The widespread adoption of Internet of Things has led to many security issues. Post the Mirai-based DDoS attack in 2016 which compromised IoT devices, a host of new malware using Mirai's leaked source code and targeting IoT devices have cropped up, e.g. Satori, Reaper, Amnesia, Masuta etc. These malware exploit software vulnerabilities to infect IoT devices instead of open TELNET ports (like Mirai) making them more difficult to block using existing solutions such as firewalls. In this research, we present EDIMA, a distributed modular solution which can be used towards the detection of IoT malware network activity in large-scale networks (e.g. ISP, enterprise networks) during the scanning infecting phase rather than during an attack. EDIMA employs machine learning algorithms for edge devices' traffic classification, a packet traffic feature vector database, a policy module and an optional packet sub-sampling module. We evaluate the classification performance of EDIMA through testbed experiments and present the results obtained.
There has also been some research on intrusion detection and anomaly detection systems for IoT. A whitelist-based intrusion detection system for IoT devices (Heimdall) has been presented in @cite_2 . The authors in @cite_3 propose an intrusion detection model for IoT backbone networks leveraging two-layer dimension reduction and two-tier classification techniques to detect U2R (User-to-Root) and R2L (Remote-to-Local) attacks.
@cite_3 @cite_2
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With increasing reliance on Internet of Things (IoT) devices and services, the capability to detect intrusions and malicious activities within IoT networks is critical for resilience of the network infrastructure. In this paper, we present a novel model for intrusion detection based on two-layer dimension reduction and two-tier classification module, designed to detect malicious activities such as User to Root (U2R) and Remote to Local (R2L) attacks. The proposed model is using component analysis and linear discriminate analysis of dimension reduction module to spate the high dimensional dataset to a lower one with lesser features. We then apply a two-tier classification module utilizing Naive Bayes and Certainty Factor version of K-Nearest Neighbor to identify suspicious behaviors. The experiment results using NSL-KDD dataset shows that our model outperforms previous models designed to detect U2R and R2L attacks. The Internet of Things (IoT) is built of many small smart objects continuously connected to the Internet. This makes these devices an easy target for attacks exploiting vulnerabilities at the network, application, and mobile level. With that it comes as no surprise that distributed denial of service attacks leveraging these vulnerable devices have become a new standard for effective botnets. In this paper, we propose Heimdall, a whitelist-based intrusion detection technique tailored to IoT devices. Heimdall operates on routers acting as gateways for IoT as a homogeneous defense for all devices behind the router. Our experimental results show that our defense mechanism is effective and has minimal overhead. Intrusion detection is well-known as an essential component to secure the systems in Information and Communication Technology (ICT). Based on the type of analyzing events, two kinds of Intrusion Detection Systems (IDS) have been proposed: anomaly-based and misuse-based. In this paper, three-layer Recurrent Neural Network (RNN) architecture with categorized features as inputs and attack types as outputs of RNN is proposed as misuse-based IDS. The input features are categorized to basic features, content features, time-based traffic features, and host-based traffic features. The attack types are classified to Denial-of-Service (DoS), Probe, Remote-to-Local (R2L), and User-to-Root (U2R). For this purpose, in this study, we use the 41 features per connection defined by International Knowledge Discovery and Data mining group (KDD). The RNN has an extra output which corresponds to normal class (no attack). The connections between the nodes of two hidden layers of RNN are considered partial. Experimental results show that the proposed model is able to improve classification rate, particularly in R2L attacks. This method also offers better Detection Rate (DR) and Cost Per Example (CPE) when compared to similar related works and also the simulated Multi-Layer Perceptron (MLP) and Elman-based intrusion detectors. On the other hand, False Alarm Rate (FAR) of the proposed model is not degraded significantly when compared to some recent machine learning methods. Security experts have demonstrated numerous risks imposed by Internet of Things (IoT) devices on organizations. Due to the widespread adoption of such devices, their diversity, standardization obstacles, and inherent mobility, organizations require an intelligent mechanism capable of automatically detecting suspicious IoT devices connected to their networks. In particular, devices not included in a white list of trustworthy IoT device types (allowed to be used within the organizational premises) should be detected. In this research, Random Forest, a supervised machine learning algorithm, was applied to features extracted from network traffic data with the aim of accurately identifying IoT device types from the white list. To train and evaluate multi-class classifiers, we collected and manually labeled network traffic data from 17 distinct IoT devices, representing nine types of IoT devices. Based on the classification of 20 consecutive sessions and the use of majority rule, IoT device types that are not on the white list were correctly detected as unknown in 96 of test cases (on average), and white listed device types were correctly classified by their actual types in 99 of cases. Some IoT device types were identified quicker than others (e.g., sockets and thermostats were successfully detected within five TCP sessions of connecting to the network). Perfect detection of unauthorized IoT device types was achieved upon analyzing 110 consecutive sessions; perfect classification of white listed types required 346 consecutive sessions, 110 of which resulted in 99.49 accuracy. Further experiments demonstrated the successful applicability of classifiers trained in one location and tested on another. In addition, a discussion is provided regarding the resilience of our machine learning-based IoT white listing method to adversarial attacks. Intrusion detection systems (IDSs) fall into two high-level categories: network-based systems (NIDS) that monitor network behaviors, and host-based systems (HIDS) that monitor system calls. In this work, we present a general technique for both systems. We use anomaly detection, which identifies patterns not conforming to a historic norm. In both types of systems, the rates of change vary dramatically over time (due to burstiness) and over components (due to service difference). To efficiently model such systems, we use continuous time Bayesian networks (CTBNs) and avoid specifying a fixed update interval common to discrete-time models. We build generative models from the normal training data, and abnormal behaviors are flagged based on their likelihood under this norm. For NIDS, we construct a hierarchical CTBN model for the network packet traces and use Rao-Blackwellized particle filtering to learn the parameters. We illustrate the power of our method through experiments on detecting real worms and identifying hosts on two publicly available network traces, the MAWI dataset and the LBNL dataset. For HIDS, we develop a novel learning method to deal with the finite resolution of system log file time stamps, without losing the benefits of our continuous time model. We demonstrate the method by detecting intrusions in the DARPA 1998 BSM dataset. The development of intrusion detection systems (IDS) that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC) and deep neural network (DNN) algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF) and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks. Intrusion detection systems (IDSs) are used to detect traces of malicious activities targeted against the network and its resources. Anomaly-based IDSs build models of the expected behavior of applications by analyzing events that are generated during the applications' normal operation. Once these models have been established, subsequent events are analyzed to identify deviations, on the assumption that anomalies represent evidence of an attack. Host-based anomaly detection systems often rely on system call sequences to characterize the normal behavior of applications. Recently, it has been shown how these systems can be evaded by launching attacks that execute legitimate system call sequences. The evasion is possible because existing techniques do not take into account all available features of system calls. In particular, system call arguments are not considered. We propose two primary improvements upon existing host-based anomaly detectors. First, we apply multiple detection models to system call arguments. Multiple models allow the arguments of each system call invocation to be evaluated from several different perspectives. Second, we introduce a sophisticated method of combining the anomaly scores from each model into an overall aggregate score. The combined anomaly score determines whether an event is part of an attack. Individual anomaly scores are often contradicting and, therefore, a simple weighted sum cannot deliver reliable results. To address this problem, we propose a technique that uses Bayesian networks to perform system call classification. We show that the analysis of system call arguments and the use of Bayesian classification improves detection accuracy and resilience against evasion attempts. In addition, the paper describes a tool based on our approach and provides a quantitative evaluation of its performance in terms of both detection effectiveness and overhead. A comparison with four related approaches is also presented. Abstract With the growing usage of technology, intrusion detection became an emerging area of research. Intrusion Detection System (IDS) attempts to identify and notify the activities of users as normal (or) anomaly. IDS is a nonlinear and complicated problem and deals with network traffic data. Many IDS methods have been proposed and produce different levels of accuracy. This is why development of effective and robust Intrusion detection system is necessary. In this paper, we have built a model for intrusion detection system using random forest classifier. Random Forest (RF) is an ensemble classifier and performs well compared to other traditional classifiers for effective classification of attacks. To evaluate the performance of our model, we conducted experiments on NSL-KDD data set. Empirical result show that proposed model is efficient with low false alarm rate and high detection rate. Prevention of security breaches completely using the existing security technologies is unrealistic. As a result, intrusion detection is an important component in network security. However, many current intrusion detection systems (IDSs) are rule-based systems, which have limitations to detect novel intrusions. Moreover, encoding rules is time-consuming and highly depends on the knowledge of known intrusions. Therefore, we propose new systematic frameworks that apply a data mining algorithm called random forests in misuse, anomaly, and hybrid-network-based IDSs. In misuse detection, patterns of intrusions are built automatically by the random forests algorithm over training data. After that, intrusions are detected by matching network activities against the patterns. In anomaly detection, novel intrusions are detected by the outlier detection mechanism of the random forests algorithm. After building the patterns of network services by the random forests algorithm, outliers related to the patterns are determined by the outlier detection algorithm. The hybrid detection system improves the detection performance by combining the advantages of the misuse and anomaly detection. We evaluate our approaches over the knowledge discovery and data mining 1999 (KDDpsila99) dataset. The experimental results demonstrate that the performance provided by the proposed misuse approach is better than the best KDDpsila99 result; compared to other reported unsupervised anomaly detection approaches, our anomaly detection approach achieves higher detection rate when the false positive rate is low; and the presented hybrid system can improve the overall performance of the aforementioned IDSs. The evolution and sophistication of cyber-attacks need resilient and evolving cybersecurity schemes. As an emerging technology, the Internet of Things (IoT) inherits cyber-attacks and threats from the IT environment despite the existence of a layered defensive security mechanism. The extension of the digital world to the physical environment of IoT brings unseen attacks that require a novel lightweight and distributed attack detection mechanism due to their architecture and resource constraints. Architecturally, fog nodes can be leveraged to offload security functions from IoT and the cloud to mitigate the resource limitation issues of IoT and scalability bottlenecks of the cloud. Classical machine learning algorithms have been extensively used for intrusion detection, although scalability, feature engineering efforts, and accuracy have hindered their penetration into the security market. These shortcomings could be mitigated using the deep learning approach as it has been successful in big data fields. Apart from eliminating the need to craft features manually, deep learning is resilient against morphing attacks with high detection accuracy. This article proposes an LSTM network for distributed cyber-attack detection in fog-to-things communication. We identify and analyze critical attacks and threats targeting IoT devices, especially attacks exploiting vulnerabilities of wireless communications. The conducted experiments on two scenarios demonstrate the effectiveness and efficiency of deeper models over traditional machine learning models. Intrusion detection systems define an important and dynamic research area for cybersecurity. The role of Intrusion Detection System within security architecture is to improve a security level by identification of all malicious and also suspicious events that could be observed in computer or network system. One of the more specific research areas related to intrusion detection is anomaly detection. Anomaly-based intrusion detection in networks refers to the problem of finding untypical events in the observed network traffic that do not conform to the expected normal patterns. It is assumed that everything that is untypical anomalous could be dangerous and related to some security events. To detect anomalies many security systems implements a classification or clustering algorithms. However, recent research proved that machine learning models might misclassify adversarial events, e.g. observations which were created by applying intentionally non-random perturbations to the dataset. Such weakness could increase of false negative rate which implies undetected attacks. This fact can lead to one of the most dangerous vulnerabilities of intrusion detection systems. The goal of the research performed was verification of the anomaly detection systems ability to resist this type of attack. This paper presents the preliminary results of tests taken to investigate existence of attack vector, which can use adversarial examples to conceal a real attack from being detected by intrusion detection systems. Intrusion detection has become one of the most critical tasks in a wireless network to prevent service outages that can take long to fix. The sheer variety of anomalous events necessitates adopting cognitive anomaly detection methods instead of the traditional signature-based detection techniques. This paper proposes an anomaly detection methodology for wireless systems that is based on monitoring and analyzing radio frequency (RF) spectrum activities. Our detection technique leverages an existing solution for the video prediction problem, and uses it on image sequences generated from monitoring the wireless spectrum. The deep predictive coding network is trained with images corresponding to the normal behavior of the system, and whenever there is an anomaly, its detection is triggered by the deviation between the actual and predicted behavior. For our analysis, we use the images generated from the time-frequency spectrograms and spectral correlation functions of the received RF signal. We test our technique on a dataset which contains anomalies such as jamming, chirping of transmitters, spectrum hijacking, and node failure, and evaluate its performance using standard classifier metrics: detection ratio, and false alarm rate. Simulation results demonstrate that the proposed methodology effectively detects many unforeseen anomalous events in real time. We discuss the applications, which encompass industrial IoT, autonomous vehicle control and mission-critical communications services. The proliferation of IoT devices that can be more easily compromised than desktop computers has led to an increase in IoT-based botnet attacks. To mitigate this threat, there is a need for new methods that detect attacks launched from compromised IoT devices and that differentiate between hours- and milliseconds-long IoT-based attacks. In this article, we propose a novel network-based anomaly detection method for the IoT called N-BaIoT that extracts behavior snapshots of the network and uses deep autoencoders to detect anomalous network traffic from compromised IoT devices. To evaluate our method, we infected nine commercial IoT devices in our lab with two widely known IoT-based botnets, Mirai and BASHLITE. The evaluation results demonstrated our proposed methods ability to accurately and instantly detect the attacks as they were being launched from the compromised IoT devices that were part of a botnet. IoT devices are being widely deployed. Many of them are vulnerable due to insecure implementations and configuration. As a result, many networks already have vulnerable devices that are easy to compromise. This has led to a new category of malware specifically targeting IoT devices. Existing intrusion detection techniques are not effective in detecting compromised IoT devices given the massive scale of the problem in terms of the number of different manufacturers involved. In this paper, we present D "IoT, a system for detecting compromised IoT devices effectively. In contrast to prior work, D "IoT uses a novel self-learning approach to classify devices into device types and build for each of these normal communication profiles that can subsequently be used to detect anomalous deviations in communication patterns. D "IoT is completely autonomous and can be trained in a distributed crowdsourced manner without requiring human intervention or labeled training data. Consequently, D "IoT copes with the emergence of new device types as well as new attacks. By systematic experiments using more than 30 real-world IoT devices, we show that D "IoT is effective (96 detection rate with 1 false alarms) and fast (<0.03 s.) at detecting devices compromised by the infamous Mirai malware. With the continuous evolution of the types of attacks against computer networks, traditional intrusion detection systems, based on pattern matching and static signatures, are increasingly limited by their need of an up-to-date and comprehensive knowledge base. Data mining techniques have been successfully applied in host-based intrusion detection. Applying data mining techniques on raw network data, however, is made difficult by the sheer size of the input; this is usually avoided by discarding the network packet contents.In this paper, we introduce a two-tier architecture to overcome this problem: the first tier is an unsupervised clustering algorithm which reduces the network packets payload to a tractable size. The second tier is a traditional anomaly detection algorithm, whose efficiency is improved by the availability of data on the packet payload content. In this paper, online detection of false data injection attacks and denial of service attacks in the smart grid is studied. The system is modeled as a discrete-time linear dynamic system and state estimation is performed using the Kalman filter. The generalized cumulative sum algorithm is employed for quickest detection of the cyber-attacks. Detectors are proposed in both centralized and distributed settings. The proposed detectors are robust to time-varying states, attacks, and set of attacked meters. Online estimates of the unknown attack variables are provided, that can be crucial for a quick system recovery. In the distributed setting, due to bandwidth constraints, local centers can only transmit quantized messages to the global center, and a novel event-based sampling scheme called level-crossing sampling with hysteresis is proposed that is shown to exhibit significant advantages compared with the conventional uniform-in-time sampling scheme. Moreover, a distributed dynamic state estimator is proposed based on information filters. Numerical examples illustrate the fast and accurate response of the proposed detectors in detecting both structured and random attacks and their advantages over existing methods. In this paper, we propose a hierarchical monitoring intrusion detection system (HAMIDS) for industrial control systems (ICS). The HAMIDS framework detects the anomalies in both level 0 and level 1 of an industrial control plant. In addition, the framework aggregates the cyber-physical process data in one point for further analysis as part of the intrusion detection process. The novelty of this framework is its ability to detect anomalies that have a distributed impact on the cyber-physical process. The performance of the proposed framework evaluated as part of SWaT security showdown (S3) in which six international teams were invited to test the framework in a real industrial control system. The proposed framework outperformed other proposed academic IDS in term of detection of ICS threats during the S3 event, which was held from July 25-29, 2016 at Singapore University of Technology and Design. This paper introduces a hybrid scheme that combines the advantages of deep belief network and support vector machine. An application of intrusion detection imaging has been chosen and hybridization scheme have been applied to see their ability and accuracy to classify the intrusion into two outcomes: normal or attack, and the attacks fall into four classes; R2L, DoS, U2R, and Probing. First, we utilize deep belief network to reduct the dimensionality of the feature sets. This is followed by a support vector machine to classify the intrusion into five outcome; Normal, R2L, DoS, U2R, and Probing. To evaluate the performance of our approach, we present tests on NSL-KDD dataset and show that the overall accuracy offered by the employed approach is high. The method of network intrusion detection based on deep learning is the hotspot of current research. This paper presents an intrusion detection model based on deep belief network and analyzes the NSL-KDD dataset. The experimental results show that the algorithm improves the detection of abnormal behavior pattern data and satisfies the requirement of high efficiency and reliability of intrusion detection system. In computer security, designing a robust intrusion detection system is one of the most fundamental and important problems. In this paper, we propose a system-call language-modeling approach for designing anomaly-based host intrusion detection systems. To remedy the issue of high false-alarm rates commonly arising in conventional methods, we employ a novel ensemble method that blends multiple thresholding classifiers into a single one, making it possible to accumulate 'highly normal' sequences. The proposed system-call language model has various advantages leveraged by the fact that it can learn the semantic meaning and interactions of each system call that existing methods cannot effectively consider. Through diverse experiments on public benchmark datasets, we demonstrate the validity and effectiveness of the proposed method. Moreover, we show that our model possesses high portability, which is one of the key aspects of realizing successful intrusion detection systems.