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. 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. 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. 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. 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. 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. Network anomaly detection is an important and dynamic research area. Many network intrusion detection methods and systems (NIDS) have been proposed in the literature. In this paper, we provide a structured and comprehensive overview of various facets of network anomaly detection so that a researcher can become quickly familiar with every aspect of network anomaly detection. We present attacks normally encountered by network intrusion detection systems. We categorize existing network anomaly detection methods and systems based on the underlying computational techniques used. Within this framework, we briefly describe and compare a large number of network anomaly detection methods and systems. In addition, we also discuss tools that can be used by network defenders and datasets that researchers in network anomaly detection can use. We also highlight research directions in network anomaly detection. Information and Communication Technology (ICT) has a great impact on social wellbeing, economic growth and national security in todays world. Generally, ICT includes computers, mobile communication devices and networks. ICT is also embraced by a group of people with malicious intent, also known as network intruders, cyber criminals, etc. Confronting these detrimental cyber activities is one of the international priorities and important research area. Anomaly detection is an important data analysis task which is useful for identifying the network intrusions. This paper presents an in-depth analysis of four major categories of anomaly detection techniques which include classification, statistical, information theory and clustering. The paper also discusses research challenges with the datasets used for network intrusion detection. HighlightsMaps different types of anomalies with network attacks.Provides an up-to-date taxonomy of network anomaly detection.Evaluates effectiveness of different categories of techniques.Explores recent research related to publicly available network intrusion evaluation datasets. Over the past decade many anomaly-detection techniques have been proposed and or deployed to provide early warnings of cyberattacks, particularly of those attacks involving masqueraders and novel methods. To date, however, there appears to be no study which has identified a systematic method that could be used by an attacker to undermine an anomaly-based intrusion detection system. This paper shows how an adversary can craft an offensive mechanism that renders an anomaly-based intrusion detector blind to the presence of on-going, common attacks. It presents a method that identifies the weaknesses of an anomaly-based intrusion detector, and shows how an attacker can manipulate common attacks to exploit those weaknesses. The paper explores the implications of this threat, and suggests possible improvements for existing and future anomaly-based intrusion detection systems. 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. The IETF's push towards standardizing the Manufacturer Usage Description (MUD) grammar and mechanism for specifying IoT device behavior is gaining increasing interest from industry. The ability to control inappropriate communication between devices in the form of access control lists (ACLs) is expected to limit the attack surface on IoT devices; however, little is known about how MUD policies will get enforced in operational networks, and how they will interact with current and future intrusion detection systems (IDS). We believe this paper is the first attempt to translate MUD policies into flow rules that can be enforced using SDN, and in relating exception behavior to attacks that can be detected via off-the-shelf IDS. Our first contribution develops and implements a system that translates MUD policies to flow rules that are proactively configured into network switches, as well as reactively inserted based on run-time bindings of DNS. We use traces of 28 consumer IoT devices taken over several months to evaluate the performance of our system in terms of switch flow-table size and fraction of exception traffic that needs software inspection. Our second contribution identifies the limitations of flow-rules derived from MUD in protecting IoT devices from internal and external network attacks, and we show how our system is able to detect such volumetric attacks (including port scanning, TCP UDP ICMP flooding, ARP spoofing, and TCP SSDP SNMP reflection) by sending only a very small fraction of exception packets to off-the-shelf IDS. The Internet of things (IoT) is still in its infancy and has attracted much interest in many industrial sectors including medical fields, logistics tracking, smart cities and automobiles. However as a paradigm, it is susceptible to a range of significant intrusion threats. This paper presents a threat analysis of the IoT and uses an Artificial Neural Network (ANN) to combat these threats. A multi-level perceptron, a type of supervised ANN, is trained using internet packet traces, then is assessed on its ability to thwart Distributed Denial of Service (DDoS DoS) attacks. This paper focuses on the classification of normal and threat patterns on an IoT Network. The ANN procedure is validated against a simulated IoT network. The experimental results demonstrate 99.4 accuracy and can successfully detect various DDoS DoS attacks. As advances in networking technology help to connect the distant corners of the globe and as the Internet continues to expand its influence as a medium for communications and commerce, the threat from spammers, attackers and criminal enterprises has also grown accordingly. It is the prevalence of such threats that has made intrusion detection systems-the cyberspace's equivalent to the burglar alarm-join ranks with firewalls as one of the fundamental technologies for network security. However, today's commercially available intrusion detection systems are predominantly signature-based intrusion detection systems that are designed to detect known attacks by utilizing the signatures of those attacks. Such systems require frequent rule-base updates and signature updates, and are not capable of detecting unknown attacks. In contrast, anomaly detection systems, a subset of intrusion detection systems, model the normal system network behavior which enables them to be extremely effective in finding and foiling both known as well as unknown or ''zero day'' attacks. While anomaly detection systems are attractive conceptually, a host of technological problems need to be overcome before they can be widely adopted. These problems include: high false alarm rate, failure to scale to gigabit speeds, etc. In this paper, we provide a comprehensive survey of anomaly detection systems and hybrid intrusion detection systems of the recent past and present. We also discuss recent technological trends in anomaly detection and identify open problems and challenges in this area. In network intrusion detection research, one popular strategy for finding attacks is monitoring a network's activity for anomalies: deviations from profiles of normality previously learned from benign traffic, typically identified using tools borrowed from the machine learning community. However, despite extensive academic research one finds a striking gap in terms of actual deployments of such systems: compared with other intrusion detection approaches, machine learning is rarely employed in operational "real world" settings. We examine the differences between the network intrusion detection problem and other areas where machine learning regularly finds much more success. Our main claim is that the task of finding attacks is fundamentally different from these other applications, making it significantly harder for the intrusion detection community to employ machine learning effectively. We support this claim by identifying challenges particular to network intrusion detection, and provide a set of guidelines meant to strengthen future research on anomaly detection. The proliferation of IoT systems, has seen them targeted by malicious third parties. To address this, realistic protection and investigation countermeasures need to be developed. Such countermeasures include network intrusion detection and network forensic systems. For that purpose, a well-structured and representative dataset is paramount for training and validating the credibility of the systems. Although there are several network, in most cases, not much information is given about the Botnet scenarios that were used. This paper, proposes a new dataset, Bot-IoT, which incorporates legitimate and simulated IoT network traffic, along with various types of attacks. We also present a realistic testbed environment for addressing the existing dataset drawbacks of capturing complete network information, accurate labeling, as well as recent and complex attack diversity. Finally, we evaluate the reliability of the BoT-IoT dataset using different statistical and machine learning methods for forensics purposes compared with the existing datasets. This work provides the baseline for allowing botnet identificaiton across IoT-specifc networks. The Bot-IoT dataset can be accessed at [1]. With the advent of anomaly-based intrusion detection systems, many approaches and techniques have been developed to track novel attacks on the systems. High detection rate of 98 at a low alarm rate of 1 can be achieved by using these techniques. Though anomaly-based approaches are efficient, signature-based detection is preferred for mainstream implementation of intrusion detection systems. As a variety of anomaly detection techniques were suggested, it is difficult to compare the strengths, weaknesses of these methods. The reason why industries don‟t favor the anomaly-based intrusion detection methods can be well understood by validating the efficiencies of the all the methods. To investigate this issue, the current state of the experiment practice in the field of anomalybased intrusion detection is reviewed and survey recent studies in this. This paper contains summarization study and identification of the drawbacks of formerly surveyed works. 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. 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.