Biblio

The detectability of malicious circuitry on FPGAs with varying placement properties yet has to be investigated. The authors utilize a Xilinx Virtex-II Pro target platform in order to insert a sequential denial-of-service Trojan into an existing AES design by manipulating a Xilinx-specific, intermediate file format prior to the bitstream generation. Thereby, there is no need for an attacker to acquire access to the hardware description language representation of a potential target architecture. Using a side-channel analysis setup for electromagnetic emanation (EM) measurements, they evaluate the detectability of different Trojan designs with varying location and logic distribution properties. The authors successfully distinguish the malicious from the genuine designs and provide information on how the location and distribution properties of the Trojan logic affect its detectability. To the best of their knowledge, this has been the first practically conducted Trojan detection using localized EM measurements.
 

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.

Recently, cloud computing has been spotlighted as a new paradigm of database management system. In this environment, databases are outsourced and deployed on a service provider in order to reduce cost for data storage and maintenance. However, the service provider might be untrusted so that the two issues of data security, including data confidentiality and query result integrity, become major concerns for users. Existing bucket-based data authentication methods have problem that the original spatial data distribution can be disclosed from data authentication index due to the unsophisticated data grouping strategies. In addition, the transmission overhead of verification object is high. In this paper, we propose a privacy-aware query authentication which guarantees data confidentiality and query result integrity for users. A periodic function-based data grouping scheme is designed to privately partition a spatial database into small groups for generating a signature of each group. The group signature is used to check the correctness and completeness of outsourced data when answering a range query to users. Through performance evaluation, it is shown that proposed method outperforms the existing method in terms of range query processing time up to 3 times.

The strong development of the Internet of Things (IoT) is dramatically changing traditional perceptions of the current Internet towards an integrated vision of smart objects interacting with each other. While in recent years many technological challenges have already been solved through the extension and adaptation of wireless technologies, security and privacy still remain as the main barriers for the IoT deployment on a broad scale. In this emerging paradigm, typical scenarios manage particularly sensitive data, and any leakage of information could severely damage the privacy of users. This paper provides a concise description of some of the major challenges related to these areas that still need to be overcome in the coming years for a full acceptance of all IoT stakeholders involved. In addition, we propose a distributed capability-based access control mechanism which is built on public key cryptography in order to cope with some of these challenges. Specifically, our solution is based on the design of a lightweight token used for access to CoAP Resources, and an optimized implementation of the Elliptic Curve Digital Signature Algorithm (ECDSA) inside the smart object. The results obtained from our experiments demonstrate the feasibility of the proposal and show promising in order to cover more complex scenarios in the future, as well as its application in specific IoT use cases.

As most of the modern encryption algorithms are broken fully/partially, the world of information security looks in new directions to protect the data it transmits. The concept of using DNA computing in the fields of cryptography has been identified as a possible technology that may bring forward a new hope for hybrid and unbreakable algorithms. Currently, several DNA computing algorithms are proposed for cryptography, cryptanalysis and steganography problems, and they are proven to be very powerful in these areas. This paper gives an architectural framework for encryption & Generation of digital signature using DNA Cryptography. To analyze the performance; the original plaintext size and the key size; together with the encryption and decryption time are examined also the experiments on plaintext with different contents are performed to test the robustness of the program.

This session reports on a workshop convened by the ACM Education Board with funding by the US National Science Foundation and invites discussion from the community on the workshop findings. The topic, curricular directions for cybersecurity, is one that resonates in many departments considering how best to prepare graduates to face the challenges of security issues in employment and future research. The session will include presentation of the workshop context and conclusions, but will be open to participant discussion. This will be the first public presentation of the results of the workshop and the first opportunity for significant response.

In his book Outliers, Malcom Gladwell describes the 10,000-Hour Rule, a key to success in any field, as simply a matter of practicing a specific task that can be accomplished with 20 hours of work a week for 10 years [10]. Ongoing changes in technology and national security needs require aspiring excellent cybersecurity professionals to set a goal of 10,000 hours of relevant, hands-on skill development. The education system today is ill prepared to meet the challenge of producing an adequate number of cybersecurity professionals, but programs that use competitions and learning environments that teach depth are filling this void.

Control system networks are increasingly being connected to enterprise level networks. These connections leave critical industrial controls systems vulnerable to cyber-attacks. Most of the effort in protecting these cyber-physical systems (CPS) from attacks has been in securing the networks using information security techniques. Effort has also been applied to increasing the protection and reliability of the control system against random hardware and software failures. However, the inability of information security techniques to protect against all intrusions means that the control system must be resilient to various signal attacks for which new analysis methods need to be developed. In this paper, sensor signal attacks are analyzed for observer-based controlled systems. The threat surface for sensor signal attacks is subdivided into denial of service, finite energy, and bounded attacks. In particular, the error signals between states of attack free systems and systems subject to these attacks are quantified. Optimal sensor and actuator signal attacks for the finite and infinite horizon linear quadratic (LQ) control in terms of maximizing the corresponding cost functions are computed. The closed-loop systems under optimal signal attacks are provided. Finally, an illustrative numerical example using a power generation network is provided together with distributed LQ controllers.

With the global widespread usage of the Internet, more and more cyber-attacks are being performed. Many of these attacks utilize IP address spoofing. This paper describes IP spoofing attacks and the proposed methods currently available to detect or prevent them. In addition, it presents a statistical analysis of the Hop Count parameter used in our proposed IP spoofing detection algorithm. We propose an algorithm, inspired by the Hop Count Filtering (HCF) technique, that changes the learning phase of HCF to include all the possible available Hop Count values. Compared to the original HCF method and its variants, our proposed method increases the true positive rate by at least 9% and consequently increases the overall accuracy of an intrusion detection system by at least 9%. Our proposed method performs in general better than HCF method and its variants.
 

In the cyber crime huge log data, transactional data occurs which tends to plenty of data for storage and analyze them. It is difficult for forensic investigators to play plenty of time to find out clue and analyze those data. In network forensic analysis involves network traces and detection of attacks. The trace involves an Intrusion Detection System and firewall logs, logs generated by network services and applications, packet captures by sniffers. In network lots of data is generated in every event of action, so it is difficult for forensic investigators to find out clue and analyzing those data. In network forensics is deals with analysis, monitoring, capturing, recording, and analysis of network traffic for detecting intrusions and investigating them. This paper focuses on data collection from the cyber system and web browser. The FTK 4.0 is discussing for memory forensic analysis and remote system forensic which is to be used as evidence for aiding investigation.
 

Botnets are one of the most destructive threats against the cyber security. Recently, HTTP protocol is frequently utilized by botnets as the Command and Communication (C&C) protocol. In this work, we aim to detect HTTP based botnet activity based on botnet behaviour analysis via machine learning approach. To achieve this, we employ flow-based network traffic utilizing NetFlow (via Softflowd). The proposed botnet analysis system is implemented by employing two different machine learning algorithms, C4.5 and Naive Bayes. Our results show that C4.5 learning algorithm based classifier obtained very promising performance on detecting HTTP based botnet activity.

Botnet is one of the most widespread and serious malware which occur frequently in today's cyber attacks. A botnet is a group of Internet-connected computer programs communicating with other similar programs in order to perform various attacks. HTTP-based botnet is most dangerous botnet among all the different botnets available today. In botnets detection, in particularly, behavioural-based approaches suffer from the unavailability of the benchmark datasets and this lead to lack of precise results evaluation of botnet detection systems, comparison, and deployment which originates from the deficiency of adequate datasets. Most of the datasets in the botnet field are from local environment and cannot be used in the large scale due to privacy problems and do not reflect common trends, and also lack some statistical features. To the best of our knowledge, there is not any benchmark dataset available which is infected by HTTP-based botnet (HBB) for performing Distributed Denial of Service (DDoS) attacks against Web servers by using HTTP-GET flooding method. In addition, there is no Web access log infected by botnet is available for researchers. Therefore, in this paper, a complete test-bed will be illustrated in order to implement a real time HTTP-based botnet for performing variety of DDoS attacks against Web servers by using HTTP-GET flooding method. In addition to this, Web access log with http bot traces are also generated. These real time datasets and Web access logs can be useful to study the behaviour of HTTP-based botnet as well as to evaluate different solutions proposed to detect HTTP-based botnet by various researchers.
 

Today, cloud networking which is the ability to connect the user with his cloud services and to interconnect these services within an inter-cloud approach, is one of the recent research areas in the cloud computing research communities. The main drawback of cloud networking consists in the lack of Quality of Service (QoS) guarantee and management in conformance with a corresponding Service Level Agreement (SLA). Several research works have been proposed for the SLA establishing in cloud computing, but not in cloud networking. In this paper, we propose an architecture for self-establishing an end-to-end service level agreement between a Cloud Service User (CSU) and a Cloud Service Provider (CSP) in a cloud networking environment. We focus on QoS parameters for NaaS and IaaS services. The architecture ensures a self-establishing of the proposed SLA using autonomic cloud managers.
 

The proliferation of digital devices in a networked industrial ecosystem, along with an exponential growth in complexity and scope, has resulted in elevated security concerns and management complexity issues. This paper describes a novel architecture utilizing concepts of autonomic computing and a simple object access protocol (SOAP)-based interface to metadata access points (IF-MAP) external communication layer to create a network security sensor. This approach simplifies integration of legacy software and supports a secure, scalable, and self-managed framework. The contribution of this paper is twofold: 1) A flexible two-level communication layer based on autonomic computing and service oriented architecture is detailed and 2) three complementary modules that dynamically reconfigure in response to a changing environment are presented. One module utilizes clustering and fuzzy logic to monitor traffic for abnormal behavior. Another module passively monitors network traffic and deploys deceptive virtual network hosts. These components of the sensor system were implemented in C++ and PERL and utilize a common internal D-Bus communication mechanism. A proof of concept prototype was deployed on a mixed-use test network showing the possible real-world applicability. In testing, 45 of the 46 network attached devices were recognized and 10 of the 12 emulated devices were created with specific operating system and port configurations. In addition, the anomaly detection algorithm achieved a 99.9% recognition rate. All output from the modules were correctly distributed using the common communication structure.

This brief presents a methodology to develop recursive filters in reproducing kernel Hilbert spaces. Unlike previous approaches that exploit the kernel trick on filtered and then mapped samples, we explicitly define the model recursivity in the Hilbert space. For that, we exploit some properties of functional analysis and recursive computation of dot products without the need of preimaging or a training dataset. We illustrate the feasibility of the methodology in the particular case of the γ-filter, which is an infinite impulse response filter with controlled stability and memory depth. Different algorithmic formulations emerge from the signal model. Experiments in chaotic and electroencephalographic time series prediction, complex nonlinear system identification, and adaptive antenna array processing demonstrate the potential of the approach for scenarios where recursivity and nonlinearity have to be readily combined.

We propose an optical security method for object authentication using photon-counting encryption implemented with phase encoded QR codes. By combining the full phase double-random-phase encryption with photon-counting imaging method and applying an iterative Huffman coding technique, we are able to encrypt and compress an image containing primary information about the object. This data can then be stored inside of an optically phase encoded QR code for robust read out, decryption, and authentication. The optically encoded QR code is verified by examining the speckle signature of the optical masks using statistical analysis. Optical experimental results are presented to demonstrate the performance of the system. In addition, experiments with a commercial Smartphone to read the optically encoded QR code are presented. To the best of our knowledge, this is the first report on integrating photon-counting security with optically phase encoded QR codes.
 

Mobile Ad-Hoc Networks (MANET) consist of peer-to-peer infrastructure less communicating nodes that are highly dynamic. As a result, routing data becomes more challenging. Ultimately routing protocols for such networks face the challenges of random topology change, nature of the link (symmetric or asymmetric) and power requirement during data transmission. Under such circumstances both, proactive as well as reactive routing are usually inefficient. We consider, zone routing protocol (ZRP) that adds the qualities of the proactive (IARP) and reactive (IERP) protocols. In ZRP, an updated topological map of zone centered on each node, is maintained. Immediate routes are available inside each zone. In order to communicate outside a zone, a route discovery mechanism is employed. The local routing information of the zones helps in this route discovery procedure. In MANET security is always an issue. It is possible that a node can turn malicious and hamper the normal flow of packets in the MANET. In order to overcome such issue we have used a clustering technique to separate the nodes having intrusive behavior from normal behavior. We call this technique as effective k-means clustering which has been motivated from k-means. We propose to implement Intrusion Detection System on each node of the MANET which is using ZRP for packet flow. Then we will use effective k-means to separate the malicious nodes from the network. Thus, our Ad-Hoc network will be free from any malicious activity and normal flow of packets will be possible.

Wireless Sensor Networks (WSNs) are used in many applications in military, environmental, and health-related areas. These applications often include the monitoring of sensitive information such as enemy movement on the battlefield or the location of personnel in a building. Security is important in WSNs. However, WSNs suffer from many constraints, including low computation capability, small memory, limited energy resources, susceptibility to physical capture, and the use of insecure wireless communication channels. These constraints make security in WSNs a challenge. In this paper, we try to explore security issue in WSN. First, the constraints, security requirements and attacks with their corresponding countermeasures in WSNs are explained. Individual sensor nodes are subject to compromised security. An adversary can inject false reports into the networks via compromised nodes. Furthermore, an adversary can create a Gray hole by compromised nodes. If these two kinds of attacks occur simultaneously in a network, some of the existing methods fail to defend against those attacks. The Ad-hoc On Demand Distance (AODV) Vector scheme for detecting Gray-Hole attack and Statistical En-Route Filtering is used for detecting false report. For increasing security level, the Elliptic Curve Cryptography (ECC) algorithm is used. Simulations results obtain so far reduces energy consumption and also provide greater network security to some extent.

Mobile ad-hoc networks are a new field in networking because it works as an autonomous network. Application of mobile ad-hoc networks are increasing day by day in recent year now a days. So it important is increasing to provide suitable routing protocol and security from attacker. Mobile ad-hoc network now a days faces many problems such as small bandwidth, energy, security, limited computational and high mobility. The main problem in mobile ad-hoc networks is that wireless networks, Infrastructure wireless networks have larger bandwidth, larger memory, power backup and different routing protocol easily applies. But in case of mobile ad-hoc networks some of these application failed due to mobility and small power backup so it is required such type of routing protocol which is take small energy during the transfer of packet. So we see that still there are many challenging works in mobile ad-hoc networks remained and to research in this area related to routing protocol, security issues, solving energy problem and many more which is feasible to it. Our research most probably will be dedicated to Authentication in mobile ad-hoc network.

The distinctive features of mobile ad hoc networks (MANETs), including dynamic topology and open wireless medium, may lead to MANETs suffering from many security vulnerabilities. In this paper, using recent advances in uncertain reasoning that originated from the artificial intelligence community, we propose a unified trust management scheme that enhances the security in MANETs. In the proposed trust management scheme, the trust model has two components: trust from direct observation and trust from indirect observation. With direct observation from an observer node, the trust value is derived using Bayesian inference, which is a type of uncertain reasoning when the full probability model can be defined. On the other hand, with indirect observation, which is also called secondhand information that is obtained from neighbor nodes of the observer node, the trust value is derived using the Dempster-Shafer theory (DST), which is another type of uncertain reasoning when the proposition of interest can be derived by an indirect method. By combining these two components in the trust model, we can obtain more accurate trust values of the observed nodes in MANETs. We then evaluate our scheme under the scenario of MANET routing. Extensive simulation results show the effectiveness of the proposed scheme. Specifically, throughput and packet delivery ratio (PDR) can be improved significantly with slightly increased average end-to-end delay and overhead of messages.

Game theory can provide a useful tool to study the security problem in mobile ad hoc networks (MANETs). Most of existing works on applying game theories to security only consider two players in the security game model: an attacker and a defender. While this assumption may be valid for a network with centralized administration, it is not realistic in MANETs, where centralized administration is not available. In this paper, using recent advances in mean field game theory, we propose a novel game theoretic approach with multiple players for security in MANETs. The mean field game theory provides a powerful mathematical tool for problems with a large number of players. The proposed scheme can enable an individual node in MANETs to make strategic security defence decisions without centralized administration. In addition, since security defence mechanisms consume precious system resources (e.g., energy), the proposed scheme considers not only the security requirement of MANETs but also the system resources. Moreover, each node in the proposed scheme only needs to know its own state information and the aggregate effect of the other nodes in the MANET. Therefore, the proposed scheme is a fully distributed scheme. Simulation results are presented to illustrate the effectiveness of the proposed scheme.

Interactive visualization provides valuable support for exploring, analyzing, and understanding textual documents. Certain tasks, however, require that insights derived from visual abstractions are verified by a human expert perusing the source text. So far, this problem is typically solved by offering overview-detail techniques, which present different views with different levels of abstractions. This often leads to problems with visual continuity. Focus-context techniques, on the other hand, succeed in accentuating interesting subsections of large text documents but are normally not suited for integrating visual abstractions. With VarifocalReader we present a technique that helps to solve some of these approaches' problems by combining characteristics from both. In particular, our method simplifies working with large and potentially complex text documents by simultaneously offering abstract representations of varying detail, based on the inherent structure of the document, and access to the text itself. In addition, VarifocalReader supports intra-document exploration through advanced navigation concepts and facilitates visual analysis tasks. The approach enables users to apply machine learning techniques and search mechanisms as well as to assess and adapt these techniques. This helps to extract entities, concepts and other artifacts from texts. In combination with the automatic generation of intermediate text levels through topic segmentation for thematic orientation, users can test hypotheses or develop interesting new research questions. To illustrate the advantages of our approach, we provide usage examples from literature studies.

Formal methods, models and tools for social big data analytics are largely limited to graph theoretical approaches such as social network analysis (SNA) informed by relational sociology. There are no other unified modeling approaches to social big data that integrate the conceptual, formal and software realms. In this paper, we first present and discuss a theory and conceptual model of social data. Second, we outline a formal model based on set theory and discuss the semantics of the formal model with a real-world social data example from Facebook. Third, we briefly present and discuss the Social Data Analytics Tool (SODATO) that realizes the conceptual model in software and provisions social data analysis based on the conceptual and formal models. Fourth and last, based on the formal model and sentiment analysis of text, we present a method for profiling of artifacts and actors and apply this technique to the data analysis of big social data collected from Facebook page of the fast fashion company, H&M.
 

Keeping up with rapid advances in research in various fields of Engineering and Technology is a challenging task. Decision makers including academics, program managers, venture capital investors, industry leaders and funding agencies not only need to be abreast of latest developments but also be able to assess the effect of growth in certain areas on their core business. Though analyst agencies like Gartner, McKinsey etc. Provide such reports for some areas, thought leaders of all organisations still need to amass data from heterogeneous collections like research publications, analyst reports, patent applications, competitor information etc. To help them finalize their own strategies. Text mining and data analytics researchers have been looking at integrating statistics, text analytics and information visualization to aid the process of retrieval and analytics. In this paper, we present our work on automated topical analysis and insight generation from large heterogeneous text collections of publications and patents. While most of the earlier work in this area provides search-based platforms, ours is an integrated platform for search and analysis. We have presented several methods and techniques that help in analysis and better comprehension of search results. We have also presented methods for generating insights about emerging and popular trends in research along with contextual differences between academic research and patenting profiles. We also present novel techniques to present topic evolution that helps users understand how a particular area has evolved over time.
 

A distributed cyber control system comprises various types of assets, including sensors, intrusion detection systems, scanners, controllers, and actuators. The modeling and analysis of these components usually require multi-disciplinary approaches. This paper presents a modeling and dynamic analysis of a distributed cyber control system for situational awareness by taking advantage of control theory and time Petri net. Linear time-invariant systems are used to model the target system, attacks, assets influences, and an anomaly-based intrusion detection system. Time Petri nets are used to model the impact and timing relationships of attacks, vulnerability, and recovery at every node. To characterize those distributed control systems that are perfectly attackable, algebraic and topological attackability conditions are derived. Numerical evaluation is performed to determine the impact of attacks on distributed control system.