Biblio
Radio-Frequency Identification (RFID) tags have been widely used as a low-cost wireless method for detection of counterfeit product injection in supply chains. In order to adequately perform authentication, current RFID monitoring schemes need to either have a persistent online connection between supply chain partners and the back-end database or have a local database on each partner site. A persistent online connection is not guaranteed and local databases on each partner site impose extra cost and security issues. We solve this problem by introducing a new scheme in which a small Non-Volatile Memory (NVM) embedded in RFID tag is used to function as a tiny “encoded local database”. In addition our scheme resists “tag tracing” so that each partner's operation remains private. Our scheme can be implemented in less than 1200 gates satisfying current RFID technology requirements.
Ideally, minimizing the flow completion time (FCT) requires millions of priorities supported by the underlying network so that each flow has its unique priority. However, in production datacenters, the available switch priority queues for flow scheduling are very limited (merely 2 or 3). This practical constraint seriously degrades the performance of previous approaches. In this paper, we introduce Explicit Priority Notification (EPN), a novel scheduling mechanism which emulates fine-grained priorities (i.e., desired priorities or DP) using only two switch priority queues. EPN can support various flow scheduling disciplines with or without flow size information. We have implemented EPN on commodity switches and evaluated its performance with both testbed experiments and extensive simulations. Our results show that, with flow size information, EPN achieves comparable FCT as pFabric that requires clean-slate switch hardware. And EPN also outperforms TCP by up to 60.5% if it bins the traffic into two priority queues according to flow size. In information-agnostic setting, EPN outperforms PIAS with two priority queues by up to 37.7%. To the best of our knowledge, EPN is the first system that provides millions of priorities for flow scheduling with commodity switches.
Cryptography is the fascinating science that deals with constructing and destructing the secret codes. The evolving digitization in this modern era possesses cryptography as one of its backbones to perform the transactions with confidentiality and security wherever the authentication is required. With the modern technology that has evolved, the use of codes has exploded, enriching cryptology and empowering citizens. One of the most important things that encryption provides anyone using any kind of computing device is `privacy'. There is no way to have true privacy with strong security, the method with which we are dealing with is to make the cipher text more robust to be by-passed. In current work, the well known and renowned Caesar cipher and Rail fence cipher techniques are combined with a cross language cipher technique and the detailed comparative analysis amongst them is carried out. The simulations have been carried out on Eclipse Juno version IDE for executions and Java, an open source language has been used to implement these said techniques.
Software-defined networks offer a promising framework for the implementation of cross-layer data-centric security policies in military systems. An important aspect of the design process for such advanced security solutions is the thorough experimental assessment and validation of proposed technical concepts prior to their deployment in operational military systems. In this paper, we describe an OpenFlow-based testbed, which was developed with a specific focus on validation of SDN security mechanisms - including both the mechanisms for protecting the software-defined network layer and the cross-layer enforcement of higher level policies, such as data-centric security policies. We also present initial experimentation results obtained using the testbed, which confirm its ability to validate simulation and analytic predictions. Our objective is to provide a sufficiently detailed description of the configuration used in our testbed so that it can be easily re-plicated and re-used by other security researchers in their experiments.
The Internet of things (IoT) is revolutionizing the management and control of automated systems leading to a paradigm shift in areas such as smart homes, smart cities, health care, transportation, etc. The IoT technology is also envisioned to play an important role in improving the effectiveness of military operations in battlefields. The interconnection of combat equipment and other battlefield resources for coordinated automated decisions is referred to as the Internet of battlefield things (IoBT). IoBT networks are significantly different from traditional IoT networks due to the battlefield specific challenges such as the absence of communication infrastructure, and the susceptibility of devices to cyber and physical attacks. The combat efficiency and coordinated decision-making in war scenarios depends highly on real-time data collection, which in turn relies on the connectivity of the network and the information dissemination in the presence of adversaries. This work aims to build the theoretical foundations of designing secure and reconfigurable IoBT networks. Leveraging the theories of stochastic geometry and mathematical epidemiology, we develop an integrated framework to study the communication of mission-critical data among different types of network devices and consequently design the network in a cost effective manner.
Cyber-Physical Systems (CPS) represent a fundamental link between information technology (IT) systems and the devices that control industrial production and maintain critical infrastructure services that support our modern world. Increasingly, the interconnections among CPS and IT systems have created exploitable security vulnerabilities due to a number of factors, including a legacy of weak information security applications on CPS and the tendency of CPS operators to prioritize operational availability at the expense of integrity and confidentiality. As a result, CPS are subject to a number of threats from cyber attackers and cyber-physical attackers, including denial of service and even attacks against the integrity of the data in the system. The effects of these attacks extend beyond mere loss of data or the inability to access information system services. Attacks against CPS can cause physical damage in the real world. This paper reviews the challenges of providing information assurance services for CPS that operate critical infrastructure systems and industrial control systems. These methods are thorough measures to close integrity and confidentiality gaps in CPS and processes to highlight the security risks that remain. This paper also outlines approaches to reduce the overhead and complexity for security methods, as well as examine novel approaches, including covert communications channels, to increase CPS security.
Supervisory control and data acquisition (SCADA) systems are the key driver for critical infrastructures and industrial facilities. Cyber-attacks to SCADA networks may cause equipment damage or even fatalities. Identifying risks in SCADA networks is critical to ensuring the normal operation of these industrial systems. In this paper we propose a Bayesian network-based cyber-security risk assessment model to dynamically and quantitatively assess the security risk level in SCADA networks. The major distinction of our work is that the proposed risk assessment method can learn model parameters from historical data and then improve assessment accuracy by incrementally learning from online observations. Furthermore, our method is able to assess the risk caused by unknown attacks. The simulation results demonstrate that the proposed approach is effective for SCADA security risk assessment.
As a problem solving method, neural networks have shown broad applicability from medical applications, speech recognition, and natural language processing. This success has even led to implementation of neural network algorithms into hardware. In this paper, we explore two questions: (a) to what extent microelectronic variations affects the quality of results by neural networks; and (b) if the answer to first question represents an opportunity to optimize the implementation of neural network algorithms. Regarding first question, variations are now increasingly common in aggressive process nodes and typically manifest as an increased frequency of timing errors. Combating variations - due to process and/or operating conditions - usually results in increased guardbands in circuit and architectural design, thus reducing the gains from process technology advances. Given the inherent resilience of neural networks due to adaptation of their learning parameters, one would expect the quality of results produced by neural networks to be relatively insensitive to the rising timing error rates caused by increased variations. On the contrary, using two frequently used neural networks (MLP and CNN), our results show that variations can significantly affect the inference accuracy. This paper outlines our assessment methodology and use of a cross-layer evaluation approach that extracts hardware-level errors from twenty different operating conditions and then inject such errors back to the software layer in an attempt to answer the second question posed above.
Wearable medical devices are playing more and more important roles in healthcare. Unlike the wired connection, the wireless connection between wearable devices and the remote servers are exceptionally vulnerable to malicious attacks, and poses threats to the safety and privacy of the patient health data. Therefore, wearable medical devices require the implementation of reliable measures to secure the wireless network communication. However, those devices usually have limited computational power that is not comparable with the desktop computer and thus, it is difficult to adopt the full-fledged security algorithm in software. In this study, we have developed an efficient authentication and encryption protocol for internetconnected wearable devices using the recognized standards of AES and SHA that can provide two-way authentication between wearable device and remote server and protection of patient privacy against various network threats. We have tested the feasibility of this protocol on the TI CC3200 Launchpad, an evaluation board of the CC3200, which is a Wi-Fi capable microcontroller designed for wearable devices and includes a hardware accelerated cryptography module for the implementation of the encryption algorithm. The microcontroller serves as the wearable device client and a Linux computer serves as the server. The embedded client software was written in ANSI C and the server software was written in Python.
Surveillance video systems are gaining increasing attention in the field of computer vision due to its demands of users for the seek of security. It is promising to observe the human movement and predict such kind of sense of movements. The need arises to develop a surveillance system that capable to overcome the shortcoming of depending on the human resource to stay monitoring, observing the normal and suspect event all the time without any absent mind and to facilitate the control of huge surveillance system network. In this paper, an intelligent human activity system recognition is developed. Series of digital image processing techniques were used in each stage of the proposed system, such as background subtraction, binarization, and morphological operation. A robust neural network was built based on the human activities features database, which was extracted from the frame sequences. Multi-layer feed forward perceptron network used to classify the activities model in the dataset. The classification results show a high performance in all of the stages of training, testing and validation. Finally, these results lead to achieving a promising performance in the activity recognition rate.
The previous consideration of power grid focuses on the power system itself, however, the recent work is aiming at both power grid and communication network, this coupling networks are firstly called as interdependent networks. Prior study on modeling interdependent networks always extracts main features from real networks, the model of network A and network B are completely symmetrical, both degree distribution in intranetwork and support pattern in inter-network, but in reality this circumstance is hard to attain. In this paper, we deliberately set both networks with same topology in order to specialized research the support pattern between networks. In terms of initial failure from power grid or communication network, we find the remaining survival fraction is greatly disparate, and the failure initially from power grid is more harmful than failure initially from communication network, which all show the vulnerability of interdependency and meantime guide us to pay more attention to the protection measures for power grid.
The Air Force is shifting its cybersecurity paradigm from an information technology (IT)-centric toward a mission oriented approach. Instead of focusing on how to defend its IT infrastructure, it seeks to provide mission assurance by defending mission relevant cyber terrain enabling mission execution in a contested environment. In order to actively defend a mission in cyberspace, efforts must be taken to understand and document that mission's dependence on cyberspace and cyber assets. This is known as cyber terrain mission mapping. This paper seeks to define mission mapping and overview methodologies. We also analyze current tools seeking to provide cyber situational awareness through mission mapping or cyber dependency impact analysis and identify existing shortfalls.
Mixed-Criticality Systems (MCS) are real-time systems characterized by two or more distinct levels of criticality. In MCS, it is imperative that high-critical flows meet their deadlines while low critical flows can tolerate some delays. Sharing resources between flows in Network-On-Chip (NoC) can lead to different unpredictable latencies and subsequently complicate the implementation of MCS in many-core architectures. This paper proposes a new virtual channel router designed for MCS deployed over NoCs. The first objective of this router is to reduce the worst-case communication latency of high-critical flows. The second aim is to improve the network use rate and reduce the communication latency for low-critical flows. The proposed router, called DAS (Double Arbiter and Switching router), jointly uses Wormhole and Store And Forward techniques for low and high-critical flows respectively. Simulations with a cycle-accurate SystemC NoC simulator show that, with a 15% network use rate, the communication delay of high-critical flows is reduced by 80% while communication delay of low-critical flow is increased by 18% compared to usual solutions based on routers with multiple virtual channels.
With the advent of smart devices and lowering prices of sensing devices, adoption of Internet of Things (IoT) is gaining momentum. These IoT devices come with greater threat of being attacked or compromised that could lead to Denial of Service (DoS) and Distributed Denial of Service (DDoS). The high volume of IoT devices with high level of heterogeneity, magnify the possibility of security threats. So far, there is no protocol to guarantee the security of IoT devices. But to enable resilience, continuous monitoring is required along with adaptive decision making. These challenges can be addressed with the help of Software Defined Networking (SDN) which can effectively handle the security threats to the IoT devices in dynamic and adaptive manner without any burden on the IoT devices. In this paper, we propose an SDN-based secure IoT framework called SoftThings to detect abnormal behaviors and attacks as early as possible and mitigate as appropriate. Machine Learning is used at the SDN controller to monitor and learn the behavior of IoT devices over time. We have conducted experiments on Mininet emulator. Initial results show that this framework is capable to detect attacks on IoT with around 98% precision.
With the development of the information and communications technology, new network architecture and applications keep emerging promoted by cloud computing, big data, virtualization technology, etc. As a novel network architecture, Software Defined Network (SDN) realizes separation of the control plane and the data plane, thus controlling hardware by a software platform which is known as the central controller. Through that method SDN realizes the flexible deployment of network resources. In the process of the development and application of SDN, its open architecture has exposed more and more security problem, which triggers a critical focus on how to build a secure SDN. Based on the hierarchical SDN architecture and characteristics, this paper analyzes the security threats that SDN may face in the application layer, the control layer, the resource layer and the interface layer. In order to solve those security threats, the paper presents an SDN security architecture which can provide corresponding defense ability. The paper also puts forward an enhanced access control strategy adopting an attribute-based encryption method in the SDN security architecture.
The majority of business activity of our integrated and connected world takes place in networks based on cloud computing infrastructure that cross national, geographic and jurisdictional boundaries. Such an efficient entity interconnection is made possible through an emerging networking paradigm, Software Defined Networking (SDN) that intends to vastly simplify policy enforcement and network reconfiguration in a dynamic manner. However, despite the obvious advantages this novel networking paradigm introduces, its increased attack surface compared to traditional networking deployments proved to be a thorny issue that creates skepticism when safety-critical applications are considered. Especially when SDN is used to support Internet-of-Things (IoT)-related networking elements, additional security concerns rise, due to the elevated vulnerability of such deployments to specific types of attacks and the necessity of inter-cloud communication any IoT application would require. The overall number of connected nodes makes the efficient monitoring of all entities a real challenge, that must be tackled to prevent system degradation and service outage. This position paper provides an overview of common security issues of SDN when linked to IoT clouds, describes the design principals of the recently introduced Blockchain paradigm and advocates the reasons that render Blockchain as a significant security factor for solutions where SDN and IoT are involved.
The majority of business activity of our integrated and connected world takes place in networks based on cloud computing infrastructure that cross national, geographic and jurisdictional boundaries. Such an efficient entity interconnection is made possible through an emerging networking paradigm, Software Defined Networking (SDN) that intends to vastly simplify policy enforcement and network reconfiguration in a dynamic manner. However, despite the obvious advantages this novel networking paradigm introduces, its increased attack surface compared to traditional networking deployments proved to be a thorny issue that creates skepticism when safety-critical applications are considered. Especially when SDN is used to support Internet-of-Things (IoT)-related networking elements, additional security concerns rise, due to the elevated vulnerability of such deployments to specific types of attacks and the necessity of inter-cloud communication any IoT application would require. The overall number of connected nodes makes the efficient monitoring of all entities a real challenge, that must be tackled to prevent system degradation and service outage. This position paper provides an overview of common security issues of SDN when linked to IoT clouds, describes the design principals of the recently introduced Blockchain paradigm and advocates the reasons that render Blockchain as a significant security factor for solutions where SDN and IoT are involved.
Machine learning and data mining algorithms typically assume that the training and testing data are sampled from the same fixed probability distribution; however, this violation is often violated in practice. The field of domain adaptation addresses the situation where this assumption of a fixed probability between the two domains is violated; however, the difference between the two domains (training/source and testing/target) may not be known a priori. There has been a recent thrust in addressing the problem of learning in the presence of an adversary, which we formulate as a problem of domain adaption to build a more robust classifier. This is because the overall security of classifiers and their preprocessing stages have been called into question with the recent findings of adversaries in a learning setting. Adversarial training (and testing) data pose a serious threat to scenarios where an attacker has the opportunity to ``poison'' the training or ``evade'' on the testing data set(s) in order to achieve something that is not in the best interest of the classifier. Recent work has begun to show the impact of adversarial data on several classifiers; however, the impact of the adversary on aspects related to preprocessing of data (i.e., dimensionality reduction or feature selection) has widely been ignored in the revamp of adversarial learning research. Furthermore, variable selection, which is a vital component to any data analysis, has been shown to be particularly susceptible under an attacker that has knowledge of the task. In this work, we explore avenues for learning resilient classification models in the adversarial learning setting by considering the effects of adversarial data and how to mitigate its effects through optimization. Our model forms a single convex optimization problem that uses the labeled training data from the source domain and known- weaknesses of the model for an adversarial component. We benchmark the proposed approach on synthetic data and show the trade-off between classification accuracy and skew-insensitive statistics.
The most of the organizations tend to accumulate the data related to security, which goes up-to terabytes in every month. They collect this data to meet the security requirements. The data is mostly in the shape of logs like Dns logs, Pcap files, and Firewall data etc. The data can be related to any communication network like cloud, telecom, or smart grid network. Generally, these logs are stored in databases or warehouses which becomes ultimately gigantic in size. Such a huge size of data upsurge the importance of security analytics in big data. In surveys, the security experts grumble about the existing tools and recommend for special tools and methods for big data security analysis. In this paper, we are using a big data analysis tool, which is known as apache spark. Although this tool is used for general purpose but we have used this for security analysis. It offers a very good library for machine learning algorithms including the clustering which is the main algorithm used in our work. In this work, we have developed a novel model, which combines rule based and clustering analysis for security analysis of big dataset. The dataset we are using in our experiment is the Kddcup99 which is a widely used dataset for intrusion detection. It is of MBs in size but can be used as a test case for big data security analysis.
Traditional information Security Risk Assessment algorithms are mainly used for evaluating small scale of information system, not suitable for massive information systems in Energy Internet. To solve the problem, this paper proposes an Information Security Risk Algorithm based on Dynamic Risk Propagation (ISRADRP). ISRADRP firstly divides information systems in the Energy Internet into different partitions according to their logical network location. Then, ISRADRP computes each partition's risk value without considering threat propagation effect via RM algorithm. Furthermore, ISRADRP calculates inside and outside propagation risk value for each partition according to Dependency Structure Matrix. Finally, the security bottleneck of systems will be identified and the overall risk value of information system will be obtained.
Code signing which at present is the only methodology of trusting a code that is distributed to others. It heavily relies on the security of the software providers private key. Attackers employ targeted attacks on the code signing infrastructure for stealing the signing keys which are used later for distributing malware in disguise of genuine software. Differentiating a malware from a benign software becomes extremely difficult once it gets signed by a trusted software providers private key as the operating systems implicitly trusts this signed code. In this paper, we analyze the growing menace of signed malware by examining several real world incidents and present a threat model for the current code signing infrastructure. We also propose a novel solution that prevents this issue of malicious code signing by requiring additional verification of the executable. We also present the serious threat it poses and it consequences. To our knowledge this is the first time this specific issue of Malicious code signing has been thoroughly studied and an implementable solution is proposed.
Anti-virus vendors receive hundreds of thousands of malware to be analysed each day. Some are new malware while others are variations or evolutions of existing malware. Because analyzing each malware sample by hand is impossible, automated techniques to analyse and categorize incoming samples are needed. In this work, we explore various machine learning features extracted from malware samples through static analysis for classification of malware binaries into already known malware families. We present a new feature based on control statement shingling that has a comparable accuracy to ordinary opcode n-gram based features while requiring smaller dimensions. This, in turn, results in a shorter training time.