MALICIOUS TRAFFIC DETECTION IN DNS INFRASTRUCTURE USING DECISION TREE ALGORITHM

Domain Name System (DNS) is an essential component in internet infrastructure to direct domains to IP addresses or conversely. Despite its important role in delivering internet services, attackers often use DNS as a bridge to breach a system. A DNS traffic analysis system is needed for early detection of attacks. However, the available security tools still have many shortcomings, for example broken authentication, sensitive data exposure, injection, etc. This research uses DNS analysis to develop anomaly-based techniques to detect malicious traffic on the DNS infrastructure. To do this, We look for network features that characterize DNS traffic. Features obtained will then be processed using the Decision Tree algorithm to classifyincoming DNS traffic. We experimented with 2.291.024 data traffic data matches the characteristics of BotNet and normal traffic. By dividing the data into 80% training and 20% testing data, our experimental results showed high detection aacuracy (96.36%) indicating the robustness of our method

MBotCS: A mobile botnet detection system based on machine learning

As the use of mobile devices spreads dramatically, hackers have started making use of mobile botnets to steal user information or perform other malicious attacks. To address this problem, in this paper we propose a mobile botnet detection system, called MBotCS. MBotCS can detect mobile device traffic indicative of the presence of a mobile botnet based on prior training using machine learning techniques. Our approach has been evaluated using real mobile device traffic captured from Android mobile devices, running normal apps and mobile botnets. In the evaluation, we investigated the use of 5 machine learning classifier algorithms and a group of machine learning box algorithms with different validation schemes. We have also evaluated the effect of our approach with respect to its effect on the overall performance and battery consumption of mobile devices

ANALYSIS OF BOTNET CLASSIFICATION AND DETECTION BASED ON C&C CHANNEL

Botnet is a serious threat to cyber-security. Botnet is a robot that can enter the computer and perform DDoS attacks through attacker’s command. Botnets are designed to extract confidential information from network channels such as LAN, Peer or Internet. They perform on hacker's intention through Command & Control(C&C) where attacker can control the whole network and can clinch illegal activities such as identity theft, unauthorized logins and money transactions. Thus, for security reason, it is very important to understand botnet behavior and go through its countermeasures. This thesis draws together the main ideas of network anomaly, botnet behavior, taxonomy of botnet, famous botnet attacks and detections processes. Based on network protocols, botnets are mainly 3 types: IRC, HTTP, and P2P botnet. All 3 botnet's behavior, vulnerability, and detection processes with examples are explained individually in upcoming chapters. Meanwhile saying shortly, IRC Botnet refers to early botnets targeting chat and messaging applications, HTTP Botnet targets internet browsing/domains and P2P Botnet targets peer network i.e. decentralized servers. Each Botnet's design, target, infecting and spreading mechanism can be different from each other. For an instance, IRC Botnet is targeted for small environment attacks where HTTP and P2P are for huge network traffic. Furthermore, detection techniques and algorithms filtration processes are also different among each of them. Based on these individual botnet's behavior, many research papers have analyzed numerous botnet detection techniques such as graph-based structure, clustering algorithm and so on. Thus, this thesis also analyzes popular detection mechanisms, C&C channels, Botnet working patterns, recorded datasets, results and false positive rates of bots prominently found in IRC, HTTP and P2P. Research area covers C&C channels, botnet behavior, domain browsing, IRC, algorithms, intrusion and detection, network and peer, security and test results. Research articles are conducted from scientific books through online source and University of Turku library

A critical review of intrusion detection systems in the internet of things : techniques, deployment strategy, validation strategy, attacks, public datasets and challenges

The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack on the end nodes. To this end, Numerous IoT intrusion detection Systems (IDS) have been proposed in the literature to tackle attacks on the IoT ecosystem, which can be broadly classified based on detection technique, validation strategy, and deployment strategy. This survey paper presents a comprehensive review of contemporary IoT IDS and an overview of techniques, deployment Strategy, validation strategy and datasets that are commonly applied for building IDS. We also review how existing IoT IDS detect intrusive attacks and secure communications on the IoT. It also presents the classification of IoT attacks and discusses future research challenges to counter such IoT attacks to make IoT more secure. These purposes help IoT security researchers by uniting, contrasting, and compiling scattered research efforts. Consequently, we provide a unique IoT IDS taxonomy, which sheds light on IoT IDS techniques, their advantages and disadvantages, IoT attacks that exploit IoT communication systems, corresponding advanced IDS and detection capabilities to detect IoT attacks. © 2021, The Author(s)