Biblio

Software Defined Networking (SDN) is the new promise towards an easily configured and remotely controlled network. Based on Centralized control, SDN technology has proved its positive impact on the world of network communications from different aspects. Security in SDN, as in traditional networks, is an essential feature that every communication system should possess. In this paper, we propose an SDN security design approach, which strikes a good balance between network performance and security features. We show how such an approach can be used to prevent DDoS attacks targeting either the controller or the different hosts in the network, and how to trace back the source of the attack. The solution lies in introducing a third plane, the security plane, in addition to the data plane, which is responsible for forwarding data packets between SDN switches, and parallel to the control plane, which is responsible for rule and data exchange between the switches and the SDN controller. The security plane is designed to exchange security-related data between a third party agent on the switch and a third party software module alongside the controller. Our evaluation shows the capability of the proposed system to enforce different levels of real-time user-defined security with low overhead and minimal configuration.

Mission assurance requires effective, near-real time defensive cyber operations to appropriately respond to cyber attacks, without having a significant impact on operations. The ability to rapidly compute, prioritize and execute network-based courses of action (CoAs) relies on accurate situational awareness and mission-context information. Although diverse solutions exist for automatically collecting and analysing infrastructure data, few deliver automated analysis and implementation of network-based CoAs in the context of the ongoing mission. In addition, such processes can be operatorintensive and available tools tend to be specific to a set of common data sources and network responses. To address these issues, Defence Research and Development Canada (DRDC) is leading the development of the Automated Computer Network Defence (ARMOUR) technology demonstrator and cyber defence science and technology (S&T) platform. ARMOUR integrates new and existing off-the-shelf capabilities to provide enhanced decision support and to automate many of the tasks currently executed manually by network operators. This paper describes the cyber defence integration framework, situational awareness, and automated mission-oriented decision support that ARMOUR provides.

Supervisory Control and Data Acquisition(SCADA) communications are often subjected to various sophisticated cyber-attacks mostly because of their static system characteristics, enabling an attacker for easier profiling of the target system(s) and thereby impacting the Critical Infrastructures(CI). In this Paper, a novel approach to mitigate such static vulnerabilities is proposed by implementing a Moving Target Defense (MTD) strategy in a power grid SCADA environment, leveraging the existing communication network with an end-to-end IP-Hopping technique among trusted peers. The main contribution involves the design and implementation of MTD Architecture on Iowa State's PowerCyber testbed for targeted cyber-attacks, without compromising the availability of a SCADA system and studying the delay and throughput characteristics for different hopping rates in a realistic environment. Finally, we study two cases and provide mitigations for potential weaknesses of the proposed mechanism. Also, we propose to incorporate port mutation to further increase attack complexity as part of future work.

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.

Wireless sensor network is a low cost network to solve many of the real world problems. These sensor nodes used to deploy in the hostile or unattended areas to sense and monitor the atmospheric situations such as motion, pressure, sound, temperature and vibration etc. The sensor nodes have low energy and low computing power, any security scheme for wireless sensor network must not be computationally complex and it should be efficient. In this paper we introduced a secure routing protocol for WSNs, which is able to prevent the network from DDoS attack. In our methodology we scan the infected nodes using the proposed algorithm and block that node from any further activities in the network. To protect the network we use intrusion prevention scheme, where specific nodes of the network acts as IPS node. These nodes operate in their radio range for the region of the network and scan the neighbors regularly. When the IPS node find a misbehavior node which is involves in frequent message passing other than UDP and TCP messages, IPS node blocks the infected node and also send the information to all genuine sender nodes to change their routes. All simulation work has been done using NS 2.35. After simulation the proposed scheme gives feasible results to protect the network against DDoS attack. The performance parameters have been improved after applying the security mechanism on an infected network.

Communication networks can be the targets of organized and distributed attacks such as flooding-type DDOS attack in which malicious users aim to cripple a network server or a network domain. For the attack to have a major effect on the network, malicious users must act in a coordinated and time correlated manner. For instance, the members of the flooding attack increase their message transmission rates rapidly but also synchronously. Even though detection and prevention of the flooding attacks are well studied at network and transport layers, the emergence and wide deployment of new systems such as VoIP (Voice over IP) have turned flooding attacks at the session layer into a new defense challenge. In this study a structured sparsity based group anomaly detection system is proposed that not only can detect synchronized attacks, but also identify the malicious groups from normal users by jointly estimating their members, structure, starting and end points. Although we mainly focus on security on SIP (Session Initiation Protocol) servers/proxies which are widely used for signaling in VoIP systems, the proposed scheme can be easily adapted for any type of communication network system at any layer.

In this paper, we propose a hardware-based defense system in Software-Defined Networking architecture to protect against the HTTP GET Flooding attacks, one of the most dangerous Distributed Denial of Service (DDoS) attacks in recent years. Our defense system utilizes per-URL counting mechanism and has been implemented on FPGA as an extension of a NetFPGA-based OpenFlow switch.

Distributed Denial of Service (DDoS) is a sophisticated cyber-attack due to its variety of types and techniques. The traditional mitigation method of this attack is to deploy dedicated security appliances such as firewall, load balancer, etc. However, due to the limited capacity of the hardware and the potential high volume of DDoS traffic, it may not be able to defend all the attacks. Therefore, cloud-based DDoS protection services were introduced to allow the organizations to redirect their traffic to the scrubbing centers in the cloud for filtering. This solution has some drawbacks such as privacy violation and latency. More recently, Network Functions Virtualization (NFV) and edge computing have been proposed as new networking service models. In this paper, we design a framework that leverages NFV and edge computing for DDoS mitigation through two-stage processes.

Software-Defined Networking (SDN) allows for fast reactions to security threats by dynamically enforcing simple forwarding rules as counter-measures. However, in classic SDN all the intelligence resides at the controller, with the switches only capable of performing stateless forwarding as ruled by the controller. It follows that the controller, in addition to network management and control duties, must collect and process any piece of information required to take advanced (stateful) forwarding decisions. This threatens both to overload the controller and to congest the control channel. On the other hand, stateful SDN represents a new concept, developed both to improve reactivity and to offload the controller and the control channel by delegating local treatments to the switches. In this paper, we adopt this stateful paradigm to protect end-hosts from Distributed Denial of Service (DDoS). We propose StateSec, a novel approach based on in-switch processing capabilities to detect and mitigate DDoS attacks. StateSec monitors packets matching configurable traffic features (e.g., IP src/dst, port src/dst) without resorting to the controller. By feeding an entropy-based algorithm with such monitoring features, StateSec detects and mitigates several threats such as (D)DoS and port scans with high accuracy. We implemented StateSec and compared it with a state-of-the-art approach to monitor traffic in SDN. We show that StateSec is more efficient: it achieves very accurate detection levels, limiting at the same time the control plane overhead.

The 911 emergency service belongs to one of the 16 critical infrastructure sectors in the United States. Distributed denial of service (DDoS) attacks launched from a mobile phone botnet pose a significant threat to the availability of this vital service. In this paper we show how attackers can exploit the cellular network protocols in order to launch an anonymized DDoS attack on 911. The current FCC regulations require that all emergency calls be immediately routed regardless of the caller's identifiers (e.g., IMSI and IMEI). A rootkit placed within the baseband firmware of a mobile phone can mask and randomize all cellular identifiers, causing the device to have no genuine identification within the cellular network. Such anonymized phones can issue repeated emergency calls that cannot be blocked by the network or the emergency call centers, technically or legally. We explore the 911 infrastructure and discuss why it is susceptible to this kind of attack. We then implement different forms of the attack and test our implementation on a small cellular network. Finally, we simulate and analyze anonymous attacks on a model of current 911 infrastructure in order to measure the severity of their impact. We found that with less than 6K bots (or \$100K hardware), attackers can block emergency services in an entire state (e.g., North Carolina) for days. We believe that this paper will assist the respective organizations, lawmakers, and security professionals in understanding the scope of this issue in order to prevent possible 911-DDoS attacks in the future.

Securing Internet of Things (IoT) systems is a challenge because of its multiple points of vulnerability. A spate of recent hacks and security breaches has unveiled glaring vulnerabilities in the IoT. Due to the computational and memory requirement constraints associated with anomaly detection algorithms in core networks, commercial in-line (part of the direct line of communication) Anomaly Detection Systems (ADSs) rely on sampling-based anomaly detection approaches to achieve line rates and truly-inline anomaly detection accuracy in real-time. However, packet sampling is inherently a lossy process which might provide an incomplete and biased approximation of the underlying traffic patterns. Moreover, commercial routers uses proprietary software making them closed to be manipulated from the outside. As a result, detecting malicious packets on the given network path is one of the most challenging problems in the field of network security. We argue that the advent of Software Defined Networking (SDN) provides a unique opportunity to effectively detect and mitigate DDoS attacks. Unlike sampling-based approaches for anomaly detection and limitation of proprietary software at routers, we use the SDN infrastructure to relax the sampling-based ADS constraints and collect traffic flow statistics which are maintained at each SDN-enabled switch to achieve high detection accuracy. In order to implement our idea, we discuss how to mitigate DDoS attacks using the features of SDN infrastructure.

A technique and algorithms for early detection of the started attack and subsequent blocking of malicious traffic are proposed. The primary separation of mixed traffic into trustworthy and malicious traffic was carried out using cluster analysis. Classification of newly arrived requests was done using different classifiers with the help of received training samples and developed success criteria.

One of the recent focuses in Cloud Computing networks is Software Defined Clouds (SDC), where the Software-Defined Networking (SDN) technology is combined with the traditional Cloud network. SDC is aimed to create an effective Cloud environment by extending the virtualization concept to all resources. In that, the control plane is decoupled from the data plane in a network device and controlled by the centralized controller using the OpenFlow Protocol (OFP). As the centralized controller performs all control functions in a network, it requires strong security. Already, Cloud Computing faces many security challenges. Most vulnerable attacks in SDC is Denial-of-Service (DoS) and Distributed DoS (DDoS) attacks. To overcome the DoS attacks, we propose a distributed Firewall with Intrusion Prevention System (IPS) for SDC. The proposed distributed security mechanism is investigated for two DoS attacks, ICMP and SYN flooding attacks for different network scenarios. From the simulation results and discussion, we showed that the distributed Firewall with IPS security detects and prevents the DoS attack effectively.

In the era of Big Data, software systems can be affected by its growing complexity, both with respect to functional and non-functional requirements. As more and more people use software applications over the web, the ability to recognize if some of this traffic is malicious or legitimate is a challenge. The traffic load of security controllers, as well as the complexity of security rules to detect attacks can grow to levels where current solutions may not suffice. In this work, we propose a hierarchical distributed architecture for security control in order to partition responsibility and workload among many security controllers. In addition, our architecture proposes a more simplified way of defining security rules to allow security to be enforced on an operational level, rather than a development level.

Cloud Computing has many significant benefits like the provision of computing resources and virtual networks on demand. However, there is the problem to assure the security of these networks against Distributed Denial-of-Service (DDoS) attack. Over the past few decades, the development of protection method based on data mining has attracted many researchers because of its effectiveness and practical significance. Most commonly these detection methods use prelearned models or models based on rules. Because of this the proposed DDoS detection methods often failure in dynamically changing cloud virtual networks. In this paper, we purposed self-learning method allows to adapt a detection model to network changes. This is minimized the false detection and reduce the possibility to mark legitimate users as malicious and vice versa. The developed method consists of two steps: collecting data about the network traffic by Netflow protocol and relearning the detection model with the new data. During the data collection we separate the traffic on legitimate and malicious. The separated traffic is labeled and sent to the relearning pool. The detection model is relearned by a data from the pool of current traffic. The experiment results show that proposed method could increase efficiency of DDoS detection systems is using data mining.

The evolution of information and communication technologies has brought new challenges in managing the Internet. Software-Defined Networking (SDN) aims to provide easily configured and remotely controlled networks based on centralized control. Since SDN will be the next disruption in networking, SDN security has become a hot research topic because of its importance in communication systems. A centralized controller can become a focal point of attack, thus preventing attack in controller will be a priority. The whole network will be affected if attacker gain access to the controller. One of the attacks that affect SDN controller is DDoS attacks. This paper reviews different detection techniques that are available to prevent DDoS attacks, characteristics of these techniques and issues that may arise using these techniques.

Distributed Denial of Service (DDoS) attacks are a popular and inexpensive form of cyber attacks. Application layer DDoS attacks utilize legitimate application layer requests to overwhelm a web server. These attacks are a major threat to Internet applications and web services. The main goal of these attacks is to make the services unavailable to legitimate users by overwhelming the resources on a web server. They look valid in connection and protocol characteristics, which makes them difficult to detect. In this paper, we propose a detection method for the application layer DDoS attacks, which is based on user behavior anomaly detection. We extract instances of user behaviors requesting resources from HTTP web server logs. We apply the Principle Component Analysis (PCA) subspace anomaly detection method for the detection of anomalous behavior instances. Web server logs from a web server hosting a student resource portal were collected as experimental data. We also generated nine different HTTP DDoS attacks through penetration testing. Our performance results on the collected data show that using PCAsubspace anomaly detection on user behavior data can detect application layer DDoS attacks, even if they are trying to mimic a normal user's behavior at some level.

Distributed Denial of Service (DDoS) attack is a congestion-based attack that makes both the network and host-based resources unavailable for legitimate users, sending flooding attack packets to the victim's resources. The non-existence of predefined rules to correctly identify the genuine network flow made the task of DDoS attack detection very difficult. In this paper, a combination of unsupervised data mining techniques as intrusion detection system are introduced. The entropy concept in term of windowing the incoming packets is applied with data mining technique using Clustering Using Representative (CURE) as cluster analysis to detect the DDoS attack in network flow. The data is mainly collected from DARPA2000, CAIDA2007 and CAIDA2008 datasets. The proposed approach has been evaluated and compared with several existing approaches in terms of accuracy, false alarm rate, detection rate, F. measure and Phi coefficient. Results indicates the superiority of the proposed approach with four out five detected phases, more than 99% accuracy rate 96.29% detection rate, around 0% false alarm rate 97.98% F-measure, and 97.98% Phi coefficient.

Denial of service (DOS) attacks are a serious threat to network security. These attacks are often sourced from virtual machines in the cloud, rather than from the attacker's own machine, to achieve anonymity and higher network bandwidth. Past research focused on analyzing traffic on the destination (victim's) side with predefined thresholds. These approaches have significant disadvantages. They are only passive defenses after the attack, they cannot use the outbound statistical features of attacks, and it is hard to trace back to the attacker with these approaches. In this paper, we propose a DOS attack detection system on the source side in the cloud, based on machine learning techniques. This system leverages statistical information from both the cloud server's hypervisor and the virtual machines, to prevent network packages from being sent out to the outside network. We evaluate nine machine learning algorithms and carefully compare their performance. Our experimental results show that more than 99.7% of four kinds of DOS attacks are successfully detected. Our approach does not degrade performance and can be easily extended to broader DOS attacks.

Aiming at the problem of internal attackers of database system, anomaly detection method of user behaviour is used to detect the internal attackers of database system. With using Discrete-time Markov Chains (DTMC), an anomaly detection system of user behavior is proposed, which can detect the internal threats of database system. First, we make an analysis on SQL queries, which are user behavior features. Then, we use DTMC model extract behavior features of a normal user and the detected user and make a comparison between them. If the deviation of features is beyond threshold, the detected user behavior is judged as an anomaly behavior. The experiments are used to test the feasibility of the detction system. The experimental results show that this detction system can detect normal and abnormal user behavior precisely and effectively.

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.

OpenFlow has recently emerged as a powerful paradigm to help build dynamic, adaptive and agile networks. By decoupling control plane from data plane, OpenFlow allows network operators to program a centralized intelligence, OpenFlow controller, to manage network-wide traffic flows to meet the changing needs. However, from the security's point of view, a buggy or even malicious controller could compromise the control logic, and then the entire network. Even worse, the recent attack Stuxnet on industrial control systems also indicates the similar, severe threat to OpenFlow controllers from the commercial operating systems they are running on. In this paper, we comprehensively studied the attack vectors against the OpenFlow critical component, controller, and proposed a cross layer diversity approach that enables OpenFlow controllers to detect attacks, corruptions, failures, and then automatically continue correct execution. Case studies demonstrate that our approach can protect OpenFlow controllers from threats coming from compromised operating systems and themselves.

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.

Nowadays the adoption of IoT solutions is gaining high momentum in several fields, including energy, home and environment monitoring, transportation, and manufacturing. However, cybersecurity attacks to low-cost end-user devices can severely undermine the expected deployment of IoT solutions in a broad range of scenarios. To face these challenges, emerging software-based networking features can introduce new security enablers, providing further scalability and flexibility required to cope with massive IoT. In this paper, we present a novel framework aiming to exploit SDN/NFV-based security features and devise new efficient integration with existing IoT security approaches. The potential benefits of the proposed framework is validated in two case studies. Finally, a feasibility study is presented, accounting for potential interactions with open-source SDN/NFV projects and relevant standardization activities.

Cloud computing emerges as an endowment technological data for the longer term and increasing on one of the standards of utility computing is most likely claimed to symbolize a wholly new paradigm for viewing and getting access to computational assets. As a result of protection problem many purchasers hesitate in relocating their touchy data on the clouds, regardless of gigantic curiosity in cloud-based computing. Security is a tremendous hassle, considering the fact that so much of firms present a alluring goal for intruders and the particular considerations will pursue to lower the advancement of distributed computing if not located. Hence, this recent scan and perception is suitable to honeypot. Distributed Denial of Service (DDoS) is an assault that threats the availability of the cloud services. It's fundamental investigate the most important features of DDoS Defence procedures. This paper provides exact techniques that been carried out to the DDoS attack. These approaches are outlined in these paper and use of applied sciences for special kind of malfunctioning within the cloud.