Biblio
Software Defined Networking (SDN) is a paradigm shift that changes the working principles of IP networks by separating the control logic from routers and switches, and logically centralizing it within a controller. In this architecture the control plane (controller) communicates with the data plane (switches) through a control channel using a standards-compliant protocol, that is, OpenFlow. While having a centralized controller creates an opportunity to monitor and program the entire network, as a side effect, it causes the control plane to become a single point of failure. Denial of service (DoS) attacks or even heavy control traffic conditions can easily become real threats to the proper functioning of the controller, which indirectly detriments the entire network. In this paper, we propose a solution to reduce the control traffic generated primarily during table-miss events. We utilize the buffer\_id feature of the OpenFlow protocol, which has been designed to identify individually buffered packets within a switch, reusing it to identify flows buffered as a series of packets during table-miss, which happens when there is no related rule in the switch flow tables that matches the received packet. Thus, we allow the OpenFlow switch to send only the first packet of a flow to the controller for a table-miss while buffering the rest of the packets in the switch memory until the controller responds or time out occurs. The test results show that OpenFlow traffic is significantly reduced when the proposed method is used.
Software Defined Networking (SDN) stands to transmute our modern networks and data centers, opening them up into highly agile frameworks that can be reconfigured depending on the requirement. Denial of Service (DoS) attacks are considered as one of the most destructive attacks. This paper, is about DoS attack detection and mitigation using SDN. DoS attack can minimize the bandwidth utilization, leaving the network unavailable for legitimate traffic. To provide a solution to the problem, concept of performance aware Software Defined Networking is used which involves real time network monitoring using sFlow as a visibility protocol. So, OpenFlow along with sFlow is used as an application to fight DoS attacks. Our analysis and results demonstrate that using this technique, DoS attacks are successfully defended implying that SDN has promising potential to detect and mitigate DoS attacks.
Detection and prevention of data breaches in corporate networks is one of the most important security problems of today's world. The techniques and applications proposed for solution are not successful when attackers attempt to steal data using steganography. Steganography is the art of storing data in a file called cover, such as picture, sound and video. The concealed data cannot be directly recognized in the cover. Steganalysis is the process of revealing the presence of embedded messages in these files. There are many statistical and signature based steganalysis algorithms. In this work, the detection of steganographic images with steganalysis techniques is reviewed and a system has been developed which automatically detects steganographic images in network traffic by using open source tools.
Cyber-security threats are a growing concern in networked environments. The development of Intrusion Detection Systems (IDSs) is fundamental in order to provide extra level of security. We have developed an unsupervised anomaly-based IDS that uses statistical techniques to conduct the detection process. Despite providing many advantages, anomaly-based IDSs tend to generate a high number of false alarms. Machine Learning (ML) techniques have gained wide interest in tasks of intrusion detection. In this work, Support Vector Machine (SVM) is deemed as an ML technique that could complement the performance of our IDS, providing a second line of detection to reduce the number of false alarms, or as an alternative detection technique. We assess the performance of our IDS against one-class and two-class SVMs, using linear and non- linear forms. The results that we present show that linear two-class SVM generates highly accurate results, and the accuracy of the linear one-class SVM is very comparable, and it does not need training datasets associated with malicious data. Similarly, the results evidence that our IDS could benefit from the use of ML techniques to increase its accuracy when analysing datasets comprising of non- homogeneous features.
The Network Intrusion Detection Systems (NIDS) are either signature based or anomaly based. In this paper presented NIDS system belongs to anomaly based Neural Network Intrusion Detection System (NNIDS). The proposed NNIDS is able to successfully recognize learned malicious activities in a network environment. It was tested for the SYN flood attack, UDP flood attack, nMap scanning attack, and also for non-malicious communication.
Cyber anonymity tools have attracted wide attention in resisting network traffic censorship and surveillance, and have played a crucial role for open communications over the Internet. The Onion Routing (Tor) is considered the prevailing technique for circumventing the traffic surveillance and providing cyber anonymity. Tor operates by tunneling a traffic through a series of relays, making such traffic to appear as if it originated from the last relay in the traffic path, rather than from the original user. However, Tor faced some obstructions in carrying out its goal effectively, such as insufficient performance and limited capacity. This paper presents a cyber anonymity technique based on software-defined networking; named SOR, which builds onion-routed tunnels across multiple anonymity service providers. SOR architecture enables any cloud tenants to participate in the anonymity service via software-defined networking. Our proposed architecture leverages the large capacity and robust connectivity of the commercial cloud networks to elevate the performance of the cyber anonymity service.
Detecting botnets and advanced persistent threats is a major challenge for network administrators. An important component of such malware is the command and control channel, which enables the malware to respond to controller commands. The detection of malware command and control channels could help prevent further malicious activity by cyber criminals using the malware. Detection of malware in network traffic is traditionally carried out by identifying specific patterns in packet payloads. Now bot writers encrypt the command and control payloads, making pattern recognition a less effective form of detection. This paper focuses instead on an effective anomaly based detection technique for bot and advanced persistent threats using a data mining approach combined with applied classification algorithms. After additional tuning, the final test on an unseen dataset, false positive rates of 0% with malware detection rates of 100% were achieved on two examined malware threats, with promising results on a number of other threats.
The wireless spectrum is a scarce resource, and the number of wireless terminals is constantly growing. One way to mitigate this strong constraint for wireless traffic is the use of dynamic mechanisms to utilize the spectrum, such as cognitive and software-defined radios. This is especially important for the upcoming wireless sensor and actuator networks in aircraft, where real-time guarantees play an important role in the network. Future wireless networks in aircraft need to be scalable, cater to the specific requirements of avionics (e.g., standardization and certification), and provide interoperability with existing technologies. In this paper, we demonstrate that dynamic network reconfigurability is a solution to the aforementioned challenges. We supplement this claim by surveying several flexible approaches in the context of wireless sensor and actuator networks in aircraft. More specifically, we examine the concept of dynamic resource management, accomplished through more flexible transceiver hardware and by employing dedicated spectrum agents. Subsequently, we evaluate the advantages of cross-layer network architectures which overcome the fixed layering of current network stacks in an effort to provide quality of service for event-based and time-triggered traffic. Lastly, the challenges related to implementation of the aforementioned mechanisms in wireless sensor and actuator networks in aircraft are elaborated, and key requirements to future research are summarized.
Spatial information network is an important part of the integrated space-terrestrial information network, its bearer services are becoming increasingly complex, and real-time requirements are also rising. Due to the structural vulnerability of the spatial information network and the dynamics of the network, this poses a serious challenge to how to ensure reliable and stable data transmission. The structural vulnerability of the spatial information network and the dynamics of the network brings a serious challenge of ensuring reliable and stable data transmission. Software Defined Networking (SDN), as a new network architecture, not only can quickly adapt to new business, but also make network reconfiguration more intelligent. In this paper, SDN is used to design the spatial information network architecture. An optimization algorithm for network self-healing based on SDN is proposed to solve the failure of switching node. With the guarantee of Quality of Service (QoS) requirement, the link is updated with the least link to realize the fast network reconfiguration and recovery. The simulation results show that the algorithm proposed in this paper can effectively reduce the delay caused by fault recovery.
The concept of Extension Headers, newly introduced with IPv6, is elusive and enables new types of threats in the Internet. Simply dropping all traffic containing any Extension Header - a current practice by operators-seemingly is an effective solution, but at the cost of possibly dropping legitimate traffic as well. To determine whether threats indeed occur, and evaluate the actual nature of the traffic, measurement solutions need to be adapted. By implementing these specific parsing capabilities in flow exporters and performing measurements on two different production networks, we show it is feasible to quantify the metrics directly related to these threats, and thus allow for monitoring and detection. Analysing the traffic that is hidden behind Extension Headers, we find mostly benign traffic that directly affects end-user QoE: simply dropping all traffic containing Extension Headers is thus a bad practice with more consequences than operators might be aware of.
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.
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.
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.
With the advances in the areas of mobile computing and wireless communications, V2X systems have become a promising technology enabling deployment of applications providing road safety, traffic efficiency and infotainment. Due to their increasing popularity, V2X networks have become a major target for attackers, making them vulnerable to security threats and network conditions, and thus affecting the safety of passengers, vehicles and roads. Existing research in V2X does not effectively address the safety, security and performance limitation threats to connected vehicles, as a result of considering these aspects separately instead of jointly. In this work, we focus on the analysis of the tradeoffs between safety, security and performance of V2X systems and propose a dynamic adaptability approach considering all three aspects jointly based on application needs and context to achieve maximum safety on the roads using an Internet of vehicles. Experiments with a simple V2V highway scenario demonstrate that an adaptive safety/security approach is essential and V2X systems have great potential for providing low reaction times.
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.
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.
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.
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.
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.
Distributed denial of service attacks represent continuous threat to availability of information and communication resources. This research conducted the analysis of relevant scientific literature and synthesize parameters on packet and traffic flow level applicable for detection of infrastructure layer DDoS attacks. It is concluded that packet level detection uses two or more parameters while traffic flow level detection often used only one parameter which makes it more convenient and resource efficient approach in DDoS detection.