Biblio

Protecting modern computer systems and complex software stacks against the growing range of possible attacks is becoming increasingly difficult. The architecture of modern commodity systems allows attackers to subvert privileged system software often using a single exploit. Once the system is compromised, inclusive permissions used by current architectures and operating systems easily allow a compromised high-privileged software layer to perform arbitrary malicious activities, even on behalf of other software layers. This paper presents a hardware-supported page permission scheme for the physical pages that is based on the concept of non-inclusive sets of memory permissions for different layers of system software such as hypervisors, operating systems, and user-level applications. Instead of viewing privilege levels as an ordered hierarchy with each successive level being more privileged, we view them as distinct levels each with its own set of permissions. Such a permission mechanism, implemented as part of a processor architecture, provides a common framework for defending against a range of recent attacks. We demonstrate that such a protection can be achieved with negligible performance overhead, low hardware complexity and minimal changes to the commodity OS and hypervisor code.
 

Security as a condition is the degree of resistance to, or protection from harm. Securing gadgets in a way that is simple for the user to deploy yet, stringent enough to deny any malware intrusions onto the protected circle is investigated to find a balance between the extremes. Basically, the dominant approach on current control access is via password or PIN, but its flaw is being clearly documented. An application (to be incorporated in a mobile phone) that allows the user's gadget to be used as a Biometric Capture device in addition to serve as a Biometric Signature acquisition device for processing a multi-level authentication procedure to allow access to any specific Web Service of exclusive confidentiality is proposed. To evaluate the lucidness of the proposed procedure, a specific set of domain specifications to work on are chosen and the accuracy of the Biometric face Recognition carried out is evaluated along with the compatibility of the Application developed with different sample inputs. The results obtained are exemplary compared to the existing other devices to suit a larger section of the society through the Internet for improving the security.

Data mining is the process of finding correlations in the relational databases. There are different techniques for identifying malicious database transactions. Many existing approaches which profile is SQL query structures and database user activities to detect intrusion, the log mining approach is the automatic discovery for identifying anomalous database transactions. Mining of the Data is very helpful to end users for extracting useful business information from large database. Multi-level and multi-dimensional data mining are employed to discover data item dependency rules, data sequence rules, domain dependency rules, and domain sequence rules from the database log containing legitimate transactions. Database transactions that do not comply with the rules are identified as malicious transactions. The log mining approach can achieve desired true and false positive rates when the confidence and support are set up appropriately. The implemented system incrementally maintain the data dependency rule sets and optimize the performance of the intrusion detection process.
 

Single sign-on (SSO) is an identity management technique that provides users the ability to use multiple Web services with one set of credentials. However, when the authentication server is down or unavailable, users cannot access Web services, even if the services are operating normally. Therefore, enabling continuous use is important in single sign on. In this paper, we present security framework to overcome credential problems of accessing multiple web application. We explain system functionality with authorization and Authentication. We consider these methods from the viewpoint of continuity, security and efficiency makes the framework highly secure.

By enabling a direct comparison of different security solutions with respect to their relative effectiveness, a network security metric may provide quantifiable evidences to assist security practitioners in securing computer networks. However, research on security metrics has been hindered by difficulties in handling zero-day attacks exploiting unknown vulnerabilities. In fact, the security risk of unknown vulnerabilities has been considered as something unmeasurable due to the less predictable nature of software flaws. This causes a major difficulty to security metrics, because a more secure configuration would be of little value if it were equally susceptible to zero-day attacks. In this paper, we propose a novel security metric, k-zero day safety, to address this issue. Instead of attempting to rank unknown vulnerabilities, our metric counts how many such vulnerabilities would be required for compromising network assets; a larger count implies more security because the likelihood of having more unknown vulnerabilities available, applicable, and exploitable all at the same time will be significantly lower. We formally define the metric, analyze the complexity of computing the metric, devise heuristic algorithms for intractable cases, and finally demonstrate through case studies that applying the metric to existing network security practices may generate actionable knowledge.

Today in the world of globalization mobile communication is one of the fastest growing medium though which one sender can interact with other in short time. During the transmission of data from sender to receiver, size of data is important, since more data takes more time. But one of the limitations of sending data through mobile devices is limited use of bandwidth and number of packets transmitted. Also the security of these data is important. Hence various protocols are implemented which not only provides security to the data but also utilizes bandwidth. Here we proposed an efficient technique of sending SMS text using combination of compression and encryption. The data to be send is first encrypted using Elliptic curve Cryptographic technique, but encryption increases the size of the text data, hence compression is applied to this encrypted data so the data gets compressed and is send in short time. The Compression technique implemented here is an efficient one since it includes an algorithm which compresses the text by 99.9%, hence a great amount of bandwidth gets saved.The hybrid technique of Compression-Encryption of SMS text message is implemented for Android Operating Systems.

The key challenge to a datacenter network is its scalability to handle many customers and their applications. In a datacenter network, packet classification plays an important role in supporting various network services. Previous algorithms store classification rules with the same length combinations in a hash table to simplify the search procedure. The search performance of hash-based algorithms is tied to the number of hash tables. To achieve fast and scalable packet classification, we propose an algorithm, encoded rule expansion, to transform rules into an equivalent set of rules with fewer distinct length combinations, without affecting the classification results. The new algorithm can minimize the storage penalty of transformation and achieve a short search time. In addition, the scheme supports fast incremental updates. Our simulation results show that more than 90% hash tables can be eliminated. The reduction of length combinations leads to an improvement on speed performance of packet classification by an order of magnitude. The results also show that the software implementation of our scheme without using any hardware parallelism can support up to one thousand customer VLANs and one million rules, where each rule consumes less than 60 bytes and each packet classification can be accomplished under 50 memory accesses.
 

In this paper, an algorithm is proposed to automatically produce hierarchical graph-based representations of maritime shipping lanes extrapolated from historical vessel positioning data. Each shipping lane is generated based on the detection of the vessel behavioural changes and represented in a compact synthetic route composed of the network nodes and route segments. The outcome of the knowledge discovery process is a geographical maritime network that can be used in Maritime Situational Awareness (MSA) applications such as track reconstruction from missing information, situation/destination prediction, and detection of anomalous behaviour. Experimental results are presented, testing the algorithm in a specific scenario of interest, the Dover Strait.
 

In this paper, we consider distributed algorithm based on a continuous-time multi-agent system to solve constrained optimization problem. The global optimization objective function is taken as the sum of agents' individual objective functions under a group of convex inequality function constraints. Because the local objective functions cannot be explicitly known by all the agents, the problem has to be solved in a distributed manner with the cooperation between agents. Here we propose a continuous-time distributed gradient dynamics based on the KKT condition and Lagrangian multiplier methods to solve the optimization problem. We show that all the agents asymptotically converge to the same optimal solution with the help of a constructed Lyapunov function and a LaSalle invariance principle of hybrid systems.

Internet into our physical world and making it present everywhere. This evolution is also raising challenges in issues such as privacy, and security. For that reason, this work is focused on the integration and lightweight adaptation of existing authentication protocols, which are able also to offer authorization and access control functionalities. In particular, this work is focused on the Extensible Authentication Protocol (EAP). EAP is widely used protocol for access control in local area networks such Wireless (802.11) and wired (802.3). This work presents an integration of the EAP frame into IEEE 802.15.4 frames, demonstrating that EAP protocol and some of its mechanisms are feasible to be applied in constrained devices, such as the devices that are populating the IoT networks.
 

In this paper we propose a framework for automating feedback control to balance hard-to-predict wind power variations. The power imbalance is a result of non-zero mean error around the wind power forecast. Our proposed framework is aimed at achieving the objective of frequency stabilization and regulation through one control action. A case-study for a real-world system on Flores island in Portugal is provided. Using a battery-based storage on the island, we illustrate the proposed control framework.
 

Visual cryptography is a way to encrypt the secret image into several meaningless share images. Noted that no information can be obtained if not all of the shares are collected. Stacking the share images, the secret image can be retrieved. The share images are meaningless to owner which results in difficult to manage. Tagged visual cryptography is a skill to print a pattern onto meaningless share images. After that, users can easily manage their own share images according to the printed pattern. Besides, access control is another popular topic to allow a user or a group to see the own authorizations. In this paper, a self-authentication mechanism with lossless construction ability for image secret sharing scheme is proposed. The experiments provide the positive data to show the feasibility of the proposed scheme.
 

Being the most important critical infrastructure in Cyber-Physical Systems (CPSs), a smart grid exhibits the complicated nature of large scale, distributed, and dynamic environment. Taxonomy of attacks is an effective tool in systematically classifying attacks and it has been placed as a top research topic in CPS by a National Science Foundation (NSG) Workshop. Most existing taxonomy of attacks in CPS are inadequate in addressing the tight coupling of cyber-physical process or/and lack systematical construction. This paper attempts to introduce taxonomy of attacks of agent-based smart grids as an effective tool to provide a structured framework. The proposed idea of introducing the structure of space-time and information flow direction, security feature, and cyber-physical causality is innovative, and it can establish a taxonomy design mechanism that can systematically construct the taxonomy of cyber attacks, which could have a potential impact on the normal operation of the agent-based smart grids. Based on the cyber-physical relationship revealed in the taxonomy, a concrete physical process based cyber attack detection scheme has been proposed. A numerical illustrative example has been provided to validate the proposed physical process based cyber detection scheme.
 

To allow fine-grained access control of sensitive data, researchers have proposed various types of functional encryption schemes, such as identity-based encryption, searchable encryption and attribute-based encryption. We observe that it is difficult to define some complex access policies in certain application scenarios by using these schemes individually. In this paper, we attempt to address this problem by proposing a functional encryption approach named Key-Policy Attribute-Based Encryption with Attribute Extension (KP-ABE-AE). In this approach, we utilize extended attributes to integrate various encryption schemes that support different access policies under a common top-level KP-ABE scheme, thus expanding the scope of access policies that can be defined. Theoretical analysis and experimental studies are conducted to demonstrate the applicability of the proposed KP-ABE-AE. We also present an optimization for a special application of KP-ABE-AE where IPE schemes are integrated with a KP-ABE scheme. The optimization results in an integrated scheme with better efficiency when compared to the existing encryption schemes that support the same scope of access policies.

We present an analysis of a heuristic for abrupt change detection of systems with bounded state variations. The proposed analysis is based on the Singular Value Decomposition (SVD) of a history matrix built from system observations. We show that monitoring the largest singular value of the history matrix can be used as a heuristic for detecting abrupt changes in the system outputs. We provide sufficient detectability conditions for the proposed heuristic. As an application, we consider detecting malicious cyber data attacks on power systems and test our proposed heuristic on the IEEE 39-bus testbed.
 

We introduce a cloud-enabled defense mechanism for Internet services against network and computational Distributed Denial-of-Service (DDoS) attacks. Our approach performs selective server replication and intelligent client re-assignment, turning victim servers into moving targets for attack isolation. We introduce a novel system architecture that leverages a "shuffling" mechanism to compute the optimal re-assignment strategy for clients on attacked servers, effectively separating benign clients from even sophisticated adversaries that persistently follow the moving targets. We introduce a family of algorithms to optimize the runtime client-to-server re-assignment plans and minimize the number of shuffles to achieve attack mitigation. The proposed shuffling-based moving target mechanism enables effective attack containment using fewer resources than attack dilution strategies using pure server expansion. Our simulations and proof-of-concept prototype using Amazon EC2 [1] demonstrate that we can successfully mitigate large-scale DDoS attacks in a small number of shuffles, each of which incurs a few seconds of user-perceived latency.
 

Advanced Metering Infrastructure (AMI) is the core component in a smart grid that exhibits a highly complex network configuration. AMI shares information about consumption, outages, and electricity rates reliably and efficiently by bidirectional communication between smart meters and utilities. However, the numerous smart meters being connected through mesh networks open new opportunities for attackers to interfere with communications and compromise utilities assets or steal customers private information. In this paper, we present a new DoS attack, called puppet attack, which can result in denial of service in AMI network. The intruder can select any normal node as a puppet node and send attack packets to this puppet node. When the puppet node receives these attack packets, this node will be controlled by the attacker and flood more packets so as to exhaust the network communication bandwidth and node energy. Simulation results show that puppet attack is a serious and packet deliver rate goes down to 20%-10%.

Monitoring is an important issue in cloud environments because it assures that acquired cloud slices attend the user's expectations. However, these environments are multitenant and dynamic, requiring automation techniques to offload cloud administrators. In a previous work, we proposed FlexACMS: a framework to automate monitoring configuration related to cloud slices using multiple monitoring solutions. In this work, we enhanced FlexACMS to allow dynamic and automatic attribution of monitoring configuration tasks to servers without administrator intervention, which was not available in previous version. FlexACMS also considers the monitoring server load when attributing configuration tasks, which allows load balancing between monitoring servers. The evaluation showed that enhancements reduced FlexACMS response time up to 60% in comparison to previous version. The scalability evaluation of enhanced version demonstrated the feasibility of our approach in large scale cloud environments.
 

Multiple string matching plays a fundamental role in network intrusion detection systems. Automata-based multiple string matching algorithms like AC, SBDM and SBOM are widely used in practice, but the huge memory usage of automata prevents them from being applied to a large-scale pattern set. Meanwhile, poor cache locality of huge automata degrades the matching speed of algorithms. Here we propose a space-efficient multiple string matching algorithm BVM, which makes use of bit-vector and succinct hash table to replace the automata used in factor-searching-based algorithms. Space complexity of the proposed algorithm is O(rm2 + ΣpϵP |p|), that is more space-efficient than the classic automata-based algorithms. Experiments on datasets including Snort, ClamAV, URL blacklist and synthetic rules show that the proposed algorithm significantly reduces memory usage and still runs at a fast matching speed. Above all, BVM costs less than 0.75% of the memory usage of AC, and is capable of matching millions of patterns efficiently.

Multiple string matching plays a fundamental role in network intrusion detection systems. Automata-based multiple string matching algorithms like AC, SBDM and SBOM are widely used in practice, but the huge memory usage of automata prevents them from being applied to a large-scale pattern set. Meanwhile, poor cache locality of huge automata degrades the matching speed of algorithms. Here we propose a space-efficient multiple string matching algorithm BVM, which makes use of bit-vector and succinct hash table to replace the automata used in factor-searching-based algorithms. Space complexity of the proposed algorithm is O(rm2 + ΣpϵP |p|), that is more space-efficient than the classic automata-based algorithms. Experiments on datasets including Snort, ClamAV, URL blacklist and synthetic rules show that the proposed algorithm significantly reduces memory usage and still runs at a fast matching speed. Above all, BVM costs less than 0.75% of the memory usage of AC, and is capable of matching millions of patterns efficiently.

We survey the state-of-the-art on the Internet-of-Things (IoT) from a wireless communications point of view, as a result of the European FP7 project BUTLER which has its focus on pervasiveness, context-awareness and security for IoT. In particular, we describe the efforts to develop so-called (wireless) enabling technologies, aimed at circumventing the many challenges involved in extending the current set of domains (“verticals”) of IoT applications towards a “horizontal” (i.e. integrated) vision of the IoT. We start by illustrating current research effort in machine-to-machine (M2M), which is mainly focused on vertical domains, and we discuss some of them in details, depicting then the necessary horizontal vision for the future intelligent daily routine (“Smart Life”). We then describe the technical features of the most relevant heterogeneous communications technologies on which the IoT relies, under the light of the on-going M2M service layer standardization. Finally we identify and present the key aspects, within three major cross-vertical categories, under which M2M technologies can function as enablers for the horizontal vision of the IoT.

This paper proposes a modified empirical-mode decomposition (EMD) filtering-based adaptive dynamic phasor estimation algorithm for the removal of exponentially decaying dc offset. Discrete Fourier transform does not have the ability to attain the accurate phasor of the fundamental frequency component in digital protective relays under dynamic system fault conditions because the characteristic of exponentially decaying dc offset is not consistent. EMD is a fully data-driven, not model-based, adaptive filtering procedure for extracting signal components. But the original EMD technique has high computational complexity and requires a large data series. In this paper, a short data series-based EMD filtering procedure is proposed and an optimum hermite polynomial fitting (OHPF) method is used in this modified procedure. The proposed filtering technique has high accuracy and convergent speed, and is greatly appropriate for relay applications. This paper illustrates the characteristics of the proposed technique and evaluates its performance by computer-simulated signals, PSCAD/EMTDC-generated signals, and real power system fault signals.

The increased interconnectivity and complexity of supervisory control and data acquisition (SCADA) systems in power system networks has exposed the systems to a multitude of potential vulnerabilities. In this paper, we present a novel approach for a next-generation SCADA-specific intrusion detection system (IDS). The proposed system analyzes multiple attributes in order to provide a comprehensive solution that is able to mitigate varied cyber-attack threats. The multiattribute IDS comprises a heterogeneous white list and behavior-based concept in order to make SCADA cybersystems more secure. This paper also proposes a multilayer cyber-security framework based on IDS for protecting SCADA cybersecurity in smart grids without compromising the availability of normal data. In addition, this paper presents a SCADA-specific cybersecurity testbed to investigate simulated attacks, which has been used in this paper to validate the proposed approach.

Software-Defined Networking (SDN) allows network capabilities and services to be managed through a central control point. Moving Target Defense (MTD) on the other hand, introduces a constantly adapting environment in order to delay or prevent attacks on a system. MTD is a use case where SDN can be leveraged in order to provide attack surface obfuscation. In this paper, we investigate how SDN can be used in some network-based MTD techniques. We first describe the advantages and disadvantages of these techniques, the potential countermeasures attackers could take to circumvent them, and the overhead of implementing MTD using SDN. Subsequently, we study the performance of the SDN-based MTD methods using Cisco's One Platform Kit and we show that they significantly increase the attacker's overheads.

Wireless channel reciprocity can be successfully exploited as a common source of randomness for the generation of a secret key by two legitimate users willing to achieve confidential communications over a public channel. This paper presents an analytical framework to investigate the theoretical limits of secret-key generation when wireless multi-dimensional Gaussian channels are used as source of randomness. The intrinsic secrecy content of wide-sense stationary wireless channels in frequency, time and spatial domains is derived through asymptotic analysis as the number of observations in a given domain tends to infinity. Some significant case studies are presented where single and multiple antenna eavesdroppers are considered. In the numerical results, the role of signal-to-noise ratio, spatial correlation, frequency and time selectivity is investigated.