Biblio

In order to solve the problem of large data volume brought by the traditional Nyquist sampling theorem in radar signal detection, a compressive detection (CD) model based on compressed sensing (CS) theory is proposed by analyzing the sparsity of the radar target in the range domain. The lower sampling rate completes the compressive sampling of the radar signal on the range field. On this basis, the two-dimensional distribution of the Doppler unit is established by moving target detention moving target detention (MTD), and the detection of the target is achieved with the two-dimensional constant false alarm rate (2D-CFAR) detection algorithm. The simulation experiment results prove that the algorithm can effectively detect the target without the need for reconstruction signals, and has good detection performance.

The Internet of Things (IoT) is more and more present in fundamental aspects of our societies and personal life. Billions of objects now have access to the Internet. This networking capability allows for new beneficial services and applications. However, it is also the entry-point for a wide variety of cyber-attacks that target these devices. The security measures present in real IoT systems lag behind those of the standard Internet. Security is sometimes completely absent. Moving Target Defense (MTD) is a 10-year-old cyber-defense paradigm. It proposes to randomize components of a system. Reasonably, an attacker will have a higher cost attacking an MTD-version of a system compared with a static-version of it. Even if MTD has been successfully applied to standard systems, its deployment for IoT is still lacking. In this paper, we propose a generic MTD framework suitable for IoT systems: IANVS (pronounced Janus). Our framework has a modular design. Its components can be adapted according to the specific constraints and requirements of a particular IoT system. We use it to instantiate two concrete MTD strategies. One that targets the UDP port numbers (port-hopping), and another a CoAP resource URI. We implement our proposal on real hardware using Pycom LoPy4 nodes. We expose the nodes to a remote Denial-of-Service attack and evaluate the effectiveness of the IANVS-based port-hopping MTD proposal.

With the rapid development of Internet of Things (IoT), distributed denial of service (DDoS) attacks become the important security threat of the IoT. Characteristics of IoT, such as large quantities and simple function, which have easily caused the IoT devices or servers to be attacked and be turned into botnets for launching DDoS attacks. In this paper, we use software-defined networking (SDN) to develop moving target defense (MTD) architecture that increases uncertainty because of ever changing attack surface. In addition, we deploy SDN-based honeypots to mimic IoT devices, luring attackers and malwares. Finally, experimental results show that combination of MTD and SDN-based honeypots can effectively hide network asset from scanner and defend against DDoS attacks in IoT.

Moving target defense (MTD) is becoming popular with the advancements in Software Defined Networking (SDN) technologies. With centralized management through SDN, changing the network attributes such as routes to escape from attacks is simple and fast. Yet, the available alternate routes are bounded by the network topology, and a persistent attacker that continuously perform the reconnaissance can extract the whole link-map of the network. To address this issue, we propose to use virtual shadow networks (VSNs) by applying Network Function Virtualization (NFV) abilities to the network in order to deceive attacker with the fake topology information and not reveal the actual network topology and characteristics. We design this approach under a formal framework for Internet Service Provider (ISP) networks and apply it to the recently emerged indirect DDoS attacks, namely Crossfire, for evaluation. The results show that attacker spends more time to figure out the network behavior while the costs on the defender and network operations are negligible until reaching a certain network size.

The safety of web browsers is essential to the privacy of Internet users and the security of their computing systems. In the last few years, there have been several cyber attacks geared towards compromising surfers' data and systems via exploiting browser-based vulnerabilities. Android and a number of mobile operating systems have been supporting a UI component called WebView, which can be embedded in any mobile application to render the web contents. Yet, this mini-browser component has been found to be vulnerable to various kinds of attacks. For instance, an attacker in her WebView-Embedded app can inject malicious JavaScripts into the WebView to modify the web contents or to steal user's input values. This kind of attack is particularly challenging due to the full control of attackers over the content of the loaded pages. In this paper, we are proposing and testing a server-side moving target defense technique to counter the risk of JavaScript injection attacks on mobile WebViews. The solution entails creating redundant HTML forms, randomizing their attributes and values, and asserting stealthy prompts for the user data. The solution does not dictate any changes to the browser or applications codes, neither it requires key sharing with benign clients. The results of our performance and security analysis suggest that our proposed approach protects the confidentiality and integrity of user input values with minimum overhead.

Big data platforms have grown popular for real-time stream processing on distributed clusters and clouds. However, execution of sensitive streaming applications on shared computing resources increases their vulnerabilities, and may lead to data leaks and injection of spurious logic that can compromise these applications. Here, we adopt Moving Target Defense (MTD) techniques into Fast Data platforms, and propose MTD strategies by which we can mitigate these attacks. Our strategies target the platform, application and data layers, which make these reusable, rather than the OS, virtual machine, or hardware layers, which are environment specific. We use Apache Storm as the canonical distributed stream processing platform for designing our MTD strategies, and offer a preliminary evaluation that indicates the feasibility and evaluates the performance overheads.

The root cause of cross-site scripting(XSS) attack is that the JavaScript engine can't distinguish between the JavaScript code in Web application and the JavaScript code injected by attackers. Moving Target Defense (MTD) is a novel technique that aim to defeat attacks by frequently changing the system configuration so that attackers can't catch the status of the system. This paper describes the design and implement of a XSS defense method based on Moving Target Defense technology. This method adds a random attribute to each unsafe element in Web application to distinguish between the JavaScript code in Web application and the JavaScript code injected by attackers and uses a security check function to verify the random attribute, if there is no random attribute or the random attribute value is not correct in a HTML (Hypertext Markup Language) element, the execution of JavaScript code will be prevented. The experiment results show that the method can effectively prevent XSS attacks and have little impact on the system performance.

Moving Target Defense (MTD) is a research hotspot in the field of network security. Moving Target Network Defense (MTND) is the implementation of MTD at network level. Numerous related works have been proposed in the field of MTND. In this paper, we focus on the scope and area of MTND, systematically present the recent representative progress from four aspects, including IP address and port mutation, route mutation, fingerprint mutation and multiple mutation, and put forward the future development directions. Several new perspectives and elucidations on MTND are rendered.

While Moving Target Defenses (MTDs) have been increasingly recognized as a promising direction for cyber security, quantifying the effects of MTDs remains mostly an open problem. Each MTD has its own set of advantages and disadvantages. No single MTD provides an effective defense against the entire range of possible threats. One of the challenges facing MTD quantification efforts is predicting the cumulative effect of implementing multiple MTDs. We present a scenario where two MTDs are deployed in an experimental testbed created to model a realistic use case. This is followed by a probabilistic analysis of the effectiveness of both MTDs against a multi-step attack, along with the MTDs' impact on availability to legitimate users. Our work is essential to providing decision makers with the knowledge to make informed choices regarding cyber defense.

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.

This paper will suggest a robust method for a network layer Moving Target Defense (MTD) using symmetric packet scheduling rules. The MTD is implemented and tested on a Supervisory Control and Data Acquisition (SCADA) network testbed. This method is shown to be efficient while providing security benefits to the issues faced by the static nature of SCADA networks. The proposed method is an automated tool that may provide defense in depth when be used in conjunction with other MTDs and traditional security devices.

Among the several threats to cyber services Distributed denial-of-service (DDoS) attack is most prevailing nowadays. DDoS involves making an online service unavailable by flooding the bandwidth or resources of a targeted system. It is easier for an insider having legitimate access to the system to circumvent any security controls thus resulting in insider attack. To mitigate insider assisted DDoS attacks, this paper proposes a moving target defense mechanism that involves isolation of insiders from innocent clients by using attack proxies. Further using the concept of load balancing an effective algorithm to detect and handle insider attack is developed with the aim of maximizing attack isolation while minimizing the total number of proxies used.

The increased number of cyber attacks makes the availability of services a major security concern. One common type of cyber threat is distributed denial of service (DDoS). A DDoS attack is aimed at disrupting the legitimate users from accessing the services. It is easier for an insider having legitimate access to the system to deceive any security controls resulting in insider attack. This paper proposes an Early Detection and Isolation Policy (EDIP)to mitigate insider-assisted DDoS attacks. EDIP detects insider among all legitimate clients present in the system at proxy level and isolate it from innocent clients by migrating it to attack proxy. Further an effective algorithm for detection and isolation of insider is developed with the aim of maximizing attack isolation while minimizing disruption to benign clients. In addition, concept of load balancing is used to prevent proxies from getting overloaded.

Many common cyberdefenses (like firewalls and intrusion-detection systems) are static, giving attackers the freedom to probe them at will. Moving-target defense (MTD) adds dynamism, putting the systems to be defended in motion, potentially at great cost to the defender. An alternative approach is a mobile resilient defense that removes attackers' ability to rely on prior experience without requiring motion in the protected infrastructure. The defensive technology absorbs most of the cost of motion, is resilient to attack, and is unpredictable to attackers. The authors' mobile resilient defense, Ant-Based Cyber Defense (ABCD), is a set of roaming, bio-inspired, digital-ant agents working with stationary agents in a hierarchy headed by a human supervisor. ABCD provides a resilient, extensible, and flexible defense that can scale to large, multi-enterprise infrastructures such as the smart electric grid.

Moving Target Defense (MTD) changes the attack surface of a system that confuses intruders to thwart attacks. Various MTD techniques are developed to enhance the security of a networked system, but the effectiveness of these techniques is not well assessed. Security models (e.g., Attack Graphs (AGs)) provide formal methods of assessing security, but modeling the MTD techniques in security models has not been studied. In this paper, we incorporate the MTD techniques in security modeling and analysis using a scalable security model, namely Hierarchical Attack Representation Models (HARMs), to assess the effectiveness of the MTD techniques. In addition, we use importance measures (IMs) for scalable security analysis and deploying the MTD techniques in an effective manner. The performance comparison between the HARM and the AG is given. Also, we compare the performance of using the IMs and the exhaustive search method in simulations.

One of the criticisms of traditional security approaches is that they present a static target for attackers. Critics state, with good justification, that by allowing the attacker to reconnoiter a system at leisure to plan an attack, defenders are immediately disadvantaged. To address this, the concept of moving-target defense (MTD) has recently emerged as a new paradigm for protecting computer networks and systems.
 

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.