Comparative Analysis Based on Survey of DDOS Attacks’ Detection Techniques at Transport, Network, and Application Layers

Distributed Denial of Service (DDOS) is one of the most prevalent attacks and can be executed in diverse ways using various tools and codes. This makes it very difficult for the security researchers and engineers to come up with a rigorous and efficient security methodology. Even with thorough research, analysis, real time implementation, and application of the best mechanisms in test environments, there are various ways to exploit the smallest vulnerability within the system that gets overlooked while designing the defense mechanism. This paper presents a comprehensive survey of various methodologies implemented by researchers and engineers to detect DDOS attacks at network, transport, and application layers using comparative analysis. DDOS attacks are most prevalent on network, transport, and application layers justifying the need to focus on these three layers in the OSI model

Statistical anomaly denial of service and reconnaissance intrusion detection

This dissertation presents the architecture, methods and results of the Hierarchical Intrusion Detection Engine (HIDE) and the Reconnaissance Intrusion Detection System (RIDS); the former is denial-of-service (DoS) attack detector while the latter is a scan and probe (P&S) reconnaissance detector; both are statistical anomaly systems. The HIDE is a packet-oriented, observation-window using, hierarchical, multi-tier, anomaly based network intrusion detection system, which monitors several network traffic parameters simultaneously, constructs a 64-bin probability density function (PDF) for each, statistically compares it to a reference PDF of normal behavior using a similarity metric, then combines the results into an anomaly status vector that is classified by a neural network classifier. Three different data sets have been utilized to test the performance of HIDE; they are OPNET simulation data, DARPA\u2798 intrusion detection evaluation data and the CONEX TESTBED attack data. The results showed that HIDE can reliably detect DoS attacks with high accuracy and very low false alarm rates on all data sets. In particular, the investigation using the DARPA\u2798 data set yielded an overall total misclassification rate of 0.13%, false negative rate of 1.42%, and false positive rate of 0.090%; the latter implies a rate of only about 2.6 false alarms per day. The RIDS is a session oriented, statistical tool, that relies on training to model the parameters of its algorithms, capable of detecting even distributed stealthy reconnaissance attacks. It consists of two main functional modules or stages: the Reconnaissance Activity Profiler (RAP) and the Reconnaissance Alert Correlater (RAC). The RAP is a session-oriented module capable of detecting stealthy scanning and probing attacks, while the RAG is an alert-correlation module that fuses the RAP alerts into attack scenarios and discovers the distributed stealthy attack scenarios. RIDS has been evaluated against two data sets: (a) the DARPA\u2798 data, and (b) 3 weeks of experimental data generated using the CONEX TESTBED network. The RIDS has demonstrably achieved remarkable success; the false positive, false negative and misclassification rates found are low, less than 0.1%, for most reconnaissance attacks; they rise to about 6% for distributed highly stealthy attacks; the latter is a most challenging type of attack, which has been difficult to detect effectively until now

A Proactive Approach to Detect IoT Based Flooding Attacks by Using Software Defined Networks and Manufacturer Usage Descriptions

abstract: The advent of the Internet of Things (IoT) and its increasing appearances in Small Office/Home Office (SOHO) networks pose a unique issue to the availability and health of the Internet at large. Many of these devices are shipped insecurely, with poor default user and password credentials and oftentimes the general consumer does not have the technical knowledge of how they may secure their devices and networks. The many vulnerabilities of the IoT coupled with the immense number of existing devices provide opportunities for malicious actors to compromise such devices and use them in large scale distributed denial of service attacks, preventing legitimate users from using services and degrading the health of the Internet in general. This thesis presents an approach that leverages the benefits of an Internet Engineering Task Force (IETF) proposed standard named Manufacturer Usage Descriptions, that is used in conjunction with the concept of Software Defined Networks (SDN) in order to detect malicious traffic generated from IoT devices suspected of being utilized in coordinated flooding attacks. The approach then works towards the ability to detect these attacks at their sources through periodic monitoring of preemptively permitted flow rules and determining which of the flows within the permitted set are misbehaving by using an acceptable traffic range using Exponentially Weighted Moving Averages (EWMA).Dissertation/ThesisMasters Thesis Computer Science 201

ShutUp: End-to-End Containment of Unwanted Traffic

While the majority of Denial-of-Service (DoS) defense proposals assume a purely infrastructure-based architecture, some recent proposals suggest that the attacking endhost may be enlisted as part of the solution, through tamper-proof software, network-imposed incentives, or user altruism. While intriguing, these proposals ultimately raise the deployment bar by requiring both the infrastructure and endhosts to cooperate. In this paper, we explore the design of a pure end-to-end architecture based on tamper-proof endhost software implemented for instance with trusted platforms and virtual machines. We present the design of a ?Shutup Service?, whereby the recipient of unwanted traffic can ask the sender to slowdown or stop. We show that this service is effective in stopping DoS attacks, and in significantly slowing down other types of unwanted traffic such as worms. The Shutup service is incrementally deployable with buy-in from OS or antivirus vendors, requiring only minimal changes to the endhost software stack and no changes to the protocol stack. We show through experimentation that the service is effective and has little impact on legitimate traffic

A survey of defense mechanisms against distributed denial of service (DDOS) flooding attacks

Distributed Denial of Service (DDoS) flooding attacks are one of the biggest concerns for security professionals. DDoS flooding attacks are typically explicit attempts to disrupt legitimate users' access to services. Attackers usually gain access to a large number of computers by exploiting their vulnerabilities to set up attack armies (i.e., Botnets). Once an attack army has been set up, an attacker can invoke a coordinated, large-scale attack against one or more targets. Developing a comprehensive defense mechanism against identified and anticipated DDoS flooding attacks is a desired goal of the intrusion detection and prevention research community. However, the development of such a mechanism requires a comprehensive understanding of the problem and the techniques that have been used thus far in preventing, detecting, and responding to various DDoS flooding attacks. In this paper, we explore the scope of the DDoS flooding attack problem and attempts to combat it. We categorize the DDoS flooding attacks and classify existing countermeasures based on where and when they prevent, detect, and respond to the DDoS flooding attacks. Moreover, we highlight the need for a comprehensive distributed and collaborative defense approach. Our primary intention for this work is to stimulate the research community into developing creative, effective, efficient, and comprehensive prevention, detection, and response mechanisms that address the DDoS flooding problem before, during and after an actual attack. © 1998-2012 IEEE

Multi-Stage Detection Technique for DNS-Based Botnets

Domain Name System (DNS) is one of the most widely used protocols in the Internet. The main purpose of the DNS protocol is mapping user-friendly domain names to IP addresses. Unfortunately, many cyber criminals deploy the DNS protocol for malicious purposes, such as botnet communications. In this type of attack, the botmasters tunnel communications between the Command and Control (C&C) servers and the bot-infected machines within DNS request and response. Designing an effective approach for botnet detection has been done previously based on specific botnet types Since botnet communications are characterized by different features, botmasters may evade detection methods by modifying some of these features. This research aims to design and implement a multi-staged detection approach for Domain Generation Algorithm (DGA), Fast Flux Service Network, and Domain Flux-based botnets, as well as encrypted DNS tunneled-based botnets using the BRO Network Security Monitor. This approach is able to detect DNS-based botnet communications by relying on analyzing different techniques used for finding the C&C server, as well as encrypting the malicious traffic

Cyber Threat Predictive Analytics for Improving Cyber Supply Chain Security

Cyber Supply Chain (CSC) system is complex which involves different sub-systems performing various tasks. Security in supply chain is challenging due to the inherent vulnerabilities and threats from any part of the system which can be exploited at any point within the supply chain. This can cause a severe disruption on the overall business continuity. Therefore, it is paramount important to understand and predicate the threats so that organization can undertake necessary control measures for the supply chain security. Cyber Threat Intelligence (CTI) provides an intelligence analysis to discover unknown to known threats using various properties including threat actor skill and motivation, Tactics, Techniques, and Procedure (TT and P), and Indicator of Compromise (IoC). This paper aims to analyse and predicate threats to improve cyber supply chain security. We have applied Cyber Threat Intelligence (CTI) with Machine Learning (ML) techniques to analyse and predict the threats based on the CTI properties. That allows to identify the inherent CSC vulnerabilities so that appropriate control actions can be undertaken for the overall cybersecurity improvement. To demonstrate the applicability of our approach, CTI data is gathered and a number of ML algorithms, i.e., Logistic Regression (LG), Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT), are used to develop predictive analytics using the Microsoft Malware Prediction dataset. The experiment considers attack and TTP as input parameters and vulnerabilities and Indicators of compromise (IoC) as output parameters. The results relating to the prediction reveal that Spyware/Ransomware and spear phishing are the most predictable threats in CSC. We have also recommended relevant controls to tackle these threats. We advocate using CTI data for the ML predicate model for the overall CSC cyber security improvement

K-Means+ID3 and dependence tree methods for supervised anomaly detection

In this dissertation, we present two novel methods for supervised anomaly detection. The first method K-Means+ID3 performs supervised anomaly detection by partitioning the training data instances into k clusters using Euclidean distance similarity. Then, on each cluster representing a density region of normal or anomaly instances, an ID3 decision tree is built. The ID3 decision tree on each cluster refines the decision boundaries by learning the subgroups within a cluster. To obtain a final decision on detection, the k-Means and ID3 decision trees are combined using two rules: (1) the nearest neighbor rule; and (2) the nearest consensus rule. The performance of the K-Means+ID3 is demonstrated over three data sets: (1) network anomaly data, (2) Duffing equation data, and (3) mechanical system data, which contain measurements drawn from three distinct application domains of computer networks, an electronic circuit implementing a forced Duffing equation, and a mechanical mass beam system subjected to fatigue stress, respectively. Results show that the detection accuracy of the K-Means+ID3 method is as high as 96.24 percent on network anomaly data; the total accuracy is as high as 80.01 percent on mechanical system data; and 79.9 percent on Duffing equation data. Further, the performance of K-Means+ID3 is compared with individual k-Means and ID3 methods implemented for anomaly detection. The second method dependence tree based anomaly detection performs supervised anomaly detection using the Bayes classification rule. The class conditional probability densities in the Bayes classification rule are approximated by dependence trees, which represent second-order product approximations of probability densities. We derive the theoretical relationship between dependence tree classification error and Bayes error rate and show that the dependence tree approximation minimizes an upper bound on the Bayes error rate. To improve the classification performance of dependence tree based anomaly detection, we use supervised and unsupervised Maximum Relevance Minimum Redundancy (MRMR) feature selection method to select a set of features that optimally characterize class information. We derive the theoretical relationship between the Bayes error rate and the MRMR feature selection criterion and show that MRMR feature selection criterion minimizes an upper bound on the Bayes error rate. The performance of the dependence tree based anomaly detection method is demonstrated on the benchmark KDD Cup 1999 intrusion detection data set. Results show that the detection accuracies of the dependence tree based anomaly detection method are as high as 99.76 percent in detecting normal traffic, 93.88 percent in detecting denial-of-service attacks, 94.88 percent in detecting probing attacks, 86.40 percent in detecting user-to-root attacks, and 24.44 percent in detecting remote-to-login attacks. Further, the performance of dependence tree based anomaly detection method is compared with the performance of naïve Bayes and ID3 decision tree methods as well as with the performance of two anomaly detection methods reported in recent literature