Biblio

Many enterprises and individuals prefer to outsource data to public cloud via various pricing approaches. One of the most widely-used approaches is the pay-as-you-go model, where the data owner hires public cloud to share data with data consumers, and only pays for the actually consumed services. To realize controllable and secure data sharing, ciphertext-policy attribute-based encryption (CP-ABE) is a suitable solution, which can provide fine-grained access control and encryption functionalities simultaneously. But there are some serious challenges when applying CP-ABE in pay-as-you-go. Firstly, the decryption cost in ABE is too heavy for data consumers. Secondly, ABE ciphertexts probably suffer distributed denial of services (DDoS) attacks, but there is no solution that can eliminate the security risk. At last, the data owner should audit resource consumption to guarantee the transparency of charge, while the existing method is inefficient. In this work, we propose a general construction named fully accountable ABE (FA-ABE), which simultaneously solves all the challenges by supporting all-sided accountability in the pay-as-you-go model. We formally define the security model and prove the security in the standard model. Also, we implement an instantiate construction with the self-developed library libabe. The experiment results indicate the efficiency and practicality of our construction.

The cybersecurity of the modern control system is becoming a critical issue to the cyber-physical systems (CPS). Mitigating potential cyberattacks in the control system is an important concern in the controller design to enhance the resilience of the overall system. This paper presents a novel robust control architecture for the PV converter system to mitigate the sensor and actuator attack and reduce the influence of the system uncertainty. The sensor and actuator attack is a vicious attack scenario when the attack signals are injected into the sensor and actuator in a CPS simultaneously. A p-synthesis robust control architecture is proposed to mitigate the sensor and actuator attack and limit the system uncertainty perturbations in a DC-DC photovoltaic (PV) converter. A new system state matrix and control architecture is presented by integrating the original system state, injected attack signals and system uncertainty perturbations. In the case study, the proposed μ-synthesis robust controller exhibits a robust performance in the face of the sensor and actuator attack.

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 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.

Protection from DDoS-attacks is one of the most urgent problems in the world of network technologies. And while protect systems has algorithms for detection and preventing DDoS attacks, there are still some unresolved problems. This article is devoted to the DDoS-attack called Pulse Wave. Providing a brief introduction to the world of network technologies and DDoS-attacks, in particular, aims at the algorithm for protecting against DDoS-attack Pulse Wave. The main goal of this article is the implementation of traffic classifier that adds rules for infected computers to put them into a separate queue with limited bandwidth. This approach reduces their load on the service and, thus, firewall neutralises the attack.

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.

Protecting critical infrastructure from cyberattacks by other nations is a matter of considerable concern. Can deterrence play a role in such protection? Can lessons from nuclear deterrence-the most elaborated and successful version of deterrence-be adapted to the cyber case? Currently, little overlap exists between the two, although that might change in the aftermath of an extensive, destructive cyberattack. The most effective way to protect the cyber-dependent infrastructure is a comprehensive defense (deterrence by denial), which was impractical in the nuclear regime. However, this approach presents challenges. Existing legal norms, particularly those related to controlling collateral damage, might provide some deterrence. Another option might be a new international agreement, but that would involve several difficult issues.

The question of whether strategic deterrence in cyberspace is achievable given the challenges of detection, attribution and credible retaliation is a topic of contention among military and civilian defense strategists. This paper examines the traditional strategic deterrence theory and its application to deterrence in cyberspace (the newly defined 5th battlespace domain, following land, air, sea and space domains), which is being used increasingly by nation-states and their proxies to achieve information dominance and to gain tactical and strategic economic and military advantage. It presents a taxonomy of cyberattacks that identifies which types of threats in the confidentiality, integrity, availability cybersecurity model triad present the greatest risk to nation-state economic and military security, including their political and social facets. The argument is presented that attacks on confidentiality cannot be subject to deterrence in the current international legal framework and that the focus of strategy needs to be applied to integrity and availability attacks. A potential cyberdeterrence strategy is put forth that can enhance national security against devastating cyberattacks through a credible declaratory retaliation capability that establishes red lines which may trigger a counter-strike against all identifiable responsible parties. The author believes such strategy can credibly influence nation-state threat actors who themselves exhibit serious vulnerabilities to cyber attacks from launching a devastating cyber first strike.

Computer crime is a topic prevalent in both the research literature and in industry, due to a number of recent high-profile cyber attacks on e-commerce organizations. While technical means for defending against internal and external hackers have been discussed at great length, researchers have shown a distinct preference towards understanding deterrence of the internal threat and have paid little attention to external deterrence. This paper uses the criminological thesis known as Broken Windows Theory to understand how external computer criminals might be deterred from attacking a particular organization. The theory's focus upon disorder as a precursor to crime is discussed, and the notion of decreasing public IS disorder to create the illusion of strong information systems security is examined. A case study of a victim e-commerce organization is reviewed in light of the theory and implications for research and practice are discussed.

What is the role of deterrence in an age where adept hackers can credibly hold strategic assets at risk? Do conventional frameworks of deterrence maintain their applicability and meaning against state actors in cyberspace? Is it possible to demonstrate credibility with either in-domain or cross-domain signaling or is cyberspace fundamentally ill-suited to the application of deterrence frameworks? Building on concepts from both rational deterrence theory and cognitive theories of deterrence this work attempts to leverage relevant examples from both within and beyond cyberspace to examine applicability of deterrence in the digital age and for digital tools in an effort to shift the conversation from Atoms to Bits and Bytes.

Software Defined Network (SDN) is a revolutionary idea to realize software-driven network with the separation of control and data planes. In essence, SDN addresses the problems faced by the traditional network architecture; however, it may as well expose the network to new attacks. Among other attacks, distributed denial of service (DDoS) attacks are hard to contain in such software-based networks. Existing DDoS mitigation techniques either lack in performance or jeopardize the accuracy of the attack detection. To fill the voids, we propose in this paper a machine learning-based DDoS mitigation technique for SDN. First, we create a model for DDoS detection in SDN using NSL-KDD dataset and then after training the model on this dataset, we use real DDoS attacks to assess our proposed model. Obtained results show that the proposed technique equates favorably to the current techniques with increased performance and accuracy.

Distributed Denial of Service (DDoS) attacks are intense and are targeted to major infrastructure, governments and military organizations in each country. There are a lot of mitigations about DDoS, and the concept of Content Delivery Network (CDN) has been able to avoid attacks on websites. However, since the existing CDN system is fundamentally centralized, it may be difficult to prevent DDoS. This paper describes the distributed CDN Schema using Private Blockchain which solves the problem of participation of existing transparent and unreliable nodes. This will explain DDoS mitigation that can be used by military and government agencies.

The emergence of Software Defined Network (SDN) and its promises in networking technology has gotten every stakeholder excited. However, it is believed that every technological development comes with its own challenges of which the most prominent in this case is security. This paper presents a real time detection of the distributed denial of service (DDoS) attacks on the SDN and a control method based on the sFlow mitigation technology. sFlow analyses samples of packets collected from the network traffic and generates handling rules to be sent to the controller in case of an attack detection. The implementation was done by emulating the network in Mininet which runs on a Virtual Machine (VM) and it was shown that the proposed method effectively detects and mitigates DDoS attacks.

Recently Distributed Denial-of-Service (DDoS) are becoming more and more sophisticated, which makes the existing defence systems not capable of tolerating by themselves against wide-ranging attacks. Thus, collaborative protection mitigation has become a needed alternative to extend defence mechanisms. However, the existing coordinated DDoS mitigation approaches either they require a complex configuration or are highly-priced. Blockchain technology offers a solution that reduces the complexity of signalling DDoS system, as well as a platform where many autonomous systems (Ass) can share hardware resources and defence capabilities for an effective DDoS defence. In this work, we also used a Deep learning DDoS detection system; we identify individual DDoS attack class and also define whether the incoming traffic is legitimate or attack. By classifying the attack traffic flow separately, our proposed mitigation technique could deny only the specific traffic causing the attack, instead of blocking all the traffic coming towards the victim(s).

Cloud computing is an assured progression inside the future of facts generation. It's far a sub-domain of network security. These days, many huge or small organizations are switching to cloud which will shop and arrange their facts. As a result, protection of cloud networks is the want of the hour. DDoS is a killer software for cloud computing environments on net today. It is a distributed denial of carrier. we will beat the ddos attacks if we have the enough assets. ddos attacks can be countered by means of dynamic allocation of the assets. In this paper the attack is detected as early as possible and prevention methods is done and also mitigation method is also implemented thus attack can be avoided before it may occur.

Vehicular ad hoc networks (VANETs) are eminent type of Mobile ad hoc Networks. The network created in VANETs is quite prone to security problem. In this work, a new mechanism is proposed to study the security of VANETs against DDoS attack. The proposed mechanism focuses on distributed denial of service attacks. The main idea of the paper is to detect the DDoS attack and mitigate it. The work consists of two stages, initially attack topology and network congestion is created. The second stage is to detect and mitigate the DDoS attack. The existing method is compared with the proposed method for mitigating DDoS attacks in VANETs. The existing solutions presented by the various researchers are also compared and analyzed. The solution for such kind of problem is provided which is used to detect and mitigate DDoS attack by using greedy approach. The network environment is created using NS-2. The results of simulation represent that the proposed approach is better in the terms of network packet loss, routing overhead and network throughput.

We are exploring new ways to analyze phishing attacks. To do this, we investigate the change in the dynamics of the power of phishing attacks. We also analyze the effectiveness of detection of phishing attacks. We are considering the possibility of using new tools for analyzing phishing attacks. As such tools, the methods of chaos theory and the ideology of wavelet coherence are used. The use of such analysis tools makes it possible to investigate the peculiarities of the phishing attacks occurrence, as well as methods for their identification effectiveness. This allows you to expand the scope of the analysis of phishing attacks. For analysis, we use real data about phishing attacks.

Phishing is a form of cybercrime where an attacker imitates a real person / institution by promoting them as an official person or entity through e-mail or other communication mediums. In this type of cyber attack, the attacker sends malicious links or attachments through phishing e-mails that can perform various functions, including capturing the login credentials or account information of the victim. These e-mails harm victims because of money loss and identity theft. In this study, a software called "Anti Phishing Simulator'' was developed, giving information about the detection problem of phishing and how to detect phishing emails. With this software, phishing and spam mails are detected by examining mail contents. Classification of spam words added to the database by Bayesian algorithm is provided.

Phishing as one of the most well-known cybercrime activities is a deception of online users to steal their personal or confidential information by impersonating a legitimate website. Several machine learning-based strategies have been proposed to detect phishing websites. These techniques are dependent on the features extracted from the website samples. However, few studies have actually considered efficient feature selection for detecting phishing attacks. In this work, we investigate an agreement on the definitive features which should be used in phishing detection. We apply Fuzzy Rough Set (FRS) theory as a tool to select most effective features from three benchmarked data sets. The selected features are fed into three often used classifiers for phishing detection. To evaluate the FRS feature selection in developing a generalizable phishing detection, the classifiers are trained by a separate out-of-sample data set of 14,000 website samples. The maximum F-measure gained by FRS feature selection is 95% using Random Forest classification. Also, there are 9 universal features selected by FRS over all the three data sets. The F-measure value using this universal feature set is approximately 93% which is a comparable result in contrast to the FRS performance. Since the universal feature set contains no features from third-part services, this finding implies that with no inquiry from external sources, we can gain a faster phishing detection which is also robust toward zero-day attacks.

Phishing is a security attack to acquire personal information like passwords, credit card details or other account details of a user by means of websites or emails. Phishing websites look similar to the legitimate ones which make it difficult for a layman to differentiate between them. As per the reports of Anti Phishing Working Group (APWG) published in December 2018, phishing against banking services and payment processor was high. Almost all the phishy URLs use HTTPS and use redirects to avoid getting detected. This paper presents a focused literature survey of methods available to detect phishing websites. A comparative study of the in-use anti-phishing tools was accomplished and their limitations were acknowledged. We analyzed the URL-based features used in the past to improve their definitions as per the current scenario which is our major contribution. Also, a step wise procedure of designing an anti-phishing model is discussed to construct an efficient framework which adds to our contribution. Observations made out of this study are stated along with recommendations on existing systems.