Biblio
The article issue is the enterprise information protection within the internet of things concept. The aim of research is to develop arrangements set to ensure secure enterprise IPv6 network operating. The object of research is the enterprise IPv6 network. The subject of research is modern switching equipment as a tool to ensure network protection. The research task is to prioritize functioning of switches in production and corporation enterprise networks, to develop a network host protection algorithm, to test the developed algorithm on the Cisco Packet Tracer 7 software emulator. The result of research is the proposed approach to IPv6-network security based on analysis of modern switches functionality, developed and tested enterprise network host protection algorithm under IPv6-protocol with an automated network SLAAC-configuration control, a set of arrangements for resisting default enterprise gateway attacks, using ACL, VLAN, SEND, RA Guard security technology, which allows creating sufficiently high level of networks security.
With the progressive development of network applications and software dependency, we need to discover more advanced methods for protecting our systems. Each industry is equally affected, and regardless of whether we consider the vulnerability of the government or each individual household or company, we have to find a sophisticated and secure way to defend our systems. The starting point is to create a reliable intrusion detection mechanism that will help us to identify the attack at a very early stage; otherwise in the cyber security space the intrusion can affect the system negatively, which can cause enormous consequences and damage the system's privacy, security or financial stability. This paper proposes a concise, and easy to use statistical learning procedure, abbreviated NASCA, which is a four-stage intrusion detection method that can successfully detect unwanted intrusion to our systems. The model is static, but it can be adapted to a dynamic set up.
Software Defined Networks (SDNs) is a new networking paradigm that has gained a lot of attention in recent years especially in implementing data center networks and in providing efficient security solutions. The popularity of SDN and its attractive security features suggest that it can be used in the context of smart grid systems to address many of the vulnerabilities and security problems facing such critical infrastructure systems. This paper studies the impact of different cyber attacks that can target smart grid communication network which is implemented as a software defined network on the operation of the smart grid system in general. In particular, we perform different attack scenarios including DDoS attacks, location highjacking and link overloading against SDN networks of different controller types that include POX, Floodlight and RYU. Our experiments were carried out using the mininet simulator. The experiments show that SDN-enabled smartgrid systems are vulnerable to different types of attacks.
The Named-Data Networking (NDN) has emerged as a clean-slate Internet proposal on the wave of Information-Centric Networking. Although the NDN's data-plane seems to offer many advantages, e.g., native support for multicast communications and flow balance, it also makes the network infrastructure vulnerable to a specific DDoS attack, the Interest Flooding Attack (IFA). In IFAs, a botnet issuing unsatisfiable content requests can be set up effortlessly to exhaust routers' resources and cause a severe performance drop to legitimate users. So far several countermeasures have addressed this security threat, however, their efficacy was proved by means of simplistic assumptions on the attack model. Therefore, we propose a more complete attack model and design an advanced IFA. We show the efficiency of our novel attack scheme by extensively assessing some of the state-of-the-art countermeasures. Further, we release the software to perform this attack as open source tool to help design future more robust defense mechanisms.
With the development of Software Defined Networking, its software programmability and openness brings new idea for network security. Therefore, many Software Defined Security Architectures emerged at the right moment. Software Defined Security decouples security control plane and security data plane. In Software Defined Security Architectures, underlying security devices are abstracted as security resources in resource pool, intellectualized and automated security business management and orchestration can be realized through software programming in security control plane. However, network management has been becoming extremely complicated due to expansible network scale, varying network devices, lack of abstraction and heterogeneity of network especially. Therefore, new-type open security devices are needed in SDS Architecture for unified management so that they can be conveniently abstracted as security resources in resource pool. This paper firstly analyses why open security devices are needed in SDS architecture and proposes a method of opening security devices. Considering this new architecture requires a new security scheduling mechanism, this paper proposes a security resource scheduling algorithm which is used for managing and scheduling security resources in resource pool according to user s security demand. The security resource scheduling algorithm aims to allocate a security protection task to a suitable security resource in resource pool so that improving security protection efficiency. In the algorithm, we use BP neural network to predict the execution time of security tasks to improve the performance of the algorithm. The simulation result shows that the algorithm has ideal performance. Finally, a usage scenario is given to illustrate the role of security resource scheduling in software defined security architecture.
Packet classification is a core function in network and security systems; hence, hardware-based solutions, such as packet classification accelerator chips or Ternary Content Addressable Memory (T-CAM), have been widely adopted for high-performance systems. With the rapid improvement of general hardware architectures and growing popularity of multi-core multi-threaded processors, software-based packet classification algorithms are attracting considerable attention, owing to their high flexibility in satisfying various industrial requirements for security and network systems. For high classification speed, these algorithms internally use large tables, whose size increases exponentially with the ruleset size; consequently, they cannot be used with a large rulesets. To overcome this problem, we propose a new software-based packet classification algorithm that simultaneously supports high scalability and fast classification performance by merging partition decision trees in a search table. While most partitioning-based packet classification algorithms show good scalability at the cost of low classification speed, our algorithm shows very high classification speed, irrespective of the number of rules, with small tables and short table building time. Our test results confirm that the proposed algorithm enables network and security systems to support heavy traffic in the most effective manner.
Bitcoin is the most famous cryptocurrency currently operating with a total marketcap of almost 7 billion USD. This innovation stands strong on the feature of pseudo anonymity and strives on its innovative de-centralized architecture based on the Blockchain. The Blockchain is a distributed ledger that keeps a public record of all the transactions processed on the bitcoin protocol network in full transparency without revealing the identity of the sender and the receiver. Over the course of 2016, cryptocurrencies have shown some instances of abuse by criminals in their activities due to its interesting nature. Darknet marketplaces are increasing the volume of their businesses in illicit and illegal trades but also cryptocurrencies have been used in cases of extortion, ransom and as part of sophisticated malware modus operandi. We tackle these challenges by developing an analytical capability that allows us to map relationships on the blockchain and filter crime instances in order to investigate the abuse in law enforcement local environment. We propose a practical bitcoin analytical process and an analyzing system that stands alone and manages all data on the blockchain in real-time with tracing and visualizing techniques rendering transactions decipherable and useful for law enforcement investigation and training. Our system adopts combination of analyzing methods that provides statistics of address, graphical transaction relation, discovery of paths and clustering of already known addresses. We evaluated our system in the three criminal cases includes marketplace, ransomware and DDoS extortion. These are practical training in law enforcement, then we determined whether our system could help investigation process and training.
We present a testbed implementation for the development, evaluation and demonstration of security orchestration in a network function virtualization environment. As a specific scenario, we demonstrate how an intelligent response to DDoS and various other kinds of targeted attacks can be formulated such that these attacks and future variations can be mitigated. We utilise machine learning to characterise normal network traffic, attacks and responses, then utilise this information to orchestrate virtualized network functions around affected components to isolate these components and to capture, redirect and filter traffic (e.g. honeypotting) for additional analysis. This allows us to maintain a high level of network quality of service to given network functions and components despite adverse network conditions.
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.
Cybersecurity is one of critical issues in modern military operations. In cyber operations, security professionals depend on various information and security systems to mitigate cyber threats through enhanced cyber situational awareness. Cyber situational awareness can give decision makers mission completeness and providing appropriate timely decision support for proactive response. The crucial information for cyber situational awareness can be collected at network boundaries through deep packet inspection with security systems. Regular expression is regarded as a practical method for deep packet inspection that is considering a next generation intrusion detection and prevention, however, it is not commonly used by the reason of its resource intensive characteristics. In this paper, we describe our effort and achievement on regular expression processing capability in real time and an evaluation method with experimental result.
In this paper, a game-theoretical solution concept is utilized to tackle the collusion attack in a SDN-based framework. In our proposed setting, the defenders (i.e., switches) are incentivized not to collude with the attackers in a repeated-game setting that utilizes a reputation system. We first illustrate our model and its components. We then use a socio-rational approach to provide a new anti-collusion solution that shows cooperation with the SDN controller is always Nash Equilibrium due to the existence of a long-term utility function in our model.
Distributed Denial of Service (DDoS) attacks serve to diminish the ability of the network to perform its intended function over time. The paper presents the design, implementation and analysis of a protocol based upon a technique for address agility called DDoS Resistant Multicast (DRM). After describing the our architecture and implementation we show an analysis that quantifies the overhead on network performance. We then present the Simple Agile RPL multiCAST (SARCAST), an Internet-of-Things routing protocol for DDoS protection. We have implemented and evaluated SARCAST in a working IoT operating system and testbed. Our results show that SARCAST provides very high levels of protection against DDoS attacks with virtually no impact on overall performance.
End-hopping is an effective component of Moving Target Defense (MTD) by randomly hopping network configuration of host, which is a game changing technique against cyber-attack and can interrupt cyber kill chain in the early stage. In this paper, a novel end-hopping model, Multi End-hopping (MEH), is proposed to exploit the full potentials of MTD techniques by hosts cooperating with others to share possible configurable space (PCS). And an optimization method based on cooperative game is presented to make hosts form optimal alliances against reconnaissance, scanning and blind probing DoS attack. Those model and method confuse adversaries by establishing alliances of hosts to enlarge their PCS, which thwarts various malicious scanning and mitigates probing DoS attack intensity. Through simulations, we validate the correctness of MEH model and the effectiveness of optimization method. Experiment results show that the proposed model and method increase system stable operational probability while introduces a low overhead in optimization.
The wireless boundaries of networks are becoming increasingly important from a security standpoint as the proliferation of 802.11 WiFi technology increases. Concurrently, the complexity of 802.11 access point implementation is rapidly outpacing the standardization process. The result is that nascent wireless functionality management is left up to the individual provider's implementation, which creates new vulnerabilities in wireless networks. One such functional improvement to 802.11 is the virtual access point (VAP), a method of broadcasting logically separate networks from the same physical equipment. Network reconnaissance benefits from VAP identification, not only because network topology is a primary aim of such reconnaissance, but because the knowledge that a secure network and an insecure network are both being broadcast from the same physical equipment is tactically relevant information. In this work, we present a novel graph-theoretic approach to VAP identification which leverages a body of research concerned with establishing community structure. We apply our approach to both synthetic data and a large corpus of real-world data to demonstrate its efficacy. In most real-world cases, near-perfect blind identification is possible highlighting the effectiveness of our proposed VAP identification algorithm.
We propose $μ$Leech, a new embedded trusted platform module for next generation power scavenging devices. Such power scavenging devices are already widely deployed. For instance, the Square point-of-sale reader uses the microphone/speaker interface of a smartphone for communications and as power supply. While such devices are used as trusted devices in security critical applications in the wild, they have not been properly evaluated yet. $μ$Leech can securely store keys and provide cryptographic services to any connected smart phone. Our design also facilitates physical security analysis by providing interfaces to facilitate acquisition of power traces and clock manipulation attacks. Thus $μ$Leech empowers security researchers to analyze leakage in next generation embedded and IoT devices and to evaluate countermeasures before deployment.
High accurate time synchronization is very important for many applications and industrial environments. In a computer network, synchronization of time for connected devices is provided by the Precision Time Protocol (PTP), which in principal allows for device time synchronization down to microsecond level. However, PTP and network infrastructures are vulnerable to cyber-attacks, which can de-synchronize an entire network, leading to potentially devastating consequences. This paper will focus on the issue of internal attacks on time synchronization networks and discuss how counter-measures based on public key infrastructures, trusted platform modules, network intrusion detection systems and time synchronization supervisors can be adopted to defeat or at least detect such internal attacks.
5G, the fifth generation of mobile communication networks, is considered as one of the main IoT enablers. Connecting billions of things, 5G/IoT will be dealing with trillions of GBytes of data. Securing such large amounts of data is a very challenging task. Collected data varies from simple temperature measurements to more critical transaction data. Thus, applying uniform security measures is a waste of resources (processing, memory, and network bandwidth). Alternatively, a multi-level security model needs to be applied according to the varying requirements. In this paper, we present a multi-level security scheme (BLP) applied originally in the information security domain. We review its application in the network domain, and propose a modified version of BLP for the 5G/IoT case. The proposed model is proven to be secure and compliant with the model rules.
This paper describes an experiment carried out to demonstrate robustness and trustworthiness of an orchestrated two-layer network test-bed (PROnet). A Robotic Operating System Industrial (ROS-I) distributed application makes use of end-to-end flow services offered by PROnet. The PROnet Orchestrator is used to provision reliable end-to-end Ethernet flows to support the ROS-I application required data exchange. For maximum reliability, the Orchestrator provisions network resource redundancy at both layers, i.e., Ethernet and optical. Experimental results show that the robotic application is not interrupted by a fiber outage.
Cloud data centers are critical infrastructures to deliver cloud services. Although security and performance of cloud data centers have been well studied in the past, their networking aspects are overlooked. Current network infrastructures in cloud data centers limit the ability of cloud provider to offer guaranteed cloud network resources to users. In order to ensure security and performance requirements as defined in the service level agreement (SLA) between cloud user and provider, cloud providers need the ability to provision network resources dynamically and on the fly. The main challenge for cloud provider in utilizing network resource can be addressed by provisioning virtual networks that support information centric services by separating the control plane from the cloud infrastructure. In this paper, we propose an sdn based information centric cloud framework to provision network resources in order to support elastic demands of cloud applications depending on SLA requirements. The framework decouples the control plane and data plane wherein the conceptually centralized control plane controls and manages the fully distributed data plane. It computes the path to ensure security and performance of the network. We report initial experiment on average round-trip delay between consumers and producers.
Vehicular ad hoc networks (VANETs) are taking more attention from both the academia and the automotive industry due to a rapid development of wireless communication technologies. And with this development, vehicles called connected cars are increasingly being equipped with more sensors, processors, storages, and communication devices as they start to provide both infotainment and safety services through V2X communication. Such increase of vehicles is also related to the rise of security attacks and potential security threats. In a vehicular environment, security is one of the most important issues and it must be addressed before VANETs can be widely deployed. Conventional VANETs have some unique characteristics such as high mobility, dynamic topology, and a short connection time. Since an attacker can launch any unexpected attacks, it is difficult to predict these attacks in advance. To handle this problem, we propose collaborative security attack detection mechanism in a software-defined vehicular networks that uses multi-class support vector machine (SVM) to detect various types of attacks dynamically. We compare our security mechanism to existing distributed approach and present simulation results. The results demonstrate that the proposed security mechanism can effectively identify the types of attacks and achieve a good performance regarding high precision, recall, and accuracy.
The IoT (Internet of Things) is one of the primary reasons for the massive growth in the number of connected devices to the Internet, thus leading to an increased volume of traffic in the core network. Fog and edge computing are becoming a solution to handle IoT traffic by moving timesensitive processing to the edge of the network, while using the conventional cloud for historical analysis and long-term storage. Providing processing, storage, and network communication at the edge network are the aim of fog computing to reduce delay, network traffic, and decentralise computing. In this paper, we define a framework that realises fog computing that can be extended to install any service of choice. Our framework utilises fog nodes as an extension of the traditional switch to include processing, networking, and storage. The fog nodes act as local decision-making elements that interface with software-defined networking (SDN), to be able to push updates throughout the network. To test our framework, we develop an IP spoofing security application and ensure its correctness through multiple experiments.
Security evaluation of diverse SDN frameworks is of significant importance to design resilient systems and deal with attacks. Focused on SDN scenarios, a game-theoretic model is proposed to analyze their security performance in existing SDN architectures. The model can describe specific traits in different structures, represent several types of information of players (attacker and defender) and quantitatively calculate systems' reliability. Simulation results illustrate dynamic SDN structures have distinct security improvement over static ones. Besides, effective dynamic scheduling mechanisms adopted in dynamic systems can enhance their security further.
With the developing understanding of Information Security and digital assets, IT technology has put on tremendous importance of network admission control (NAC). In NAC architecture, admission decisions and resource reservations are taken at edge devices, rather than resources or individual routers within the network. The NAC architecture enables resilient resource reservation, maintaining reservations even after failures and intra-domain rerouting. Admission Control Networks destiny is based on IP networks through its Security and Quality of Service (QoS) demands for real time multimedia application via advance resource reservation techniques. To achieve Security & QoS demands, in real time performance networks, admission control algorithm decides whether the new traffic flow can be admitted to the network or not. Secure allocation of Peer for multimedia traffic flows with required performance is a great challenge in resource reservation schemes. In this paper, we have proposed our model for VoIP networks in order to achieve security services along with QoS, where admission control decisions are taken place at edge routers. We have analyzed and argued that the measurement based admission control should be done at edge routers which employs on-demand probing parallel from both edge routers to secure the source and destination nodes respectively. In order to achieve Security and QoS for a new call, we choose various probe packet sizes for voice and video calls respectively. Similarly a technique is adopted to attain a security allocation approach for selecting an admission control threshold by proposing our admission control algorithm. All results are tested on NS2 based simulation to evalualate the network performance of edge router based upon network admission control in VoIP traffic.
Customer Edge Switching (CES) is an experimental Internet architecture that provides reliable and resilient multi-domain communications. It provides resilience against security threats because domains negotiate inbound and outbound policies before admitting new traffic. As CES and its signalling protocols are being prototyped, there is a need for independent testing of the CES architecture. Hence, our research goal is to develop an automated test framework that CES protocol designers and early adopters can use to improve the architecture. The test framework includes security, functional, and performance tests. Using the Robot Framework and STRIDE analysis, in this paper we present this automated security test framework. By evaluating sample test scenarios, we show that the Robot Framework and our CES test suite have provided productive discussions about this new architecture, in addition to serving as clear, easy-to-read documentation. Our research also confirms that test automation can be useful to improve new protocol architectures and validate their implementation.