Biblio

A DDoS attack is a spiteful attempt to disrupt legitimate traffic to a server by overwhelming the target with a flood of requests from geographically dispersed systems. Today attackers prefer DDoS attack methods to disrupt target services as they generate GBs to TBs of random data to flood the target. In existing mitigation strategies, because of lack of resources and not having the flexibility to cope with attacks by themselves, they are not considered to be that effective. So effective DDoS mitigation techniques can be provided using emerging technologies such as blockchain and SDN(Software-Defined Networking). We propose an architecture where a smart contract is deployed in a private blockchain, which facilitates a collaborative DDoS mitigation architecture across multiple network domains. Blockchain application is used as an additional security service. With Blockchain, shared protection is enabled among all hosts. With help of smart contracts, rules are distributed among all hosts. In addition, SDN can effectively enable services and security policies dynamically. This mechanism provides ASes(Autonomous Systems) the possibility to deploy their own DPS(DDoS Prevention Service) and there is no need to transfer control of the network to the third party. This paper focuses on the challenges of protecting a hybridized enterprise from the ravages of rapidly evolving Distributed Denial of Service(DDoS) attack.

Mobile Ad hoc Network (MANET) routing is basic and route selection ought to be made faster before the node leaves the system. MANET routing Methods are intended to work in a friendly and satisfying condition which makes them helpless against different attacks. MANET is one of the most encouraging fields for innovative work of remote system. MANET has now turned out to be one of the most lively and dynamic field of communication among systems. A MANET is a self-sufficient gathering of mobile nodes that speak with one another over remote connections and coordinate in an appropriated way so as to give the fundamental system convenience without a fixed framework. MANET has transfer speed limitations yet it permits self-ruling communication of versatile clients over it. Because of regular node mobility, and along these lines change in route topology, the architecture of the system goes unpredicted after some time. In such a decentralized situation, secured route identification is a key task for communication among nodes. Trust calculation among nodes is done for involving trusted nodes in route discovery process. In this manuscript, a novel secure routing method is proposed which identifies route among trusted nodes and update the routing table info frequently because of dynamic topology of the network. The outcomes demonstrate that the proposed method takes better routing technique when compared with existing methods.

SDN is new networking concept which has revolutionized the network architecture in recent years. It decouples control plane from data plane. Architectural change provides re-programmability and centralized control management of the network. At the same time it also increases the complexity of underlying physical infrastructure of the network. Unfortunately, the centralized control of the network introduces new vulnerabilities and attacks. Attackers can exploit the limitation of centralized control by DDoS attack on control plane. The entire network can be compromised by DDoS attack. Based on packet entropy, a solution for mitigation of DDoS attack provided in the proposed scheme.

Anonymous Communication (AC) hides traffic patterns and protects message metadata from being leaked during message transmission. Many practical AC systems have been proposed aiming to reduce communication latency and support a large number of users. However, how to design AC systems which possess strong security property and at the same time achieve optimal performance (i.e., the lowest latency or highest horizontal scalability) has been a challenging problem. In this paper, we propose an ObliComm framework, which consists of six modular AC subroutines. We also present a strong security definition for AC, named oblivious communication, encompassing confidentiality, unobservability, and a new requirement sending-and-receiving operation hiding. The AC subroutines in ObliComm allow for modular construction of oblivious communication systems in different network topologies. All constructed systems satisfy oblivious communication definition and can be provably secure in the universal composability (UC) framework. Additionally, we model the relationship between the network topology and communication measurements by queuing theory, which enables the system's efficiency can be optimized and estimated by quantitative analysis and calculation. Through theoretical analyses and empirical experiments, we demonstrate the efficiency of our scheme and soundness of the queuing model.

Scalability is an important design factor for evaluating the performance of routing protocols as the network size or traffic load increases. One of the most appropriate design methods is to use geographic routing approach to ensure scalability. This paper describes a scalability study comparing Secure Region Based Geographic Routing (SRBGR) and Dynamic Window Secure Implicit Geographic Forwarding (DWSIGF) protocols in various network density scenarios based on an end-to-end delay performance metric. The simulation studies were conducted in MATLAB 2106b where the network densities were varied according to the network topology size with increasing traffic rates. The results showed that DWSIGF has a lower end-to-end delay as compared to SRBGR for both sparse (15.4%) and high density (63.3%) network scenarios.Despite SRBGR having good security features, there is a need to improve the performance of its end-to-end delay to fulfil the application requirements.

The current global Internet USES the TCP/IP protocol cluster, the current version is IPv4. The IPv4 is with 32-bit addresses, the maximum number of computers connected to the Internet in the world is 232. With the development of Internet of things, big data and cloud storage and other technologies, the limited address space defined by IPv4 has been exhausted. To expand the address space, the IETF designed the next generation IPv6 to replace IPv4. IPv6 using a 128-bit address length that provides almost unlimited addresses. However, with the development and application of the Internet of things, big data and cloud storage, IPv6 has some shortcomings in its addressing structure design; security and network compatibility, These technologies are gradually applied in recent years, the continuous development of new technologies application show that the IPv6 address structure design ideas have some fatal defects. This paper proposed a route to upgrade the original IPv4 by studying on the structure of IPv6 "spliced address", and point out the defects in the design of IPv6 interface ID and the potential problems such as security holes.

Honeypots have become an important tool for capturing attacks. Hybrid honeypots, including the front end and the back end, are widely used in research because of the scalability of the front end and the high interactivity of the back end. However, traditional hybrid honeypots have some problems that the flow control is difficult and topology simulation is not realistic. This paper proposes a new architecture based on SDN applied to the hybrid honeypot system for network topology simulation and attack traffic migration. Our system uses the good expansibility and controllability of the SDN controller to simulate a large and realistic network to attract attackers and redirect high-level attacks to a high-interaction honeypot for attack capture and further analysis. It improves the deficiencies in the network spoofing technology and flow control technology in the traditional honeynet. Finally, we set up the experimental environment on the mininet and verified the mechanism. The test results show that the system is more intelligent and the traffic migration is more stealthy.

As the dynamics of cyber warfare continue to change, it is very important to be aware of the issues currently confronting cyberspace. One threat which continues to grow in the danger it poses to cyber security are botnets. Botnets can launch massive Distributed Denial of Service (DDoS) attacks against internet connected hosts anonymously, undertake intricate spam campaigns, launch mass financial fraud campaigns, and even manipulate public opinion via social media bots. The network topology and technology undergirding each botnet varies greatly, as do the motivations commonly behind such networks. Furthermore, as botnets have continued to evolve, many newer ones demonstrate increased levels of anonymity and sophistication, making it more difficult to effectively counter them. Increases in the production of vulnerable Internet of Things (IoT) devices has made it easier for malicious actors to quickly assemble sizable botnets. Because of this, the steps necessary to stop botnets also vary, and in some cases, it may be extremely difficult to effectively defeat a fully functional and sophisticated botnet. While in some cases, the infrastructure supporting the botnet can be targeted and remotely disabled, other cases require the physical assistance of law enforcement to shut down the botnet. In the latter case, it is often a significant challenge to cheaply end a botnet. On the other hand, there are many steps and mitigations that can be taken by end-users to prevent their own devices from becoming part of a botnet. Many of these solutions involve implementing basic cybersecurity practices like installing firewalls and changing default passwords. More sophisticated botnets may require similarly sophisticated intrusion detection systems, to detect and remove malicious infections. Much research has gone into such systems and in recent years many researchers have begun to implement machine learning techniques to defeat botnets. This paper is intended present a review on botnet evolution, trends and mitigations, and offer related examples and research to provide the reader with quick access to a broad understanding of the issues at hand.

Differential privacy is heavily used in privacy protection due to it provides strong protection against private data. The existing differential privacy scheme mainly implements the privacy protection of nodes or edges in the network by perturbing the data query results. Most of them cannot meet the privacy protection requirements of multiple types of information. In order to overcome these issues, a differential privacy security mechanism with average path length (APL) query is proposed in this paper, which realize the privacy protection of both network vertices and edge weights. Firstly, by describing APL, the reasons for choosing this attribute as the query function are analyzed. Secondly, global sensitivity of APL query under the need of node privacy protection and edge-weighted privacy protection is proved. Finally, the relationship between data availability and privacy control parameters in differential privacy is analyzed through experiments.

The smart grid consists of two parts, one is the physical power grid, the other is the information network. In order to study the cascading failure, the vulnerability analysis of the smart grid is done under a kind of community attack style in this paper. Two types of information networks are considered, i.e. topology consistency and scale-free cyber networks, respectively. The concept of control center is presented and the controllable power nodes and observable power lines are defined. Minimum load reduction model(MLRM) is given and described as a linear programming problem. A index is introduced to assess the vulnerability. New England 39 nodes system is applied to simulate the cascading failure process to demonstrate the effectiveness of the proposed MLRM where community the attack methods include attack the power lines among and in power communities.

Despite the wide of range of research and technologies that deal with the problem of routing in computer networks, there remains a gap between the level of network hardware administration and the level of business requirements and constraints. Not much has been accomplished in literature in order to have a direct enforcement of such requirements on the network. This paper presents a new solution in specifying and directly enforcing security policies to control the routing configuration in a software-defined network by using Row-Level Security checks which enable fine-grained security policies on individual rows in database tables. We show, as a first step, how a specific class of such policies, namely multilevel security policies, can be enforced on a database-defined network, which presents an abstraction of a network's configuration as a set of database tables. We show that such policies can be used to control the flow of data in the network either in an upward or downward manner.

Software Defined Network (SDN) is a revolutionary networking paradigm which provides the flexibility of programming the network interface as per the need and demand of the user. Software Defined Network (SDN) is independent of vendor specific hardware or protocols and offers the easy extensions in the networking. A customized network as per on user demand facilitates communication control via a single entity i.e. SDN controller. Due to this SDN Controller has become more vulnerable to SDN security attacks and more specifically a single point of failure. It is worth noticing that vulnerabilities were identified because of customized applications which are semi-independent of underlying network infrastructure. No doubt, SDN has provided numerous benefits like breaking vendor lock-ins, reducing overhead cost, easy innovations, increasing programmability among devices, introducing new features and so on. But security of SDN cannot be neglected and it has become a major topic of debate. The communication channel used in SDN is OpenFlow which has made TLS implementation an optional approach in SDN. TLS adoption is important and still vulnerable. This paper focuses on making SDN OpenFlow communication more secure by following extended TLS support and defensive algorithm.

The supervisory control and data acquisition (SCADA) network in a smart grid requires to be reliable and efficient to transmit real-time data to the controller. Introducing SDN into a SCADA network helps in deploying novel grid control operations, as well as, their management. As the overall network cannot be transformed to have only SDN-enabled devices overnight because of budget constraints, a systematic deployment methodology is needed. In this work, we present a framework, named SDNSynth, that can design a hybrid network consisting of both legacy forwarding devices and programmable SDN-enabled switches. The design satisfies the resiliency requirements of the SCADA network, which are specified with respect to a set of identified threat vectors. The deployment plan primarily includes the best placements of the SDN-enabled switches. The plan may include one or more links to be installed newly. We model and implement the SDNSynth framework that includes the satisfaction of several requirements and constraints involved in resilient operation of the SCADA. It uses satisfiability modulo theories (SMT) for encoding the synthesis model and solving it. We demonstrate SDNSynth on a case study and evaluate its performance on different synthetic SCADA systems.

In recent years, mobile social networks (MSNs) have developed rapidly and their application fields are becoming more and more widespread. Due to the continuous movement of nodes in mobile social networks, the network topology is very unstable. How to ensure the credibility of network communication is a subject worth studying. In this paper, based on the characteristics of mobile social networks, the definition of trust level is introduced into the DSR routing protocol, and a trusted DSR routing mechanism (TDR) is proposed. The scheme combines the sliding window model to design the calculation method of trust level between nodes and path trust level. The nodes in the network participate in the routing process according to their trust level. When the source node receives multiple routes carried by the response, the appropriate trusted path is selected according to the path trust level. Through simulation analysis, compared with the original DSR protocol, the TDR protocol improves the performance of average delay, route cost and packet delivery fraction, and verifies the reliability and credibility of the TDR protocol.

Network virtualization is a fundamental technology for datacenters and upcoming wireless communications (e.g., 5G). It takes advantage of software-defined networking (SDN) that provides efficient network management by converting networking fabrics into SDN-capable devices. Moreover, white-box switches, which provide flexible and fast packet processing, are broadly deployed in commercial datacenters. A white-box switch requires a specific and restricted packet processing pipeline; however, to date, there has been no SDN-based network hypervisor that can support the pipeline of white-box switches. Therefore, in this paper, we propose WhiteVisor: a network hypervisor which can support the physical network composed of white-box switches. WhiteVisor converts a flow rule from the virtual network into a packet processing pipeline compatible with the white-box switch. We implement the prototype herein and show its feasibility and effectiveness with pipeline conversion and overhead.

We consider the problem of attack detection for IoT networks based only on passively collected network parameters. For the first time in the literature, we develop a blind attack detection method based on data conformity evaluation. Network parameters collected passively, are converted to their conformity values through iterative projections on refined L1-norm tensor subspaces. We demonstrate our algorithmic development in a case study for a simulated star topology network. Type of attack, affected devices, as well as, attack time frame can be easily identified.

In Cloud Computing Environment, using only static security measures didn't mitigate the attack considerably. Hence, deployment of sophisticated methods by the attackers to understand the network topology of complex network makes the task easier. For this reason, the use of dynamic security measure as virtual machine (VM) migration increases uncertainty to locate a virtual machine in a dynamic attack surface. Although this, not all VM's migration enhances security. Indeed, the destination server to host the VM should be selected precisely in order to avoid externality and attack at the same time. In this paper, we model migration in cloud environment by using continuous Markov Chain. Then, we analyze the probability of a VM to be compromised based on the destination server parameters. Finally, we provide some numerical results to show the effectiveness of our approach in term of avoiding intrusion.

Warnings happen a lot in real transmission networks. These warnings can affect people's lives. It is significant to analyze the alarm association rules in the network. Many algorithms can help solve this problem but not considering the actual physical significance. Therefore, in this study, we mine the association rules in warning weblogs based on a sequential mining algorithm (GSP) with topology structure. We define a topology constraint from network physical connection data. Under the topology constraint, network nodes have topology relation if they are directly connected or have a common adjacency node. In addition, due to the large amount of data, we implement the hash-tree search method to improve the mining efficiency. The theoretical solution is feasible and the simulation results verify our method. In simulation, the topology constraint improves the accuracy for 86%-96% and decreases the run time greatly at the same time. The hash-tree based mining results show that hash tree efficiency improvements are in 3-30% while the number of patterns remains unchanged. In conclusion, using our method can mine association rules efficiently and accurately in warning weblogs.

Coupling regular topologies with optimized routing algorithms is key in pushing the performance of interconnection networks of HPC systems. In this paper we present Dmodc, a fast deterministic routing algorithm for Parallel Generalized Fat-Trees (PGFTs) which minimizes congestion risk even under massive topology degradation caused by equipment failure. It applies a modulo-based computation of forwarding tables among switches closer to the destination, using only knowledge of subtrees for pre-modulo division. Dmodc allows complete re-routing of topologies with tens of thousands of nodes in less than a second, which greatly helps centralized fabric management react to faults with high-quality routing tables and no impact to running applications in current and future very large-scale HPC clusters. We compare Dmodc against routing algorithms available in the InfiniBand control software (OpenSM) first for routing execution time to show feasibility at scale, and then for congestion risk under degradation to demonstrate robustness. The latter comparison is done using static analysis of routing tables under random permutation (RP), shift permutation (SP) and all-to-all (A2A) traffic patterns. Results for Dmodc show A2A and RP congestion risks similar under heavy degradation as the most stable algorithms compared, and near-optimal SP congestion risk up to 1% of random degradation.

Adversarial attacks can affect the performance of existing deep learning models. With the increased interest in graph based machine learning techniques, there have been investigations which suggest that these models are also vulnerable to attacks. In particular, corruptions of the graph topology can degrade the performance of graph based learning algorithms severely. This is due to the fact that the prediction capability of these algorithms relies mostly on the similarity structure imposed by the graph connectivity. Therefore, detecting the location of the corruption and correcting the induced errors becomes crucial. There has been some recent work which tackles the detection problem, however these methods do not address the effect of the attack on the downstream learning task. In this work, we propose an algorithm that uses node copying to mitigate the degradation in classification that is caused by adversarial attacks. The proposed methodology is applied only after the model for the downstream task is trained and the added computation cost scales well for large graphs. Experimental results show the effectiveness of our approach for several real world datasets.

Realizing the importance of the concept of “smart city” and its impact on the quality of life, many infrastructures, such as power plants, began their digital transformation process by leveraging modern computing and advanced communication technologies. Unfortunately, by increasing the number of connections, power plants become more and more vulnerable and also an attractive target for cyber-physical attacks. The analysis of interdependencies among system components reveals interdependent connections, and facilitates the identification of those among them that are in need of special protection. In this paper, we review the recent literature which utilizes graph-based models and network-based models to study these interdependencies. A comprehensive overview, based on the main features of the systems including communication direction, control parameters, research target, scalability, security and safety, is presented. We also assess the computational complexity associated with the approaches presented in the reviewed papers, and we use this metric to assess the scalability of the approaches.

Common vulnerability scoring system (CVSS) is an industry standard that can assess the vulnerability of nodes in traditional computer systems. The metrics computed by CVSS would determine critical nodes and attack paths. However, traditional IT security models would not fit IoT embedded networks due to distinct nature and unique characteristics of IoT systems. This paper analyses the application of CVSS for IoT embedded systems and proposes an improved vulnerability scoring system based on CVSS v3 framework. The proposed framework, named CVSSIoT, is applied to a realistic IT supply chain system and the results are compared with the actual vulnerabilities from the national vulnerability database. The comparison result validates the proposed model. CVSSIoT is not only effective, simple and capable of vulnerability evaluation for traditional IT system, but also exploits unique characteristics of IoT devices.

Software Defined Networking (SDN) is very popular due to the benefits it provides such as scalability, flexibility, monitoring, and ease of innovation. However, it needs to be properly protected from security threats. One major attack that plagues the SDN network is the distributed denial-of-service (DDoS) attack. There are several approaches to prevent the DDoS attack in an SDN network. We have evaluated a few machine learning techniques, i.e., J48, Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN), to detect and block the DDoS attack in an SDN network. The evaluation process involved training and selecting the best model for the proposed network and applying it in a mitigation and prevention script to detect and mitigate attacks. The results showed that J48 performs better than the other evaluated algorithms, especially in terms of training and testing time.

Nowadays, many applications involve big data and big data analysis methods appear in many fields. As a preliminary attempt to solve the challenge of big data analysis, this paper presents a distributed online learning algorithm based on differential privacy. Since online learning can effectively process sensitive data, we introduce the concept of differential privacy in distributed online learning algorithms, with the aim at ensuring data privacy during online learning to prevent adversarial nodes from inferring any important data information. In particular, for different adversary models, we consider different type graphs to tolerate a limited number of adversaries near each regular node or tolerate a global limited number of adversaries.

We propose a distributed machine-learning architecture to predict trustworthiness of sensor services in Mobile Edge Computing (MEC) based Internet of Things (IoT) services, which aligns well with the goals of MEC and requirements of modern IoT systems. The proposed machine-learning architecture models training a distributed trust prediction model over a topology of MEC-environments as a Network Lasso problem, which allows simultaneous clustering and optimization on large-scale networked-graphs. We then attempt to solve it using Alternate Direction Method of Multipliers (ADMM) in a way that makes it suitable for MEC-based IoT systems. We present analytical and simulation results to show the validity and efficiency of the proposed solution.