A Survey on Enterprise Network Security: Asset Behavioral Monitoring and Distributed Attack Detection

Enterprise networks that host valuable assets and services are popular and frequent targets of distributed network attacks. In order to cope with the ever-increasing threats, industrial and research communities develop systems and methods to monitor the behaviors of their assets and protect them from critical attacks. In this paper, we systematically survey related research articles and industrial systems to highlight the current status of this arms race in enterprise network security. First, we discuss the taxonomy of distributed network attacks on enterprise assets, including distributed denial-of-service (DDoS) and reconnaissance attacks. Second, we review existing methods in monitoring and classifying network behavior of enterprise hosts to verify their benign activities and isolate potential anomalies. Third, state-of-the-art detection methods for distributed network attacks sourced from external attackers are elaborated, highlighting their merits and bottlenecks. Fourth, as programmable networks and machine learning (ML) techniques are increasingly becoming adopted by the community, their current applications in network security are discussed. Finally, we highlight several research gaps on enterprise network security to inspire future research.Comment: Journal paper submitted to Elseive

Toward Network-based DDoS Detection in Software-defined Networks

To combat susceptibility of modern computing systems to cyberattack, identifying and disrupting malicious traffic without human intervention is essential. To accomplish this, three main tasks for an effective intrusion detection system have been identified: monitor network traffic, categorize and identify anomalous behavior in near real time, and take appropriate action against the identified threat. This system leverages distributed SDN architecture and the principles of Artificial Immune Systems and Self-Organizing Maps to build a network-based intrusion detection system capable of detecting and terminating DDoS attacks in progress

Security techniques for sensor systems and the Internet of Things

Sensor systems are becoming pervasive in many domains, and are recently being generalized by the Internet of Things (IoT). This wide deployment, however, presents significant security issues. We develop security techniques for sensor systems and IoT, addressing all security management phases. Prior to deployment, the nodes need to be hardened. We develop nesCheck, a novel approach that combines static analysis and dynamic checking to efficiently enforce memory safety on TinyOS applications. As security guarantees come at a cost, determining which resources to protect becomes important. Our solution, OptAll, leverages game-theoretic techniques to determine the optimal allocation of security resources in IoT networks, taking into account fixed and variable costs, criticality of different portions of the network, and risk metrics related to a specified security goal. Monitoring IoT devices and sensors during operation is necessary to detect incidents. We design Kalis, a knowledge-driven intrusion detection technique for IoT that does not target a single protocol or application, and adapts the detection strategy to the network features. As the scale of IoT makes the devices good targets for botnets, we design Heimdall, a whitelist-based anomaly detection technique for detecting and protecting against IoT-based denial of service attacks. Once our monitoring tools detect an attack, determining its actual cause is crucial to an effective reaction. We design a fine-grained analysis tool for sensor networks that leverages resident packet parameters to determine whether a packet loss attack is node- or link-related and, in the second case, locate the attack source. Moreover, we design a statistical model for determining optimal system thresholds by exploiting packet parameters variances. With our techniques\u27 diagnosis information, we develop Kinesis, a security incident response system for sensor networks designed to recover from attacks without significant interruption, dynamically selecting response actions while being lightweight in communication and energy overhead

A principled approach to measuring the IoT ecosystem

Internet of Things (IoT) devices combine network connectivity, cheap hardware, and actuation to provide new ways to interface with the world. In spite of this growth, little work has been done to measure the network properties of IoT devices. Such measurements can help to inform systems designers and security researchers of IoT networking behavior in practice to guide future research. Unfortunately, properly measuring the IoT ecosystem is not trivial. Devices may have different capabilities and behaviors, which require both active measurements and passive observation to quantify. Furthermore, the IoT devices that are connected to the public Internet may vary from those connected inside home networks, requiring both an external and internal vantage point to draw measurements from. In this thesis, we demonstrate how IoT measurements drawn from a single vantage point or mesaurement technique lead to a biased view of the network services in the IoT ecosystem. To do this, we conduct several real-world IoT measurements, drawn from both inside and outside home networks using active and passive monitoring. First, we leverage active scanning and passive observation in understanding the Mirai botnet---chiefly, we report on the devices it infected, the command and control infrastructure behind the botnet, and how the malware evolved over time. We then conduct active measurements from inside 16M home networks spanning 83M devices from 11~geographic regions to survey the IoT devices installed around the world. We demonstrate how these measurements can uncover the device types that are most at risk and the vendors who manufacture the weakest devices. We compare our measurements with passive external observation by detecting compromised scanning behavior from smart homes. We find that while passive external observation can drive insight about compromised networks, it offers little by way of concrete device attribution. We next compare our results from active external scanning with active internal scanning and show how relying solely on external scanning for IoT measurements under-reports security important IoT protocols, potentially skewing the services investigated by the security community. Finally, we conduct passive measurements of 275~smart home networks to investigate IoT behavior. We find that IoT device behavior varies by type and devices regularly communicate over a myriad of bespoke ports, in many cases to speak standard protocols (e.g., HTTP). Finally, we observe that devices regularly offer active services (e.g., Telnet, rpcbind) that are rarely, if ever, used in actual communication, demonstrating the need for both active and passive measurements to properly compare device capabilities and behaviors. Our results highlight the need for a confluence of measurement perspectives to comprehensively understand IoT ecosystem. We conclude with recommendations for future measurements of IoT devices as well as directions for the systems and security community informed by our work

Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats

Despite its technological benefits, Internet of Things (IoT) has cyber weaknesses due to the vulnerabilities in the wireless medium. Machine learning (ML)-based methods are widely used against cyber threats in IoT networks with promising performance. Advanced persistent threat (APT) is prominent for cybercriminals to compromise networks, and it is crucial to long-term and harmful characteristics. However, it is difficult to apply ML-based approaches to identify APT attacks to obtain a promising detection performance due to an extremely small percentage among normal traffic. There are limited surveys to fully investigate APT attacks in IoT networks due to the lack of public datasets with all types of APT attacks. It is worth to bridge the state-of-the-art in network attack detection with APT attack detection in a comprehensive review article. This survey article reviews the security challenges in IoT networks and presents the well-known attacks, APT attacks, and threat models in IoT systems. Meanwhile, signature-based, anomaly-based, and hybrid intrusion detection systems are summarized for IoT networks. The article highlights statistical insights regarding frequently applied ML-based methods against network intrusion alongside the number of attacks types detected. Finally, open issues and challenges for common network intrusion and APT attacks are presented for future research.Comment: ACM Computing Surveys, 2022, 35 pages, 10 Figures, 8 Table