Biblio

Use of internet increases day by day so securing network and data is a big issue. So, it is very important to maintain security to ensure safe and trusted communication of information between different organizations. Because of these IDS is a very useful component of computer and network security. IDS system is used by many organizations or industries to detect the weakness in their security, documenting previous attacks and threats and preventing all of this from violating security policies. Because of these advantages, this system is important in system security. In this paper, we find a multilevel solution for different approaches (attacks) based on intrusion detection system. In this paper, we identify different attacks and find the solutions for different type of attacks such as DDOS, SQL injection and Brute force attack. In this case, we use client-server architecture. To implement this we maintain profile of user and base on this we find normal user or attacker when system find that attack is present then it directly block the attack.

Web Application becomes the leading solution for the utilization of systems that need access globally, distributed, cost-effective, as well as the diversity of the content that can run on this technology. At the same time web application security have always been a major issue that must be considered due to the fact that 60% of Internet attacks targeting web application platform. One of the biggest impacts on this technology is Cross Site Scripting (XSS) attack, the most frequently occurred and are always in the TOP 10 list of Open Web Application Security Project (OWASP). Vulnerabilities in this attack occur in the absence of checking, testing, and the attention about secure coding practices. There are several alternatives to prevent the attacks that associated with this threat. Network Intrusion Detection System can be used as one solution to prevent the influence of XSS Attack. This paper investigates the XSS attack recognition and detection using regular expression pattern matching and a preprocessing method. Experiments are conducted on a testbed with the aim to reveal the behaviour of the attack.

As the most successful cryptocurrency to date, Bitcoin constitutes a target of choice for attackers. While many attack vectors have already been uncovered, one important vector has been left out though: attacking the currency via the Internet routing infrastructure itself. Indeed, by manipulating routing advertisements (BGP hijacks) or by naturally intercepting traffic, Autonomous Systems (ASes) can intercept and manipulate a large fraction of Bitcoin traffic. This paper presents the first taxonomy of routing attacks and their impact on Bitcoin, considering both small-scale attacks, targeting individual nodes, and large-scale attacks, targeting the network as a whole. While challenging, we show that two key properties make routing attacks practical: (i) the efficiency of routing manipulation; and (ii) the significant centralization of Bitcoin in terms of mining and routing. Specifically, we find that any network attacker can hijack few (\textbackslashtextless;100) BGP prefixes to isolate 50% of the mining power-even when considering that mining pools are heavily multi-homed. We also show that on-path network attackers can considerably slow down block propagation by interfering with few key Bitcoin messages. We demonstrate the feasibility of each attack against the deployed Bitcoin software. We also quantify their effectiveness on the current Bitcoin topology using data collected from a Bitcoin supernode combined with BGP routing data. The potential damage to Bitcoin is worrying. By isolating parts of the network or delaying block propagation, attackers can cause a significant amount of mining power to be wasted, leading to revenue losses and enabling a wide range of exploits such as double spending. To prevent such effects in practice, we provide both short and long-term countermeasures, some of which can be deployed immediately.

Bitcoin, one major virtual currency, attracts users' attention by its novel mode in recent years. With blockchain as its basic technique, Bitcoin possesses strong security features which anonymizes user's identity to protect their private information. However, some criminals utilize Bitcoin to do several illegal activities bringing in great security threat to the society. Therefore, it is necessary to get knowledge of the current trend of Bitcoin and make effort to de-anonymize. In this paper, we put forward and realize a system to analyze Bitcoin from two aspects: blockchain data and network traffic data. We resolve the blockchain data to analyze Bitcoin from the point of Bitcoin address while simulate Bitcoin P2P protocol to evaluate Bitcoin from the point of IP address. At last, with our system, we finish analyzing its current trends and tracing its transactions by putting some statistics on Bitcoin transactions and addresses, tracing the transaction flow and de-anonymizing some Bitcoin addresses to IPs.

Cyber attacks, (e.g., DDoS), on computers connected to the Internet occur everyday. A DDoS attack in 2016 that used “Mirai botnet” generated over 600 Gbit/s traffic, which was twice as that of last year. In view of this situation, we can no longer adequately protect our computers using current end-point security solutions and must therefore introduce a new method of protection that uses distributed nodes, e.g., routers. We propose an Autonomous and Distributed Internet Security (AIS) infrastructure that provides two key functions: first, filtering source address spoofing packets (proactive filter), and second, filtering malicious packets that are observed at the end point (reactive filter) at the closest malicious packets origins. We also propose three types of Multi-Layer Binding Routers (MLBRs) to realize these functions. We implemented the MLBRs and constructed experimental systems to simulate DDoS attacks. Results showed that all malicious packets could be filtered by using the AIS infrastructure.

Data from cyber logs can often be represented as a bipartite graph (e.g. internal IP-external IP, user-application, or client-server). State-of-the-art graph based anomaly detection often generalizes across all types of graphs — namely bipartite and non-bipartite. This confounds the interpretation and use of specific graph features such as degree, page rank, and eigencentrality that can provide a security analyst with rapid situational awareness of their network. Furthermore, graph algorithms applied to data collected from large, distributed enterprise scale networks require accompanying methods that allow them to scale to the data collected. In this paper, we provide a novel, scalable, directional graph projection framework that operates on cyber logs that can be represented as bipartite graphs. This framework computes directional graph projections and identifies a set of interpretable graph features that describe anomalies within each partite.

Distributed Denial of Service (DDoS) attacks serve to diminish the ability of the network to perform its intended function over time. The paper presents the design, implementation and analysis of a protocol based upon a technique for address agility called DDoS Resistant Multicast (DRM). After describing the our architecture and implementation we show an analysis that quantifies the overhead on network performance. We then present the Simple Agile RPL multiCAST (SARCAST), an Internet-of-Things routing protocol for DDoS protection. We have implemented and evaluated SARCAST in a working IoT operating system and testbed. Our results show that SARCAST provides very high levels of protection against DDoS attacks with virtually no impact on overall performance.

Software-defined networking (SDN) separates the control plane from underlying devices, and allows it to control the data plane from a global view. While SDN brings conveniences to management, it also introduces new security threats. Knowing reactive rules, attackers can launch denial-of-service (DoS) attacks by sending numerous rule-matched packets which trigger packet-in packets to overburden the controller. In this work, we present a novel method ``INferring SDN by Probing and Rule Extraction'' (INSPIRE) to discover the flow rules in SDN from probing packets. We evaluate the delay time from probing packets, classify them into defined classes, and infer the rules. This method involves three relevant steps: probing, clustering and rule inference. First, forged packets with various header fields are sent to measure processing and propagation time in the path. Second, it classifies the packets into multiple classes by using k-means clustering based on packet delay time. Finally, the apriori algorithm will find common header fields in the classes to infer the rules. We show how INSPIRE is able to infer flow rules via simulation, and the accuracy of inference can be up to 98.41% with very low false-positive rates.

Linking the growing IPv6 deployment to existing IPv4 addresses is an interesting field of research, be it for network forensics, structural analysis, or reconnaissance. In this work, we focus on classifying pairs of server IPv6 and IPv4 addresses as siblings, i.e., running on the same machine. Our methodology leverages active measurements of TCP timestamps and other network characteristics, which we measure against a diverse ground truth of 682 hosts. We define and extract a set of features, including estimation of variable (opposed to constant) remote clock skew. On these features, we train a manually crafted algorithm as well as a machine-learned decision tree. By conducting several measurement runs and training in cross-validation rounds, we aim to create models that generalize well and do not overfit our training data. We find both models to exceed 99% precision in train and test performance. We validate scalability by classifying 149k siblings in a large-scale measurement of 371k sibling candidates. We argue that this methodology, thoroughly cross-validated and likely to generalize well, can aid comparative studies of IPv6 and IPv4 behavior in the Internet. Striving for applicability and replicability, we release ready-to-use source code and raw data from our study.

High accurate time synchronization is very important for many applications and industrial environments. In a computer network, synchronization of time for connected devices is provided by the Precision Time Protocol (PTP), which in principal allows for device time synchronization down to microsecond level. However, PTP and network infrastructures are vulnerable to cyber-attacks, which can de-synchronize an entire network, leading to potentially devastating consequences. This paper will focus on the issue of internal attacks on time synchronization networks and discuss how counter-measures based on public key infrastructures, trusted platform modules, network intrusion detection systems and time synchronization supervisors can be adopted to defeat or at least detect such internal attacks.

Autonomous vehicles must communicate with each other effectively and securely to make robust decisions. However, today's Internet falls short in supporting efficient data delivery and strong data security, especially in a mobile ad-hoc environment. Named Data Networking (NDN), a new data-centric Internet architecture, provides a better foundation for secure data sharing among autonomous vehicles. We examine two potential threats, false data dissemination and vehicle tracking, in an NDN-based autonomous vehicular network. To detect false data, we propose a four-level hierarchical trust model and the associated naming scheme for vehicular data authentication. Moreover, we address vehicle tracking concerns using a pseudonym scheme to anonymize vehicle names and certificate issuing proxies to further protect vehicle identity. Finally, we implemented and evaluated our AutoNDN application on Raspberry Pi-based mini cars in a wireless environment.

A mobile ad hoc network (MANET) is a collection of mobile nodes that do not need to rely on a pre-existing network infrastructure or centralized administration. Securing MANETs is a serious concern as current research on MANETs continues to progress. Each node in a MANET acts as a router, forwarding data packets for other nodes and exchanging routing information between nodes. It is this intrinsic nature that introduces the serious security issues to routing protocols. A black hole attack is one of the well-known security threats for MANETs. A black hole is a security attack in which a malicious node absorbs all data packets by sending fake routing information and drops them without forwarding them. In order to defend against a black hole attack, in this paper we propose a new threshold-based black hole attack prevention method. To investigate the performance of the proposed method, we compared it with existing methods. Our simulation results show that the proposed method outperforms existing methods from the standpoints of black hole node detection rate, throughput, and packet delivery rate.

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.

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

Graph analysis can capture relationships between network entities and can be used to identify and rank anomalous hosts, users, or applications from various types of cyber logs. It is often the case that the data in the logs can be represented as a bipartite graph (e.g. internal IP-external IP, user-application, or client-server). State-of-the-art graph based anomaly detection often generalizes across all types of graphs — namely bipartite and non-bipartite. This confounds the interpretation and use of specific graph features such as degree, page rank, and eigencentrality that can provide a security analyst with situational awareness and even insights to potential attacks on enterprise scale networks. Furthermore, graph algorithms applied to data collected from large, distributed enterprise scale networks require accompanying methods that allow them to scale to the data collected. In this paper, we provide a novel, scalable, directional graph projection framework that operates on cyber logs that can be represented as bipartite graphs. We also present methodologies to further narrow returned results to anomalous/outlier cases that may be indicative of a cyber security event. This framework computes directional graph projections and identifies a set of interpretable graph features that describe anomalies within each partite.

The trend in computing is towards the use of FPGAs to improve performance at reduced costs. An indication of this is the adoption of FPGAs for data centre and server application acceleration by notable technological giants like Microsoft, Amazon, and Baidu. The continued protection of Intellectual Properties (IPs) on the FPGA has thus become both more important and challenging. To facilitate IP security, FPGA vendors have provided bitstream authentication and encryption. However, advancements in FPGA programming technology have engendered a bitstream manipulation technique like partial bitstream relocation (PBR), which is promising in terms of reducing bitstream storage cost and facilitating adaptability. Meanwhile, encrypted bitstreams are not amenable to PBR. In this paper, we present three methods for performing encrypted PBR with varying overheads of resources and time. These methods ensure that PBR can be applied to bitstreams without losing the protection of IPs.

Servers in a network are typically assigned a static identity. Static assignment of identities is a cornerstone for adversaries in finding targets. Moving Target Defense (MTD) mutates the environment to increase unpredictability for an attacker. On another side, Software Defined Networks (SDN) facilitate a global view of a network through a central control point. The potential of SDN can not only make network management flexible and convenient, but it can also assist MTD to enhance attack surface obfuscation. In this paper, we propose an effective framework for the prevention, detection, and mitigation of flooding-based Denial of Service (DoS) attacks. Our framework includes a light-weight SDN assisted MTD strategy for network reconnaissance protection and an efficient approach for tackling DoS attacks using Software Defined-Internet Exchange Point (SD-IXP). To assess the effectiveness of the MTD strategy and DoS mitigation scheme, we set two different experiments. Our results confirm the effectiveness of our framework. With the MTD strategy in place, at maximum, barely 16% reconnaissance attempts were successful while the DoS attacks were accurately detected with false alarm rate as low as 7.1%.

As the use of low-power and low resource embedded devices continues to increase dramatically with the introduction of new Internet of Things (IoT) devices, security techniques are necessary which are compatible with these devices. This research advances the knowledge in the area of cyber security for the IoT through the exploration of a moving target defense to apply for limiting the time attackers may conduct reconnaissance on embedded systems while considering the challenges presented from IoT devices such as resource and performance constraints. We introduce the design and optimizations for a Micro-Moving Target IPv6 Defense including a description of the modes of operation, needed protocols, and use of lightweight hash algorithms. We also detail the testing and validation possibilities including a Cooja simulation configuration, and describe the direction to further enhance and validate the security technique through large scale simulations and hardware testing followed by providing information on other future considerations.

This work presents the proof of concept implementation for the first hardware-based design of Moving Target Defense over IPv6 (MT6D) in full Register Transfer Level (RTL) logic, with future sights on an embedded Application-Specified Integrated Circuit (ASIC) implementation. Contributions are an IEEE 802.3 Ethernet stream-based in-line network packet processor with a specialized Complex Instruction Set Computer (CISC) instruction set architecture, RTL-based Network Time Protocol v4 synchronization, and a modular crypto engine. Traditional static network addressing allows attackers the incredible advantage of taking time to plan and execute attacks against a network. To counter, MT6D provides a network host obfuscation technique that offers network-based keyed access to specific hosts without altering existing network infrastructure and is an excellent technique for protecting the Internet of Things, IPv6 over Low Power Wireless Personal Area Networks, and high value globally routable IPv6 interfaces. This is done by crypto-graphically altering IPv6 network addresses every few seconds in a synchronous manner at all endpoints. A border gateway device can be used to intercept select packets to unobtrusively perform this action. Software driven implementations have posed many challenges, namely, constant code maintenance to remain compliant with all library and kernel dependencies, the need for a host computing platform, and less than optimal throughput. This work seeks to overcome these challenges in a lightweight system to be developed for practical wide deployment.

This paper will suggest a robust method for a network layer Moving Target Defense (MTD) using symmetric packet scheduling rules. The MTD is implemented and tested on a Supervisory Control and Data Acquisition (SCADA) network testbed. This method is shown to be efficient while providing security benefits to the issues faced by the static nature of SCADA networks. The proposed method is an automated tool that may provide defense in depth when be used in conjunction with other MTDs and traditional security devices.

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

This paper presents a wireless intrusion prevention tool for distributed denial of service attacks DDoS. This tool, called Wireless Distributed IPS WIDIP, uses a different collection of data to identify attackers from inside a private network. WIDIP blocks attackers and also propagates its information to other wireless routers that run the IPS. This communication behavior provides higher fault tolerance and stops attacks from different network endpoints. WIDIP also block network attackers at its first hop and thus reduce the malicious traffic near its source. Comparative tests of WIDIP with other two tools demonstrated that our tool reduce the delay of target response after attacks in application servers by 11%. In addition to reducing response time, WIDIP comparatively reduces the number of control messages on the network when compared to IREMAC.

As DDOS attacks interrupt internet services, DDOS tools confirm the effectiveness of the current attack. DDOS attack and countermeasures continue to increase in number and complexity. In this paper, we explore the scope of the DDoS flooding attack problem and attempts to combat it. A contemporary escalation of application layer distributed denial of service attacks on the web services has quickly transferred the focus of the research community from conventional network based denial of service. As a result, new genres of attacks were explored like HTTP GET Flood, HTTP POST Flood, Slowloris, R-U-Dead-Yet (RUDY), DNS etc. Also after a brief introduction to DDOS attacks, we discuss the characteristics of newly proposed application layer distributed denial of service attacks and embellish their impact on modern web services.

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

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