Evaluation of Machine Learning Algorithms for Intrusion Detection System

Intrusion detection system (IDS) is one of the implemented solutions against harmful attacks. Furthermore, attackers always keep changing their tools and techniques. However, implementing an accepted IDS system is also a challenging task. In this paper, several experiments have been performed and evaluated to assess various machine learning classifiers based on KDD intrusion dataset. It succeeded to compute several performance metrics in order to evaluate the selected classifiers. The focus was on false negative and false positive performance metrics in order to enhance the detection rate of the intrusion detection system. The implemented experiments demonstrated that the decision table classifier achieved the lowest value of false negative while the random forest classifier has achieved the highest average accuracy rate

A Characterization of Cybersecurity Posture from Network Telescope Data

Data-driven understanding of cybersecurity posture is an important problem that has not been adequately explored. In this paper, we analyze some real data collected by CAIDA's network telescope during the month of March 2013. We propose to formalize the concept of cybersecurity posture from the perspectives of three kinds of time series: the number of victims (i.e., telescope IP addresses that are attacked), the number of attackers that are observed by the telescope, and the number of attacks that are observed by the telescope. Characterizing cybersecurity posture therefore becomes investigating the phenomena and statistical properties exhibited by these time series, and explaining their cybersecurity meanings. For example, we propose the concept of {\em sweep-time}, and show that sweep-time should be modeled by stochastic process, rather than random variable. We report that the number of attackers (and attacks) from a certain country dominates the total number of attackers (and attacks) that are observed by the telescope. We also show that substantially smaller network telescopes might not be as useful as a large telescope

An Efficient Intrusion Detection Approach Utilizing Various WEKA Classifiers

Detection of Intrusion is an essential expertise business segment as well as a dynamic area of study and expansion caused by its requirement. Modern day intrusion detection systems still have these limitations of time sensitivity. The main requirement is to develop a system which is able of handling large volume of network data to detect attacks more accurately and proactively. Research conducted by on the KDDCUP99 dataset resulted in a various set of attributes for each of the four major attack types. Without reducing the number of features, detecting attack patterns within the data is more difficult for rule generation, forecasting, or classification. The goal of this research is to present a new method that Compare results of appropriately categorized and inaccurately categorized as proportions and the features chosen. In this research paper we explained our approach “An Efficient Intrusion Detection Approach Utilizing Various WEKA Classifiers” which is proposed to enhance the competence of recognition of intrusion employing different WEKA classifiers on processed KDDCUP99 dataset. During the experiment we employed Adaboost, J48, JRip, NaiveBayes and Random Tree classifiers to categorize the different attacks from the processed KDDCUP99. Keywords: Classifier, Data Mining, IDS, Network Security, Attacks, Cyber Securit

Applications of Machine Learning to Threat Intelligence, Intrusion Detection and Malware

Artificial Intelligence (AI) and Machine Learning (ML) are emerging technologies with applications to many fields. This paper is a survey of use cases of ML for threat intelligence, intrusion detection, and malware analysis and detection. Threat intelligence, especially attack attribution, can benefit from the use of ML classification. False positives from rule-based intrusion detection systems can be reduced with the use of ML models. Malware analysis and classification can be made easier by developing ML frameworks to distill similarities between the malicious programs. Adversarial machine learning will also be discussed, because while ML can be used to solve problems or reduce analyst workload, it also introduces new attack surfaces

Secure Routing in Wireless Mesh Networks

Wireless mesh networks (WMNs) have emerged as a promising concept to meet the challenges in next-generation networks such as providing flexible, adaptive, and reconfigurable architecture while offering cost-effective solutions to the service providers. Unlike traditional Wi-Fi networks, with each access point (AP) connected to the wired network, in WMNs only a subset of the APs are required to be connected to the wired network. The APs that are connected to the wired network are called the Internet gateways (IGWs), while the APs that do not have wired connections are called the mesh routers (MRs). The MRs are connected to the IGWs using multi-hop communication. The IGWs provide access to conventional clients and interconnect ad hoc, sensor, cellular, and other networks to the Internet. However, most of the existing routing protocols for WMNs are extensions of protocols originally designed for mobile ad hoc networks (MANETs) and thus they perform sub-optimally. Moreover, most routing protocols for WMNs are designed without security issues in mind, where the nodes are all assumed to be honest. In practical deployment scenarios, this assumption does not hold. This chapter provides a comprehensive overview of security issues in WMNs and then particularly focuses on secure routing in these networks. First, it identifies security vulnerabilities in the medium access control (MAC) and the network layers. Various possibilities of compromising data confidentiality, data integrity, replay attacks and offline cryptanalysis are also discussed. Then various types of attacks in the MAC and the network layers are discussed. After enumerating the various types of attacks on the MAC and the network layer, the chapter briefly discusses on some of the preventive mechanisms for these attacks.Comment: 44 pages, 17 figures, 5 table

Intrusion Detection System for Platooning Connected Autonomous Vehicles

The deployment of Connected Autonomous Vehicles (CAVs) in Vehicular Ad Hoc Networks (VANETs) requires secure wireless communication in order to ensure reliable connectivity and safety. However, this wireless communication is vulnerable to a variety of cyber atacks such as spoofing or jamming attacks. In this paper, we describe an Intrusion Detection System (IDS) based on Machine Learning (ML) techniques designed to detect both spoofing and jamming attacks in a CAV environment. The IDS would reduce the risk of traffic disruption and accident caused as a result of cyber-attacks. The detection engine of the presented IDS is based on the ML algorithms Random Forest (RF), k-Nearest Neighbour (k-NN) and One-Class Support Vector Machine (OCSVM), as well as data fusion techniques in a cross-layer approach. To the best of the authors’ knowledge, the proposed IDS is the first in literature that uses a cross-layer approach to detect both spoofing and jamming attacks against the communication of connected vehicles platooning. The evaluation results of the implemented IDS present a high accuracy of over 90% using training datasets containing both known and unknown attacks

On modeling and mitigating new breed of dos attacks

Denial of Service (DoS) attacks pose serious threats to the Internet, exerting in tremendous impact on our daily lives that are heavily dependent on the good health of the Internet. This dissertation aims to achieve two objectives:1) to model new possibilities of the low rate DoS attacks; 2) to develop effective mitigation mechanisms to counter the threat from low rate DoS attacks. A new stealthy DDoS attack model referred to as the quiet attack is proposed in this dissertation. The attack traffic consists of TCP traffic only. Widely used botnets in today\u27s various attacks and newly introduced network feedback control are integral part of the quiet attack model. The quiet attack shows that short-lived TCP flows used as attack flows can be intentionally misused. This dissertation proposes another attack model referred to as the perfect storm which uses a combination of UDP and TCP. Better CAPTCHAs are highlighted as current defense against botnets to mitigate the quiet attack and the perfect storm. A novel time domain technique is proposed that relies on the time difference between subsequent packets of each flow to detect periodicity of the low rate DoS attack flow. An attacker can easily use different IP address spoofing techniques or botnets to launch a low rate DoS attack and fool the detection system. To mitigate such a threat, this dissertation proposes a second detection algorithm that detects the sudden increase in the traffic load of all the expired flows within a short period. In a network rate DoS attacks, it is shown that the traffic load of all the expired flows is less than certain thresholds, which are derived from real Internet traffic analysis. A novel filtering scheme is proposed to drop the low rate DoS attack packets. The simulation results confirm attack mitigation by using proposed technique. Future research directions will be briefly discussed