Biblio
In this paper we discuss the Internet of Things (IoT) by exploring aspects which go beyond the proliferation of devices and information enabled by: the growth of the Internet, increased miniaturization, prolonged battery life and an IT literate user base. We highlight the role of feedback mechanisms and illustrate this with reference to implemented computer enabled factory control systems. As the technology has developed, the cost of computing has reduced drastically, programming interfaces have improved, sensors are simpler and more cost effective and high performance communications across a wide area are readily available. We illustrate this by considering an application based on the Raspberry Pi, which is a low cost, small, programmable and network capable computer based on a powerful ARM processor with a programmable I/O interface, which can provide access to sensors (and other devices). The prototype application running on this platform can sense the presence of human being, using inexpensive passive infrared detectors. This can be used to monitor the activity of vulnerable adults, logging the results to a central server using a domestic Internet solution over a Wireless LAN. Whilst this demonstrates the potential for the use of such control/monitoring systems, practical systems spanning thousands of sites will be more complex to deliver and will have more stringent data processing and management demands and security requirements. We will discuss these concepts in the context of delivery of a smart interconnected society.
The detection of cyber-attacks has become a crucial task for highly sophisticated systems like industrial control systems (ICS). These systems are an essential part of critical information infrastructure. Therefore, we can highlight their vital role in contemporary society. The effective and reliable ICS cyber defense is a significant challenge for the cyber security community. Thus, intrusion detection is one of the demanding tasks for the cyber security researchers. In this article, we examine classification problem. The proposed detection system is based on supervised anomaly detection techniques. Moreover, we utilized classifiers algorithms in order to increase intrusion detection capabilities. The fusion of the classifiers is the way how to achieve the predefined goal.
Modbus over TCP/IP is one of the most popular industrial network protocol that are widely used in critical infrastructures. However, vulnerability of Modbus TCP protocol has attracted widely concern in the public. The traditional intrusion detection methods can identify some intrusion behaviors, but there are still some problems. In this paper, we present an innovative approach, SD-IDS (Stereo Depth IDS), which is designed for perform real-time deep inspection for Modbus TCP traffic. SD-IDS algorithm is composed of two parts: rule extraction and deep inspection. The rule extraction module not only analyzes the characteristics of industrial traffic, but also explores the semantic relationship among the key field in the Modbus TCP protocol. The deep inspection module is based on rule-based anomaly intrusion detection. Furthermore, we use the online test to evaluate the performance of our SD-IDS system. Our approach get a low rate of false positive and false negative.
Industrial control systems (ICS) used in industrial plants are vulnerable to cyber-attacks that can cause fatal damage to the plants. Intrusion detection systems (IDSs) monitor ICS network traffic and detect suspicious activities. However, many IDSs overlook sophisticated cyber-attacks because it is hard to make a complete database of cyber-attacks and distinguish operational anomalies when compared to an established baseline. In this paper, a discriminant model between normal and anomalous packets was constructed with a support vector machine (SVM) based on an ICS communication profile, which represents only packet intervals and length, and an IDS with the applied model is proposed. Furthermore, the proposed IDS was evaluated using penetration tests on our cyber security test bed. Although the IDS was constructed by the limited features (intervals and length) of packets, the IDS successfully detected cyber-attacks by monitoring the rate of predicted attacking packets.
We outline an anomaly detection method for industrial control systems (ICS) that combines the analysis of network package contents that are transacted between ICS nodes and their time-series structure. Specifically, we take advantage of the predictable and regular nature of communication patterns that exist between so-called field devices in ICS networks. By observing a system for a period of time without the presence of anomalies we develop a base-line signature database for general packages. A Bloom filter is used to store the signature database which is then used for package content level anomaly detection. Furthermore, we approach time-series anomaly detection by proposing a stacked Long Short Term Memory (LSTM) network-based softmax classifier which learns to predict the most likely package signatures that are likely to occur given previously seen package traffic. Finally, by the inspection of a real dataset created from a gas pipeline SCADA system, we show that an anomaly detection scheme combining both approaches can achieve higher performance compared to various current state-of-the-art techniques.
Currently, security protection in Industrial Control Systems has become a hot topic, and a great number of defense techniques have sprung up. As one of the most effective approaches, area isolation has the exceptional advantages and is widely used to prevent attacks or hazards propagating. However, most existing methods for inter-area communication protection present some limitations, i.e., excessively depending on the analyzing rules, affecting original communication. Additionally, the network architecture and data flow direction can hardly be adjusted after being deployed. To address these problems, a dynamical and customized communication protection technology is proposed in this paper. In detail, a security inter-area communication architecture based on Software Defined Network is designed firstly, where devices or subsystems can be dynamically added into or removed from the communication link. And then, a security inspection method based on information entropy is presented for deep network behaviors analysis. According to the security analysis results, the communications in the network can be adjusted in time. Finally, simulations are constructed, and the results indicate that the proposed approach is sensitive and effective for cyber-attacks detection.
In this paper, an industrial testbed is proposed utilizing commercial-off-the-shelf equipment, and it is used to study the weakness of industrial Ethernet, i.e., PROFINET. The investigation is based on observation of the principles of operation of PROFINET and the functionality of industrial control systems.
Until recently, IT security received limited attention within the scope of Process Control Systems (PCS). In the past, PCS consisted of isolated, specialized components running closed process control applications, where hardware was placed in physically secured locations and connections to remote network infrastructures were forbidden. Nowadays, industrial communications are fully exploiting the plethora of features and novel capabilities deriving from the adoption of commodity off the shelf (COTS) hardware and software. Nonetheless, the reliance on COTS for remote monitoring, configuration and maintenance also exposed PCS to significant cyber threats. In light of these issues, this paper presents the steps for the design, verification and implementation of a lightweight remote attestation protocol. The protocol is aimed at providing a secure software integrity verification scheme that can be readily integrated into existing industrial applications. The main novelty of the designed protocol is that it encapsulates key elements for the protection of both participating parties (i.e., verifier and prover) against cyber attacks. The protocol is formally verified for correctness with the help of the Scyther model checking tool. The protocol implementation and experimental results are provided for a Phoenix-Contact industrial controller, which is widely used in the automation of gas transportation networks in Romania.
The discussion of threats and vulnerabilities in Industrial Control Systems has gained popularity during the last decade due to the increase in interest and growing concern to secure these systems. In order to provide an overview of the complete landscape of these threats and vulnerabilities this contribution provides a tiered security analysis of the assets that constitute Industrial Control Systems. The identification of assets is obtained from a generalization of the system's architecture. Additionally, the security analysis is complemented by discussing security countermeasures and solutions that can be used to counteract the vulnerabilities and increase the security of control systems.
Supervisory Control and Data Acquisition (SCADA) systems complexity and interconnectivity increase in recent years have exposed the SCADA networks to numerous potential vulnerabilities. Several studies have shown that anomaly-based Intrusion Detection Systems (IDS) achieves improved performance to identify unknown or zero-day attacks. In this paper, we propose a hybrid model for anomaly-based intrusion detection in SCADA networks using machine learning approach. In the first part, we present a robust hybrid model for anomaly-based intrusion detection in SCADA networks. Finally, we present a feature selection model for anomaly-based intrusion detection in SCADA networks by removing redundant and irrelevant features. Irrelevant features in the dataset can affect modeling power and reduce predictive accuracy. These models were evaluated using an industrial control system dataset developed at the Distributed Analytics and Security Institute Mississippi State University Starkville, MS, USA. The experimental results show that our proposed model has a key effect in reducing the time and computational complexity and achieved improved accuracy and detection rate. The accuracy of our proposed model was measured as 99.5 % for specific-attack-labeled.
Industrial Control Systems (ICS) are found in critical infrastructure such as for power generation and water treatment. When security requirements are incorporated into an ICS, one needs to test the additional code and devices added do improve the prevention and detection of cyber attacks. Conducting such tests in legacy systems is a challenge due to the high availability requirement. An approach using Timed Automata (TA) is proposed to overcome this challenge. This approach enables assessment of the effectiveness of an attack detection method based on process invariants. The approach has been demonstrated in a case study on one stage of a 6- stage operational water treatment plant. The model constructed captured the interactions among components in the selected stage. In addition, a set of attacks, attack detection mechanisms, and security specifications were also modeled using TA. These TA models were conjoined into a network and implemented in UPPAAL. The models so implemented were found effective in detecting the attacks considered. The study suggests the use of TA as an effective tool to model an ICS and study its attack detection mechanisms as a complement to doing so in a real plant-operational or under design.
This paper focuses on exploitable cyber vulnerabilities in industrial control systems (ICS) and on a new approach of resiliency against them. Even with numerous metrics and methods for intrusion detection and mitigation strategy, a complete detection and deterrence of cyber-attacks for ICS is impossible. Countering the impact and consequence of possible malfunctions caused by such attacks in the safety-critical ICS's, this paper proposes new controller architecture to fail-operate even under compromised situations. The proposed new ICS is realized with diversification of hardware/software and unidirectional communication in alerting suspicious infiltration to upper-level management. Equipped with control bus monitoring, this operation-basis approach of infiltration detection would become a truly cyber-resilient ICS. The proposed system is tested in a lab hardware experimentation setup and on a cybersecurity test bed, DeterLab, for validation.
A lot of research in security of cyber physical systems focus on threat models where an attacker can spoof sensor readings by compromising the communication channel. A little focus is given to attacks on physical components. In this paper a method to detect potential attacks on physical components in a Cyber Physical System (CPS) is proposed. Physical attacks are detected through a comparison of noise pattern from sensor measurements to a reference noise pattern. If an adversary has physically modified or replaced a sensor, the proposed method issues an alert indicating that a sensor is probably compromised or is defective. A reference noise pattern is established from the sensor data using a deterministic model. This pattern is referred to as a fingerprint of the corresponding sensor. The fingerprint so derived is used as a reference to identify measured data during the operation of a CPS. Extensive experimentation with ultrasonic level sensors in a realistic water treatment testbed point to the effectiveness of the proposed fingerprinting method in detecting physical attacks.
The survey of related work in the very specialized field of information security (IS) ensurance for the Internet of Things (IoT) allowed us to work out a taxonomy of typical attacks against the IoT elements (with special attention to the IoT device protection). The key directions of countering these attacks were defined on this basis. According to the modern demand for the IoT big IS-related data processing, the application of Security Intelligence approach is proposed. The main direction of the future research, namely the IoT operational resilience, is indicated.
Intrusion detection has been an active field of research for more than 35 years. Numerous systems had been built based on the two fundamental detection principles, knowledge-based and behavior-based detection. Anyway, having a look at day-to-day news about data breaches and successful attacks, detection effectiveness is still limited. Even more, heavy-weight intrusion detection systems cannot be installed in every endangered environment. For example, Industrial Control Systems are typically utilized for decades, charging off huge investments of companies. Thus, some of these systems have been in operation for years, but were designed afore without security in mind. Even worse, as systems often have connections to other networks and even the Internet nowadays, an adequate protection is mandatory, but integrating intrusion detection can be extremely difficult - or even impossible to date. We propose a new lightweight current-based IDS which is using a difficult to manipulate measurement base and verifiable ground truth. Focus of our system is providing intrusion detection for ICS and SCADA on a low-priced base, easy to integrate. Dr. WATTson, a prototype implemented based on our concept provides high detection and low false alarm rates.
Industrial Control System (ICS) consists of large number of electronic devices connected to field devices to execute the physical processes. Communication network of ICS supports wide range of packet based applications. A growing issue with network security and its impact on ICS have highlighted some fundamental risks to critical infrastructure. To address network security issues for ICS a clear understanding of security specific defensive countermeasures is required. Reconnaissance of ICS network by deep packet inspection (DPI) consists analysis of the contents of the captured packets in order to get accurate measures of process that uses specific countermeasure to create an aggregated posture. In this paper we focus on novel approach by presenting a technique with captured network traffic. This technique is capable to identify the protocols and extract different features for classification of traffic based on network protocol, header information and payload to understand the whole architecture of complex system. Here we have segregated possible types of attacks on ICS.
Due to the fact that the cyber security risks exist in industrial control system, risk assessment on Industrial Automation Platform (IAP) is discussed in this paper. The cyber security assessment model for IAP is built based on relevant standards at abroad. Fuzzy analytic hierarchy process and fuzzy comprehensive evaluation method based on entropy theory are utilized to evaluate the communication links' risk of IAP software. As a result, the risk weight of communication links which have impacts on platform and the risk level of this platform are given for further study on protective strategy. The assessment result shows that the methods used can evaluate this platform efficiently and practically.
Industrial Control Systems (ICS) which among others are comprised of Supervisory Control and Data Acquisition (SCADA) and Distributed Control Systems (DCS) are used to control industrial processes. ICS have now been connected to other Information Technology (IT) systems and have as a result become vulnerable to Advanced Persistent Threats (APT). APTs are targeted attacks that use zero-day attacks to attack systems. Current ICS security mechanisms fail to deter APTs from infiltrating ICS. An analysis of possible solutions to deter APTs was done. This paper proposes the use of Artificial Immune Systems to secure ICS from APTs.
The proliferation of digital devices in a networked industrial ecosystem, along with an exponential growth in complexity and scope, has resulted in elevated security concerns and management complexity issues. This paper describes a novel architecture utilizing concepts of autonomic computing and a simple object access protocol (SOAP)-based interface to metadata access points (IF-MAP) external communication layer to create a network security sensor. This approach simplifies integration of legacy software and supports a secure, scalable, and self-managed framework. The contribution of this paper is twofold: 1) A flexible two-level communication layer based on autonomic computing and service oriented architecture is detailed and 2) three complementary modules that dynamically reconfigure in response to a changing environment are presented. One module utilizes clustering and fuzzy logic to monitor traffic for abnormal behavior. Another module passively monitors network traffic and deploys deceptive virtual network hosts. These components of the sensor system were implemented in C++ and PERL and utilize a common internal D-Bus communication mechanism. A proof of concept prototype was deployed on a mixed-use test network showing the possible real-world applicability. In testing, 45 of the 46 network attached devices were recognized and 10 of the 12 emulated devices were created with specific operating system and port configurations. In addition, the anomaly detection algorithm achieved a 99.9% recognition rate. All output from the modules were correctly distributed using the common communication structure.
The proliferation of digital devices in a networked industrial ecosystem, along with an exponential growth in complexity and scope, has resulted in elevated security concerns and management complexity issues. This paper describes a novel architecture utilizing concepts of autonomic computing and a simple object access protocol (SOAP)-based interface to metadata access points (IF-MAP) external communication layer to create a network security sensor. This approach simplifies integration of legacy software and supports a secure, scalable, and self-managed framework. The contribution of this paper is twofold: 1) A flexible two-level communication layer based on autonomic computing and service oriented architecture is detailed and 2) three complementary modules that dynamically reconfigure in response to a changing environment are presented. One module utilizes clustering and fuzzy logic to monitor traffic for abnormal behavior. Another module passively monitors network traffic and deploys deceptive virtual network hosts. These components of the sensor system were implemented in C++ and PERL and utilize a common internal D-Bus communication mechanism. A proof of concept prototype was deployed on a mixed-use test network showing the possible real-world applicability. In testing, 45 of the 46 network attached devices were recognized and 10 of the 12 emulated devices were created with specific operating system and port configurations. In addition, the anomaly detection algorithm achieved a 99.9% recognition rate. All output from the modules were correctly distributed using the common communication structure.
This paper presents a survey on cyber security issues in in current industrial automation and control systems, which also includes observations and insights collected and distilled through a series of discussion by some of major Japanese experts in this field. It also tries to provide a conceptual framework of those issues and big pictures of some ongoing projects to try to enhance it.