Biblio
In this paper, we propose a scheme to protect the Software Defined Network(SDN) controller from Distributed Denial-of-Service(DDoS) attacks. We first predict the amount of new requests for each openflow switch periodically based on Taylor series, and the requests will then be directed to the security gateway if the prediction value is beyond the threshold. The requests that caused the dramatic decrease of entropy will be filtered out and rules will be made in security gateway by our algorithm; the rules of these requests will be sent to the controller. The controller will send the rules to each switch to make them direct the flows matching with the rules to the honey pot. The simulation shows the averages of both false positive and false negative are less than 2%.
Cloud is the requirement of today's competitive world that demand flexible, agile and adaptable technology to be at par with rapidly changing IT industry. Cloud offers scalable, on-demand, pay-as-you-go services to enterprise and has hence become a part of growing trend of organizations IT service model. With emerging trend of cloud the security concerns have further increased and one of the biggest concerns related to cloud is DDoS attack. DDoS attack tends to exhaust all the available resources and leads to unavailability of services in cloud to legitimate users. In this paper the concept of fog computing is used, it is nothing but an extension to cloud computing that performs analysis at the edge of the network, i.e. bring intelligence at the edge of the network for quick real time decision making and reducing the amount of data that is forwarded to cloud. We have proposed a framework in which DDoS attack traffic is generated using different tools which is made to pass through fog defender to cloud. Furthermore, rules are applied on fog defender to detect and filter DDoS attack traffic targeted to cloud.
As the Internet of Things (IoT) continues to grow, there arises concerns and challenges with regard to the security and privacy of the IoT system. In this paper, we propose a FOg CompUting-based Security (FOCUS) system to address the security challenges in the IoT. The proposed FOCUS system leverages the virtual private network (VPN) to secure the access channel to the IoT devices. In addition, FOCUS adopts a challenge-response authentication to protect the VPN server against distributed denial of service (DDoS) attacks, which can further enhance the security of the IoT system. FOCUS is implemented in fog computing that is close to the end users, thus achieving a fast and efficient protection. We demonstrate FOCUS in a proof-of-concept prototype, and conduct experiments to evaluate its performance. The results show that FOCUS can effectively filter out malicious attacks with a very low response latency.
One of the biggest problems of today's internet technologies is cyber attacks. In this paper whether DDoS attacks will be determined by deep packet inspection. Initially packets are captured by listening of network traffic. Packet filtering was achieved at desired number and type. These packets are recorded to database to be analyzed, daily values and average values are compared by known attack patterns and will be determined whether a DDoS attack attempts in real time systems.
According to the new Tor network (6.0.5 version) can help the domestic users easily realize "over the wall", and of course criminals may use it to visit deep and dark website also. The paper analyzes the core technology of the new Tor network: the new flow obfuscation technology based on meek plug-in and real instance is used to verify the new Tor network's fast connectivity. On the basis of analyzing the traffic confusion mechanism and the network crime based on Tor, it puts forward some measures to prevent the using of Tor network to implement network crime.
With a large number of sensors and control units in networked systems, distributed support vector machines (DSVMs) play a fundamental role in scalable and efficient multi-sensor classification and prediction tasks. However, DSVMs are vulnerable to adversaries who can modify and generate data to deceive the system to misclassification and misprediction. This work aims to design defense strategies for DSVM learner against a potential adversary. We use a game-theoretic framework to capture the conflicting interests between the DSVM learner and the attacker. The Nash equilibrium of the game allows predicting the outcome of learning algorithms in adversarial environments, and enhancing the resilience of the machine learning through dynamic distributed algorithms. We develop a secure and resilient DSVM algorithm with rejection method, and show its resiliency against adversary with numerical experiments.
Mobile Ad hoc Network (MANET) is one of the most popular dynamic topology reconfigurable local wireless network standards. Distributed Denial of Services is one of the most challenging threats in such a network. Flooding attack is one of the forms of DDoS attack whereby certain nodes in the network miss-utilizes the allocated channel by flooding packets with very high packet rate to it's neighbors, causing a fast energy loss to the neighbors and causing other legitimate nodes a denial of routing and transmission services from these nodes. In this work we propose a novel link layer assessment based flooding attack detection and prevention method. MAC layer of the nodes analyzes the signal properties and incorporated into the routing table by a cross layer MAC/Network interface. Once a node is marked as a flooding node, it is blacklisted in the routing table and is communicated to MAC through Network/MAC cross layer interface. Results shows that the proposed technique produces more accurate flooding attack detection in comparison to current state of art statistical analysis based flooding attack detection by network layer.
Figuring innovations and development of web diminishes the exertion required for different procedures. Among them the most profited businesses are electronic frameworks, managing an account, showcasing, web based business and so on. This framework mostly includes the data trades ceaselessly starting with one host then onto the next. Amid this move there are such a variety of spots where the secrecy of the information and client gets loosed. Ordinarily the zone where there is greater likelihood of assault event is known as defenceless zones. Electronic framework association is one of such place where numerous clients performs there undertaking as indicated by the benefits allotted to them by the director. Here the aggressor makes the utilization of open ranges, for example, login or some different spots from where the noxious script is embedded into the framework. This scripts points towards trading off the security imperatives intended for the framework. Few of them identified with clients embedded scripts towards web communications are SQL infusion and cross webpage scripting (XSS). Such assaults must be distinguished and evacuated before they have an effect on the security and classification of the information. Amid the most recent couple of years different arrangements have been incorporated to the framework for making such security issues settled on time. Input approvals is one of the notable fields however experiences the issue of execution drops and constrained coordinating. Some other component, for example, disinfection and polluting will create high false report demonstrating the misclassified designs. At the center, both include string assessment and change investigation towards un-trusted hotspots for totally deciphering the effect and profundity of the assault. This work proposes an enhanced lead based assault discovery with specifically message fields for viably identifying the malevolent scripts. The work obstructs the ordinary access for malignant so- rce utilizing and hearty manage coordinating through unified vault which routinely gets refreshed. At the underlying level of assessment, the work appears to give a solid base to further research.
Code signing which at present is the only methodology of trusting a code that is distributed to others. It heavily relies on the security of the software providers private key. Attackers employ targeted attacks on the code signing infrastructure for stealing the signing keys which are used later for distributing malware in disguise of genuine software. Differentiating a malware from a benign software becomes extremely difficult once it gets signed by a trusted software providers private key as the operating systems implicitly trusts this signed code. In this paper, we analyze the growing menace of signed malware by examining several real world incidents and present a threat model for the current code signing infrastructure. We also propose a novel solution that prevents this issue of malicious code signing by requiring additional verification of the executable. We also present the serious threat it poses and it consequences. To our knowledge this is the first time this specific issue of Malicious code signing has been thoroughly studied and an implementable solution is proposed.
Malicious emails pose substantial threats to businesses. Whether it is a malware attachment or a URL leading to malware, exploitation or phishing, attackers have been employing emails as an effective way to gain a foothold inside organizations of all kinds. To combat email threats, especially targeted attacks, traditional signature- and rule-based email filtering as well as advanced sandboxing technology both have their own weaknesses. In this paper, we propose a predictive analysis approach that learns the differences between legit and malicious emails through static analysis, creates a machine learning model and makes detection and prediction on unseen emails effectively and efficiently. By comparing three different machine learning algorithms, our preliminary evaluation reveals that a Random Forests model performs the best.
With the rapid development of sophisticated attack techniques, individual security systems that base all of their decisions and actions of attack prevention and response on their own observations and knowledge become incompetent. To cope with this problem, collaborative security in which a set of security entities are coordinated to perform specific security actions is proposed in literature. In collaborative security schemes, multiple entities collaborate with each other by sharing threat evidence or analytics to make more effective decisions. Nevertheless, the anticipated information exchange raises privacy concerns, especially for those privacy-sensitive entities. In order to obtain a quantitative understanding of the fundamental tradeoff between the effectiveness of collaboration and the entities' privacy, a repeated two-layer single-leader multi-follower game is proposed in this work. Based on our game-theoretic analysis, the expected behaviors of both the attacker and the security entities are derived and the utility-privacy tradeoff curve is obtained. In addition, the existence of Nash equilibrium (NE) for the collaborative entities is proven, and an asynchronous dynamic update algorithm is proposed to compute the optimal collaboration strategies of the entities. Furthermore, the existence of Byzantine entities is considered and its influence is investigated. Finally, simulation results are presented to validate the analysis.
SDN is a new network framework which can be controlled and defined by software programming, and OpenFlow is the communication protocol between SDN controller plane and data plane. With centralized control of SDN, the network is more vulnerable encounter APT than traditional network. After deeply analyzing the process of APT at each stage in SDN, this paper proposes the APT detection method based on HMM, which can fully reflect the relationship between attack behavior and APT stage. Experiment shows that the method is more accurate to detect APT in SDN, and less overhead.
Over the years cybercriminals have misused the Domain Name System (DNS) - a critical component of the Internet - to gain profit. Despite this persisting trend, little empirical information about the security of Top-Level Domains (TLDs) and of the overall 'health' of the DNS ecosystem exists. In this paper, we present security metrics for this ecosystem and measure the operational values of such metrics using three representative phishing and malware datasets. We benchmark entire TLDs against the rest of the market. We explicitly distinguish these metrics from the idea of measuring security performance, because the measured values are driven by multiple factors, not just by the performance of the particular market player. We consider two types of security metrics: occurrence of abuse and persistence of abuse. In conjunction, they provide a good understanding of the overall health of a TLD. We demonstrate that attackers abuse a variety of free services with good reputation, affecting not only the reputation of those services, but of entire TLDs. We find that, when normalized by size, old TLDs like .com host more bad content than new generic TLDs. We propose a statistical regression model to analyze how the different properties of TLD intermediaries relate to abuse counts. We find that next to TLD size, abuse is positively associated with domain pricing (i.e. registries who provide free domain registrations witness more abuse). Last but not least, we observe a negative relation between the DNSSEC deployment rate and the count of phishing domains.
Over the years cybercriminals have misused the Domain Name System (DNS) - a critical component of the Internet - to gain profit. Despite this persisting trend, little empirical information about the security of Top-Level Domains (TLDs) and of the overall 'health' of the DNS ecosystem exists. In this paper, we present security metrics for this ecosystem and measure the operational values of such metrics using three representative phishing and malware datasets. We benchmark entire TLDs against the rest of the market. We explicitly distinguish these metrics from the idea of measuring security performance, because the measured values are driven by multiple factors, not just by the performance of the particular market player. We consider two types of security metrics: occurrence of abuse and persistence of abuse. In conjunction, they provide a good understanding of the overall health of a TLD. We demonstrate that attackers abuse a variety of free services with good reputation, affecting not only the reputation of those services, but of entire TLDs. We find that, when normalized by size, old TLDs like .com host more bad content than new generic TLDs. We propose a statistical regression model to analyze how the different properties of TLD intermediaries relate to abuse counts. We find that next to TLD size, abuse is positively associated with domain pricing (i.e. registries who provide free domain registrations witness more abuse). Last but not least, we observe a negative relation between the DNSSEC deployment rate and the count of phishing domains.
Distributed attacks originating from botnet-infected machines (bots) such as large-scale malware propagation campaigns orchestrated via spam emails can quickly affect other network infrastructures. As these attacks are made successful only by the fact that hundreds of infected machines engage in them collectively, their damage can be avoided if machines infected with a common botnet can be detected early rather than after an attack is launched. Prior studies have suggested that outgoing bot attacks are often preceded by other ``tell-tale'' malicious behaviour, such as communication with botnet controllers (C&C servers) that command botnets to carry out attacks. We postulate that observing similar behaviour occuring in a synchronised manner across multiple machines is an early indicator of a widespread infection of a single botnet, leading potentially to a large-scale, distributed attack. Intuitively, if we can detect such synchronised behaviour early enough on a few machines in the network, we can quickly contain the threat before an attack does any serious damage. In this work we present a measurement-driven analysis to validate this intuition. We empirically analyse the various stages of malicious behaviour that are observed in real botnet traffic, and carry out the first systematic study of the network behaviour that typically precedes outgoing bot attacks and is synchronised across multiple infected machines. We then implement as a proof-of-concept a set of analysers that monitor synchronisation in botnet communication to generate early infection and attack alerts. We show that with this approach, we can quickly detect nearly 80% of real-world spamming and port scanning attacks, and even demonstrate a novel capability of preventing these attacks altogether by predicting them before they are launched.
This paper studies the stability of event-triggered control systems subject to Denial-of-Service attacks. An improved method is provided to increase frequency and duration of the DoS attacks where closed-loop stability is not destroyed. A two-mode switching control method is adopted to maintain stability of event-triggered control systems in the presence of attacks. Moreover, this paper reveals the relationship between robustness of systems against DoS attacks and lower bound of the inter-event times, namely, enlarging the inter-execution time contributes to enhancing the robustness of the systems against DoS attacks. Finally, some simulations are presented to illustrate the efficiency and feasibility of the obtained results.
Hacker forums and other social platforms may contain vital information about cyber security threats. But using manual analysis to extract relevant threat information from these sources is a time consuming and error-prone process that requires a significant allocation of resources. In this paper, we explore the potential of Machine Learning methods to rapidly sift through hacker forums for relevant threat intelligence. Utilizing text data from a real hacker forum, we compared the text classification performance of Convolutional Neural Network methods against more traditional Machine Learning approaches. We found that traditional machine learning methods, such as Support Vector Machines, can yield high levels of performance that are on par with Convolutional Neural Network algorithms.
Cloud computing is a revolution in IT technology that provides scalable, virtualized on-demand resources to the end users with greater flexibility, less maintenance and reduced infrastructure cost. These resources are supervised by different management organizations and provided over Internet using known networking protocols, standards and formats. The underlying technologies and legacy protocols contain bugs and vulnerabilities that can open doors for intrusion by the attackers. Attacks as DDoS (Distributed Denial of Service) are ones of the most frequent that inflict serious damage and affect the cloud performance. In a DDoS attack, the attacker usually uses innocent compromised computers (called zombies) by taking advantages of known or unknown bugs and vulnerabilities to send a large number of packets from these already-captured zombies to a server. This may occupy a major portion of network bandwidth of the victim cloud infrastructures or consume much of the servers time. Thus, in this work, we designed a DDoS detection system based on the C.4.5 algorithm to mitigate the DDoS threat. This algorithm, coupled with signature detection techniques, generates a decision tree to perform automatic, effective detection of signatures attacks for DDoS flooding attacks. To validate our system, we selected other machine learning techniques and compared the obtained results.
In this paper, we describe an efficient methodology to guide investigators during network forensic analysis. To this end, we introduce the concept of core attack graph, a compact representation of the main routes an attacker can take towards specific network targets. Such compactness allows forensic investigators to focus their efforts on critical nodes that are more likely to be part of attack paths, thus reducing the overall number of nodes (devices, network privileges) that need to be examined. Nevertheless, core graphs also allow investigators to hierarchically explore the graph in order to retrieve different levels of summarised information. We have evaluated our approach over different network topologies varying parameters such as network size, density, and forensic evaluation threshold. Our results demonstrate that we can achieve the same level of accuracy provided by standard logical attack graphs while significantly reducing the exploration rate of the network.
Hackers create different types of Malware such as Trojans which they use to steal user-confidential information (e.g. credit card details) with a few simple commands, recent malware however has been created intelligently and in an uncontrolled size, which puts malware analysis as one of the top important subjects of information security. This paper proposes an efficient dynamic malware-detection method based on API similarity. This proposed method outperform the traditional signature-based detection method. The experiment evaluated 197 malware samples and the proposed method showed promising results of correctly identified malware.
Honeypots are servers or systems built to mimic critical parts of a network, distracting attackers while logging their information to develop attack profiles. This paper discusses the design and implementation of a honeypot disguised as a REpresentational State Transfer (REST) Application Programming Interface (API). We discuss the motivation for this work, design features of the honeypot, and experimental performance results under various traffic conditions. We also present analyses of both a distributed denial of service (DDoS) attack and a cross-site scripting (XSS) malware insertion attempt against this honeypot.
Deep web, a hidden and encrypted network that crawls beneath the surface web today has become a social hub for various criminals who carry out their crime through the cyber space and all the crime is being conducted and hosted on the Deep Web. This research paper is an effort to bring forth various techniques and ways in which an internet user can be safe online and protect his privacy through anonymity. Understanding how user's data and private information is phished and what are the risks of sharing personal information on social media.
Distributed Denial of Service (DDoS) attacks are some of the most persistent threats on the Internet today. The evolution of DDoS attacks calls for an in-depth analysis of those attacks. A better understanding of the attackers' behavior can provide insights to unveil patterns and strategies utilized by attackers. The prior art on the attackers' behavior analysis often falls in two aspects: it assumes that adversaries are static, and makes certain simplifying assumptions on their behavior, which often are not supported by real attack data. In this paper, we take a data-driven approach to designing and validating three DDoS attack models from temporal (e.g., attack magnitudes), spatial (e.g., attacker origin), and spatiotemporal (e.g., attack inter-launching time) perspectives. We design these models based on the analysis of traces consisting of more than 50,000 verified DDoS attacks from industrial mitigation operations. Each model is also validated by testing its effectiveness in accurately predicting future DDoS attacks. Comparisons against simple intuitive models further show that our models can more accurately capture the essential features of DDoS attacks.
The 6L0WPAN adaptation layer is widely used in many Internet of Things (IoT) and vehicular networking applications. The current IoT framework [1], which introduced 6LoWPAN to the TCP/IP model, does not specif the implementation for managing its received-fragments buffer. This paper looks into the effect of current implementations of buffer management strategies at 6LoWPAN's response in case of fragmentation-based, buffer reservation Denial of Service (DoS) attacks. The Packet Drop Rate (PDR) is used to analyze how successful the attacker is for each management technique. Our investigation uses different defence strategies, which include our implementation of the Split Buffer mechanism [2] and a modified version of this mechanism that we devise in this paper as well. In particular, we introduce dynamic calculation for the average time between consecutive fragments and the use of a list of previously dropped packets tags. NS3 is used to simulate all the implementations. Our results show that using a ``slotted'' buffer would enhance 6LoWPAN's response against these attacks. The simulations also provide an in-depth look at using scoring systems to manage buffer cleanups.