmark power systems and five widely utilized EMSs show the practical feasibility of our attack generation and implementation approach.