Biblio

Software Defined Networks (SDNs) have gained prominence recently due to their flexible management and superior configuration functionality of the underlying network. SDNs, with OpenFlow as their primary implementation, allow for the use of a centralised controller to drive the decision making for all the supported devices in the network and manage traffic through routing table changes for incoming flows. In conventional networks, machine learning has been shown to detect malicious intrusion, and classify attacks such as DoS, user to root, and probe attacks. In this work, we extend the use of machine learning to improve traffic tolerance for SDNs. To achieve this, we extend the functionality of the controller to include a resilience framework, ReSDN, that incorporates machine learning to be able to distinguish DoS attacks, focussing on a neptune attack for our experiments. Our model is trained using the MIT KDD 1999 dataset. The system is developed as a module on top of the POX controller platform and evaluated using the Mininet simulator.

The network intrusion detection problem domain is described with mathematical knowledge in this paper, and a novel IDS detection model based on immune mechanism is designed. We study the key modules of IDS system, detector tolerance module and the algorithms of IDS detection intensively. Then, the continuous bit matching algorithm for computing affinity is improved by further analysis. At the same time, we adopt controllable variation and random variation, as well as dynamic demotion to improve the dynamic clonal selection algorithm. Finally the experimental simulations verify that the novel artificial immune algorithm has better detection rate and lower noise factor.

Computer security has become an increasingly important hot topic in computer and communication industry, since it is important to support critical business process and to protect personal and sensitive information. Computer security is to keep security attributes (confidentiality, integrity and availability) of computer systems, which face the threats such as deny-of-service (DoS), virus and intrusion. To ensure high computer security, the intrusion tolerance technique based on fault-tolerant scheme has been widely applied. This paper presents the quantitative performance evaluation of a virtual machine (VM) based intrusion tolerant system. Concretely, two security measures are derived; MTTSF (mean time to security failure) and the effective traffic intensity. The mathematical analysis is achieved by using Laplace-Stieltjes transforms according to the analysis of M/G/1 queueing system.

As the Internet becomes an important part of the infrastructure our society depends on, it is crucial to construct networks that are able to work even when part of the network is compromised. This paper presents the first practical intrusion-tolerant network service, targeting high-value applications such as monitoring and control of global clouds and management of critical infrastructure for the power grid. We use an overlay approach to leverage the existing IP infrastructure while providing the required resiliency and timeliness. Our solution overcomes malicious attacks and compromises in both the underlying network infrastructure and in the overlay itself. We deploy and evaluate the intrusion-tolerant overlay implementation on a global cloud spanning East Asia, North America, and Europe, and make it publicly available.

The prevalence and effectiveness of phishing attacks, despite the presence of a vast array of technical defences, are due largely to the fact that attackers are ruthlessly targeting what is often referred to as the weakest link in the system - the human. This paper reports the results of an investigation into how end users behave when faced with phishing websites and how this behaviour exposes them to attack. Specifically, the paper presents a proof of concept computer model for simulating human behaviour with respect to phishing website detection based on the ACT-R cognitive architecture, and draws conclusions as to the applicability of this architecture to human behaviour modelling within a phishing detection scenario. Following the development of a high-level conceptual model of the phishing website detection process, the study draws upon ACT-R to model and simulate the cognitive processes involved in judging the validity of a representative webpage based primarily around the characteristics of the HTTPS padlock security indicator. The study concludes that despite the low-level nature of the architecture and its very basic user interface support, ACT-R possesses strong capabilities which map well onto the phishing use case, and that further work to more fully represent the range of human security knowledge and behaviours in an ACT-R model could lead to improved insights into how best to combine technical and human defences to reduce the risk to end users from phishing attacks.

To address increasing problems caused by cyber attacks, we leverage Software Defined networks and Network Function Virtualisation governed by a SARNET-agent to enable autonomous response and attack mitigation. A Secure Autonomous Response Network (SARNET) uses a control loop to constantly assess the security state of the network by means of observables. Using a prototype we introduce the metrics impact and effectiveness and show how they can be used to compare and evaluate countermeasures. These metrics become building blocks for self learning SARNET which exhibit true autonomous response.

Wireless Sensor Network (WSN) are spread everywhere throughout the world and are ordinarily used to gather physical data from the encompassing scene. WSN play a focal part in the Internet of Things (IoT) vision. WSN is rising as a noticeable component in the middleware connecting together the Internet of Things (IoT) and the Web of Things (WoT). But the integration of WSN to WoT brings new challenges that cannot be solved in a satisfactory way with traditional layer of security. This paper examined the security issue of integration between WSN and WoT, aiming to shed light on how the WSN and WoT security issue are understood and applied, both in academia and industries. This paper introduces security perfective of integration WSN to WoT which offers capabilities to identify and connect worldwide physical objects into a unified system. As a part of the integration, serious concerns are raised over access of personal information pertaining to device (smart thing) and individual privacy. The motivation of this paper is to summarizes the security threats of the integration and suggestion to mitigate the threat.

A Software-Defined Wireless Sensor Network (SD-WSN) is a recently developed model which is expected to play a large role not only in the development of the Internet of Things (IoT) paradigm but also as a platform for other applications such as smart water management. This model makes use of a Software-Defined Networking (SDN) approach to manage a Wireless Sensor Network (WSN) in order to solve most of the inherent issues surrounding WSNs. One of the most important aspects of any network, is security. This is an area that has received little attention within the development of SDWSNs, as most research addresses security concerns within SDN and WSNs independently. There is a need for research into the security of SDWSN. Some concepts from both SDN and WSN security can be adjusted to suit the SDWSN model while others cannot. Further research is needed into consolidating SDN and WSN security measures to consider security in SDWSN. Threats, challenges and potential solutions to securing SDWSN are presented by considering both the WSN and SDN paradigms.

Two emerging architectural paradigms, i.e., Software Defined Networking (SDN) and Network Function Virtualization (NFV), enable the deployment and management of Service Function Chains (SFCs). A SFC is an ordered sequence of abstract Service Functions (SFs), e.g., firewalls, VPN-gateways, traffic monitors, that packets have to traverse in the route from source to destination. While this appealing solution offers significant advantages in terms of flexibility, it also introduces new challenges such as the correct configuration and ordering of SFs in the chain to satisfy overall security requirements. This paper presents a formal model conceived to enable the verification of correct policy enforcements in SFCs. Software tools based on the model can then be designed to cope with unwanted network behaviors (e.g., security flaws) deriving from incorrect interactions of SFs of the same SFC. 

The focus of this paper is to propose an integration between Internet of Things (IoT) and Video Surveillance, with the aim to satisfy the requirements of the future needs of Video Surveillance, and to accomplish a better use. IoT is a new technology in the sector of telecommunications. It is a network that contains physical objects, items, and devices, which are embedded with sensors and software, thus enabling the objects, and allowing for their data exchange. Video Surveillance systems collect and exchange the data which has been recorded by sensors and cameras and send it through the network. This paper proposes an innovative topology paradigm which could offer a better use of IoT technology in Video Surveillance systems. Furthermore, the contribution of these technologies provided by Internet of Things features in dealing with the basic types of Video Surveillance technology with the aim to improve their use and to have a better transmission of video data through the network. Additionally, there is a comparison between our proposed topology and relevant proposed topologies focusing on the security issue.

Software-defined networking is considered a promising new paradigm, enabling more reliable and formally verifiable communication networks. However, this paper shows that the separation of the control plane from the data plane, which lies at the heart of Software-Defined Networks (SDNs), introduces a new vulnerability which we call teleportation. An attacker (e.g., a malicious switch in the data plane or a host connected to the network) can use teleportation to transmit information via the control plane and bypass critical network functions in the data plane (e.g., a firewall), and to violate security policies as well as logical and even physical separations. This paper characterizes the design space for teleportation attacks theoretically, and then identifies four different teleportation techniques. We demonstrate and discuss how these techniques can be exploited for different attacks (e.g., exfiltrating confidential data at high rates), and also initiate the discussion of possible countermeasures. Generally, and given today's trend toward more intent-based networking, we believe that our findings are relevant beyond the use cases considered in this paper.

Many IoT devices are part of fixed critical infrastructure, where the mere act of moving an IoT device may constitute an attack. Moving pressure, chemical and radiation sensors in a factory can have devastating consequences. Relocating roadside speed sensors, or smart meters without knowledge of command and control center can similarly wreck havoc. Consequently, authenticating geolocation of IoT devices is an important problem. Unfortunately, an IoT device itself may be compromised by an adversary. Hence, location information from the IoT device cannot be trusted. Thus, we have to rely on infrastructure to obtain a proximal location. Infrastructure routers may similarly be compromised. Therefore, there must be a way to authenticate trusted routers remotely. Unfortunately, IP packets may be blocked, hijacked or forged by an adversary. Therefore IP packets are not trustworthy either. Thus, we resort to covert channels for authenticating Internet packet routers as an intermediate step towards proximal geolocation of IoT devices. Several techniques have been proposed in the literature to obtain the geolocation of an edge device, but it has been shown that a knowledgeable adversary can circumvent these techniques. In this paper, we survey the state-of-the-art geolocation techniques and corresponding adversarial countermeasures to evade geolocation to justify the use of covert channels on networks. We propose a technique for determining proximal geolocation using covert channel. Challenges and directions for future work are also explored.

Internet of things (IoT) is internetworking of various physical devices to provide a range of services and applications. IoT is a rapidly growing field, on an account of this; the security measurements for IoT should be at first concern. In the modern day world, the most emerging cyber-attack threat for IoT is ransomware attack. Ransomware is a kind of malware with the aim of rendering a victim's computer unusable or inaccessible, and then asking the user to pay a ransom to revert the destruction. In this paper we are evaluating ransomware attacks statistics for the past 2 years and the present year to estimate growth rate of the most emerging ransomware families from the last 3 years to evaluate most threatening ransomware attacks for IoT. Growth rate results shows that the number of attacks for Cryptowall and locky ransomware are notably increasing therefore, these ransomware families are potential threat to IoT. Moreover, we present a Cryptowall ransomware attack detection model based on the communication and behavioral study of Cryptowall for IoT environment. The proposed model observes incoming TCP/IP traffic through web proxy server then extracts TCP/IP header and uses command and control (C&C) server black listing to detect ransomware attacks.

Promoting data sharing between organisations is challenging, without the added concerns over having actions traced. Even with encrypted search capabilities, the entities digital location and downloaded information can be traced, leaking information to the hosting organisation. This is a problem for law enforcement and government agencies, where any information leakage is not acceptable, especially for investigations. Anonymous routing is a technique to stop a host learning which agency is accessing information. Many related works for anonymous routing have been proposed, but are designed for Internet traffic, and are over complicated for internal usage. A streaming design for circuit creation is proposed using elliptic curve cryptography. Allowing for a simple anonymous routing solution, which provides fast performance with source and destination anonymity to other organisations.

Recently, the increase of interconnectivity has led to a rising amount of IoT enabled devices in botnets. Such botnets are currently used for large scale DDoS attacks. To keep track with these malicious activities, Honeypots have proven to be a vital tool. We developed and set up a distributed and highly-scalable WAN Honeypot with an attached backend infrastructure for sophisticated processing of the gathered data. For the processed data to be understandable we designed a graphical frontend that displays all relevant information that has been obtained from the data. We group attacks originating in a short period of time in one source as sessions. This enriches the data and enables a more in-depth analysis. We produced common statistics like usernames, passwords, username/password combinations, password lengths, originating country and more. From the information gathered, we were able to identify common dictionaries used for brute-force login attacks and other more sophisticated statistics like login attempts per session and attack efficiency.

Building the Internet of Things requires deploying a huge number of objects with full or limited connectivity to the Internet. Given that these objects are exposed to attackers and generally not secured-by-design, it is essential to be able to update them, to patch their vulnerabilities and to prevent hackers from enrolling them into botnets. Ideally, the update infrastructure should implement the CIA triad properties, i.e., confidentiality, integrity and availability. In this work, we investigate how the use of a blockchain infrastructure can meet these requirements, with a focus on availability. In addition, we propose a peer-to-peer mechanism, to spread updates between objects that have limited access to the Internet. Finally, we give an overview of our ongoing prototype implementation.

Botnets have long been used for malicious purposes with huge economic costs to the society. With the proliferation of cheap but non-secure Internet-of-Things (IoT) devices generating large amounts of data, the potential for damage from botnets has increased manifold. There are several approaches to detect bots or botnets, though many traditional techniques are becoming less effective as botnets with centralized command & control structure are being replaced by peer-to-peer (P2P) botnets which are harder to detect. Several algorithms have been proposed in literature that use graph analysis or machine learning techniques to detect the overlay structure of P2P networks in communication graphs. Many of these algorithms however, depend on the availability of a universal communication graph or a communication graph aggregated from several ISPs, which is not likely to be available in reality. In real world deployments, significant gaps in communication graphs are expected and any solution proposed should be able to work with partial information. In this paper, we analyze the effectiveness of some community detection algorithms in detecting P2P botnets, especially with partial information. We show that the approach can work with only about half of the nodes reporting their communication graphs, with only small increase in detection errors.

Botnets have been a serious threat to the Internet security. With the constant sophistication and the resilience of them, a new trend has emerged, shifting botnets from the traditional desktop to the mobile environment. As in the desktop domain, detecting mobile botnets is essential to minimize the threat that they impose. Along the diverse set of strategies applied to detect these botnets, the ones that show the best and most generalized results involve discovering patterns in their anomalous behavior. In the mobile botnet field, one way to detect these patterns is by analyzing the operation parameters of this kind of applications. In this paper, we present an anomaly-based and host-based approach to detect mobile botnets. The proposed approach uses machine learning algorithms to identify anomalous behaviors in statistical features extracted from system calls. Using a self-generated dataset containing 13 families of mobile botnets and legitimate applications, we were able to test the performance of our approach in a close-to-reality scenario. The proposed approach achieved great results, including low false positive rates and high true detection rates.

Peer-to-peer (P2P) botnets have become one of the major threats in network security for serving as the infrastructure that responsible for various of cyber-crimes. Though a few existing work claimed to detect traditional botnets effectively, the problem of detecting P2P botnets involves more challenges. In this paper, we present PeerHunter, a community behavior analysis based method, which is capable of detecting botnets that communicate via a P2P structure. PeerHunter starts from a P2P hosts detection component. Then, it uses mutual contacts as the main feature to cluster bots into communities. Finally, it uses community behavior analysis to detect potential botnet communities and further identify bot candidates. Through extensive experiments with real and simulated network traces, PeerHunter can achieve very high detection rate and low false positives.

Protecting Critical Infrastructures (CIs) against contemporary cyber attacks has become a crucial as well as complex task. Modern attack campaigns, such as Advanced Persistent Threats (APTs), leverage weaknesses in the organization's business processes and exploit vulnerabilities of several systems to hit their target. Although their life-cycle can last for months, these campaigns typically go undetected until they achieve their goal. They usually aim at performing data exfiltration, cause service disruptions and can also undermine the safety of humans. Novel detection techniques and incident handling approaches are therefore required, to effectively protect CI's networks and timely react to this type of threats. Correlating large amounts of data, collected from a multitude of relevant sources, is necessary and sometimes required by national authorities to establish cyber situational awareness, and allow to promptly adopt suitable countermeasures in case of an attack. In this paper we propose three novel methods for security information correlation designed to discover relevant insights and support the establishment of cyber situational awareness.

Attacks against websites are increasing rapidly with the expansion of web services. An increasing number of diversified web services make it difficult to prevent such attacks due to many known vulnerabilities in websites. To overcome this problem, it is necessary to collect the most recent attacks using decoy web honeypots and to implement countermeasures against malicious threats. Web honeypots collect not only malicious accesses by attackers but also benign accesses such as those by web search crawlers. Thus, it is essential to develop a means of automatically identifying malicious accesses from mixed collected data including both malicious and benign accesses. Specifically, detecting vulnerability scanning, which is a preliminary process, is important for preventing attacks. In this study, we focused on classification of accesses for web crawling and vulnerability scanning since these accesses are too similar to be identified. We propose a feature vector including features of collective accesses, e.g., intervals of request arrivals and the dispersion of source port numbers, obtained with multiple honeypots deployed in different networks for classification. Through evaluation using data collected from 37 honeypots in a real network, we show that features of collective accesses are advantageous for vulnerability scanning and crawler classification.

Intrusion detection has been an active field of research for more than 35 years. Numerous systems had been built based on the two fundamental detection principles, knowledge-based and behavior-based detection. Anyway, having a look at day-to-day news about data breaches and successful attacks, detection effectiveness is still limited. Even more, heavy-weight intrusion detection systems cannot be installed in every endangered environment. For example, Industrial Control Systems are typically utilized for decades, charging off huge investments of companies. Thus, some of these systems have been in operation for years, but were designed afore without security in mind. Even worse, as systems often have connections to other networks and even the Internet nowadays, an adequate protection is mandatory, but integrating intrusion detection can be extremely difficult - or even impossible to date. We propose a new lightweight current-based IDS which is using a difficult to manipulate measurement base and verifiable ground truth. Focus of our system is providing intrusion detection for ICS and SCADA on a low-priced base, easy to integrate. Dr. WATTson, a prototype implemented based on our concept provides high detection and low false alarm rates.

Separation of network control from devices in Software Defined Network (SDN) allows for centralized implementation and management of security policies in a cloud computing environment. The ease of programmability also makes SDN a great platform implementation of various initiatives that involve application deployment, dynamic topology changes, and decentralized network management in a multi-tenant data center environment. Dynamic change of network topology, or host reconfiguration in such networks might require corresponding changes to the flow rules in the SDN based cloud environment. Verifying adherence of these new flow policies in the environment to the organizational security policies and ensuring a conflict free environment is especially challenging. In this paper, we extend the work on rule conflicts from a traditional environment to an SDN environment, introducing a new classification to describe conflicts stemming from cross-layer conflicts. Our framework ensures that in any SDN based cloud, flow rules do not have conflicts at any layer; thereby ensuring that changes to the environment do not lead to unintended consequences. We demonstrate the correctness, feasibility and scalability of our framework through a proof-of-concept prototype.

Controllers for software defined networks (SDNs) are quickly maturing to offer network operators more intuitive programming frameworks and greater abstractions for network application development. Likewise, many security solutions now exist within SDN environments for detecting and blocking clients who violate network policies. However, many of these solutions stop at triggering the security measure and give little thought to amending it. As a consequence, once the violation is addressed, no clear path exists for reinstating the flagged client beyond having the network operator reset the controller or manually implement a state change via an external command. This presents a burden for the network and its clients and administrators. Hence, we present a security policy transition framework for revoking security measures in an SDN environment once said measures are activated.

Defending key network infrastructure, such as Internet backbone links or the communication channels of critical infrastructure, is paramount, yet challenging. The inherently complex nature and quantity of network data impedes detecting attacks in real world settings. In this paper, we utilize features of network flows, characterized by their entropy, together with an extended version of the original Replicator Neural Network (RNN) and deep learning techniques to learn models of normality. This combination allows us to apply anomaly-based intrusion detection on arbitrarily large amounts of data and, consequently, large networks. Our approach is unsupervised and requires no labeled data. It also accurately detects network-wide anomalies without presuming that the training data is completely free of attacks. The evaluation of our intrusion detection method, on top of real network data, indicates that it can accurately detect resource exhaustion attacks and network profiling techniques of varying intensities. The developed method is efficient because a normality model can be learned by training an RNN within a few seconds only.