Biblio
Nowadays, the Internet of Things (IoT) is a consolidated reality. Smart homes are equipped with a growing number of IoT devices that capture more and more information about human beings lives. However, manufacturers paid little or no attention to security, so that various challenges are still in place. In this paper, we propose a novel approach to secure IoT systems that combines the concept of Security-by-Contract (S×C) with the Fog computing distributed paradigm. We define the pillars of our approach, namely the notions of IoT device contract, Fog node policy and contract-policy matching, the respective life-cycles, and the resulting S×C workflow. To better understand all the concepts of the S×C framework, and highlight its practical feasibility, we use a running case study based on a context-aware system deployed in a real smart home.
The paper presents a conceptual framework for security embedded task offloading requirements for IoT-Fog based future communication networks. The focus of the paper is to enumerate the need of embedded security requirements in this IoT-Fog paradigm including the middleware technologies in the overall architecture. Task offloading plays a significant role in the load balancing, energy and data management, security, reducing information processing and propagation latencies. The motivation behind introducing the embedded security is to meet the challenges of future smart networks including two main reasons namely; to improve the data protection and to minimize the internet disturbance and intrusiveness. We further discuss the middleware technologies such as cloudlets, mobile edge computing, micro datacenters, self-healing infrastructures and delay tolerant networks for security provision, optimized energy consumption and to reduce the latency. The paper introduces concepts of system virtualization and parallelism in IoT-Fog based systems and highlight the security features of the system. Some research opportunities and challenges are discussed to improve secure offloading from IoT into fog.
Internet of Things (IoT) stack models differ in their architecture, applications and needs. Hence, there are different approaches to apply IoT; for instance, it can be based on traditional data center or based on cloud computing. In fact, Cloud-based IoT is gaining more popularity due to its high scalability and cost effectiveness; hence, it is becoming the norm. However, Cloud is usually located far from the IoT devices and some recent research suggests using Fog-Based IoT by using a nearby light-weight middleware to bridge the gap and to provide the essential support and communication between devices, sensors, receptors and the servers. Therefore, Fog reduces centrality and provides local processing for faster analysis, especially for the time-sensitive applications. Thus, processing is done faster, giving the system flexibility for faster response time. Fog-Based Internet of Things security architecture should be suitable to the environment and provide the necessary measures to improve all security aspects with respect to the available resources and within performance constraints. In this work, we discuss some of these challenges, analyze performance of Fog based IoT and propose a security scheme based on MQTT protocol. Moreover, we present a discussion on security-performance tradeoffs.
With the advent of the big data era, information systems have exhibited some new features, including boundary obfuscation, system virtualization, unstructured and diversification of data types, and low coupling among function and data. These features not only lead to a big difference between big data technology (DT) and information technology (IT), but also promote the upgrading and evolution of network security technology. In response to these changes, in this paper we compare the characteristics between IT era and DT era, and then propose four DT security principles: privacy, integrity, traceability, and controllability, as well as active and dynamic defense strategy based on "propagation prediction, audit prediction, dynamic management and control". We further discuss the security challenges faced by DT and the corresponding assurance strategies. On this basis, the big data security technologies can be divided into four levels: elimination, continuation, improvement, and innovation. These technologies are analyzed, combed and explained according to six categories: access control, identification and authentication, data encryption, data privacy, intrusion prevention, security audit and disaster recovery. The results will support the evolution of security technologies in the DT era, the construction of big data platforms, the designation of security assurance strategies, and security technology choices suitable for big data.
Industrial production plants traditionally include sensors for monitoring or documenting processes, and actuators for enabling corrective actions in cases of misconfigurations, failures, or dangerous events. With the advent of the IoT, embedded controllers link these `things' to local networks that often are of low power wireless kind, and are interconnected via gateways to some cloud from the global Internet. Inter-networked sensors and actuators in the industrial IoT form a critical subsystem while frequently operating under harsh conditions. It is currently under debate how to approach inter-networking of critical industrial components in a safe and secure manner.In this paper, we analyze the potentials of ICN for providing a secure and robust networking solution for constrained controllers in industrial safety systems. We showcase hazardous gas sensing in widespread industrial environments, such as refineries, and compare with IP-based approaches such as CoAP and MQTT. Our findings indicate that the content-centric security model, as well as enhanced DoS resistance are important arguments for deploying Information Centric Networking in a safety-critical industrial IoT. Evaluation of the crypto efforts on the RIOT operating system for content security reveal its feasibility for common deployment scenarios.
Traditional firewalls, Intrusion Detection Systems(IDS) and network analytics tools extensively use the `flow' connection concept, consisting of five `tuples' of source and destination IP, ports and protocol type, for classification and management of network activities. By analysing flows, information can be obtained from TCP/IP fields and packet content to give an understanding of what is being transferred within a single connection. As networks have evolved to incorporate more connections and greater bandwidth, particularly from ``always on'' IoT devices and video and data streaming, so too have malicious network threats, whose communication methods have increased in sophistication. As a result, the concept of the 5 tuple flow in isolation is unable to detect such threats and malicious behaviours. This is due to factors such as the length of time and data required to understand the network traffic behaviour, which cannot be accomplished by observing a single connection. To alleviate this issue, this paper proposes the use of additional, two tuple and single tuple flow types to associate multiple 5 tuple communications, with generated metadata used to profile individual connnection behaviour. This proposed approach enables advanced linking of different connections and behaviours, developing a clearer picture as to what network activities have been taking place over a prolonged period of time. To demonstrate the capability of this approach, an expert system rule set has been developed to detect the presence of a multi-peered ZeuS botnet, which communicates by making multiple connections with multiple hosts, thus undetectable to standard IDS systems observing 5 tuple flow types in isolation. Finally, as the solution is rule based, this implementation operates in realtime and does not require post-processing and analytics of other research solutions. This paper aims to demonstrate possible applications for next generation firewalls and methods to acquire additional information from network traffic.
With the ever-growing occurrence of networking attacks, robust network security systems are essential to prevent and mitigate their harming effects. In recent years, machine learning-based systems have gain popularity for network security applications, usually considering the application of shallow models, where a set of expert handcrafted features are needed to pre-process the data before training. The main problem with this approach is that handcrafted features can fail to perform well given different kinds of scenarios and problems. Deep Learning models can solve this kind of issues using their ability to learn feature representations from input raw or basic, non-processed data. In this paper we explore the power of deep learning models on the specific problem of detection and classification of malware network traffic, using different representations for the input data. As a major advantage as compared to the state of the art, we consider raw measurements coming directly from the stream of monitored bytes as the input to the proposed models, and evaluate different raw-traffic feature representations, including packet and flow-level ones. Our results suggest that deep learning models can better capture the underlying statistics of malicious traffic as compared to classical, shallow-like models, even while operating in the dark, i.e., without any sort of expert handcrafted inputs.
Distributed Denial of Service (DDoS) attacks have two defense perspectives firstly, to defend your network, resources and other information assets from this disastrous attack. Secondly, to prevent your network to be the part of botnet (botforce) bondage to launch attacks on other networks and resources mainly be controlled from a control center. This work focuses on the development of a botnet prevention system for Internet of Things (IoT) that uses the benefits of both Software Defined Networking (SDN) and Distributed Blockchain (DBC). We simulate and analyze that using blockchain and SDN, how can detect and mitigate botnets and prevent our devices to play into the hands of attackers.
The server is an important for storing data, collected during the diagnostics of Smart Business Center (SBC) as a subsystem of Industrial Internet of Things including sensors, network equipment, components for start and storage of monitoring programs and technical diagnostics. The server is exposed most often to various kind of attacks, in particular, aimed at processor, interface system, random access memory. The goal of the paper is analyzing the methods of the SBC server protection from malicious actions, as well as the development and investigation of the Markov model of the server's functioning in the SBC network, taking into account the impact of DDoS-attacks.
The ever rising attacks on IT infrastructure, especially on networks has become the cause of anxiety for the IT professionals and the people venturing in the cyber-world. There are numerous instances wherein the vulnerabilities in the network has been exploited by the attackers leading to huge financial loss. Distributed denial of service (DDoS) is one of the most indirect security attack on computer networks. Many active computer bots or zombies start flooding the servers with requests, but due to its distributed nature throughout the Internet, it cannot simply be terminated at server side. Once the DDoS attack initiates, it causes huge overhead to the servers in terms of its processing capability and service delivery. Though, the study and analysis of request packets may help in distinguishing the legitimate users from among the malicious attackers but such detection becomes non-viable due to continuous flooding of packets on servers and eventually leads to denial of service to the authorized users. In the present research, we propose traffic flow and flow count variable based prevention mechanism with the difference in homogeneity. Its simplicity and practical approach facilitates the detection of DDoS attack at the early stage which helps in prevention of the attack and the subsequent damage. Further, simulation result based on different instances of time has been shown on T-value including generation of simple and harmonic homogeneity for observing the real time request difference and gaps.
Distributed Denial of Service (DDoS) strike is a malevolent undertaking to irritate regular action of a concentrated on server, organization or framework by overwhelming the goal or its incorporating establishment with a flood of Internet development. DDoS ambushes achieve feasibility by utilizing different exchanged off PC structures as wellsprings of strike action. Mishandled machines can join PCs and other masterminded resources, for instance, IoT contraptions. From an anomalous express, a DDoS attack looks like a vehicle convergence ceasing up with the road, shielding standard action from meeting up at its pined for objective.
Everyday., the DoS/DDoS attacks are increasing all over the world and the ways attackers are using changing continuously. This increase and variety on the attacks are affecting the governments, institutions, organizations and corporations in a bad way. Every successful attack is causing them to lose money and lose reputation in return. This paper presents an introduction to a method which can show what the attack and where the attack based on. This is tried to be achieved with using clustering algorithm DBSCAN on network traffic because of the change and variety in attack vectors.
In recent years, the attacks on systems have increased and among such attack is Distributed Denial of Service (DDoS) attack. The path identifiers (PIDs) used for inter-domain routing are static, which makes it easier the attack easier. To address this vulnerability, this paper addresses the usage of Dynamic Path Identifiers (D-PIDs) for routing. The PID of inter-domain path connector is kept oblivious and changes dynamically, thus making it difficult to attack the system. The prototype designed with major components like client, server and router analyses the outcome of D-PID usage instead of PIDs. The results show that, DDoS attacks can be effectively prevented if Dynamic Path Identifiers (D-PIDs) are used instead of Static Path Identifiers (PIDs).
Denial-of-Service attack (DoS attack) is an attack on network in which an attacker tries to disrupt the availability of network resources by overwhelming the target network with attack packets. In DoS attack it is typically done using a single source, and in a Distributed Denial-of-Service attack (DDoS attack), like the name suggests, multiple sources are used to flood the incoming traffic of victim. Typically, such attacks use vulnerabilities of Domain Name System (DNS) protocol and IP spoofing to disrupt the normal functioning of service provider or Internet user. The attacks involving DNS, or attacks exploiting vulnerabilities of DNS are known as DNS based DDOS attacks. Many of the proposed DNS based DDoS solutions try to prevent/mitigate such attacks using some intelligent non-``network layer'' (typically application layer) protocols. Utilizing the flexibility and programmability aspects of Software Defined Networks (SDN), via this proposed doctoral research it is intended to make underlying network intelligent enough so as to prevent DNS based DDoS attacks.
Distributed denial of service (DDoS) attacks is a serious cyberattack that exhausts target machine's processing capacity by sending a huge number of packets from hijacked machines. To minimize resource consumption caused by DDoS attacks, filtering attack packets at source machines is the best approach. Although many studies have explored the detection of DDoS attacks, few studies have proposed DDoS attack prevention schemes that work at source machines. We propose a reliable, lightweight, transparent, and flexible DDoS attack prevention scheme that works at source machines. In this scheme, we employ a hypervisor with a packet filtering mechanism on each managed machine to allow the administrator to easily and reliably suppress packet transmissions. To make the proposed scheme lightweight and transparent, we exploit a thin hypervisor that allows pass-through access to hardware (except for network devices) from the operating system, thereby reducing virtualization overhead and avoiding compromising user experience. To make the proposed scheme flexible, we exploit a configurable packet filtering mechanism with a guaranteed safe code execution mechanism that allows the administrator to provide a filtering policy as executable code. In this study, we implemented the proposed scheme using BitVisor and the Berkeley Packet Filter. Experimental results show that the proposed scheme can suppress arbitrary packet transmissions with negligible latency and throughput overhead compared to a bare metal system without filtering mechanisms.
The convergence of access networks in the fifth-generation (5G) evolution promises multi-tier networking infrastructures for the successes of various applications realizing the Internet-of-Everything era. However, in this context, the support of a massive number of connected devices also opens great opportunities for attackers to exploit these devices in illegal actions against their victims, especially within the distributed denial-of-services (DDoS) attacks. Nowadays, DDoS prevention still remains an open issue in term of performance improvement although there is a significant number of existing solutions have been proposed in the literature. In this paper, we investigate the advances of multi-access edge computing (MAEC), which is considered as one of the most important emerging technologies in 5G networks, in order to provide an effective DDoS prevention solution (referred to be MAEC-X). The proposed MAEC-X architecture and mechanism are developed as well as proved its effectiveness against DDoS attacks through intensive security analysis.