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.
Third, unlike @cite_6 @cite_9 , we aim to detect IoT malware activity much before the actual attack, during the scanning infection phase. Finally, instead of fingerprinting the normal traffic of IoT devices @cite_10 @cite_9 and using those fingerprints towards anomaly detection, we detect the malware-induced scanning packet traffic generated by infected IoT devices. This is because the former approach suffers from limitations such as possibility of misclassification of an infected device as a legitimate device type, testing against only simple malware e.g. Mirai which may result in failure to detect other, more sophisticated malware, etc. The latter approach is not free from limitations as well, since it is not resilient against new undiscovered malware whose scanning traffic features have not been updated in the database. We advocate for a combined approach consisting of both IoT device fingerprinting anomaly detection and IoT malware scanning traffic detection.
@cite_9 @cite_10 @cite_6
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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. Abstract In this paper we present a new behavior-based anomaly detection system for detecting meaningful deviations in a mobile application's network behavior. The main goal of the proposed system is to protect mobile device users and cellular infrastructure companies from malicious applications by: (1) identification of malicious attacks or masquerading applications installed on a mobile device, and (2) identification of republished popular applications injected with a malicious code (i.e., repackaging). More specifically, we attempt to detect a new type of mobile malware with self-updating capabilities that were recently found on the official Google Android marketplace. Malware of this type cannot be detected using the standard signatures approach or by applying regular static or dynamic analysis methods. The detection is performed based on the application's network traffic patterns only. For each application, a model representing its specific traffic pattern is learned locally (i.e., on the device). Semi-supervised machine-learning methods are used for learning the normal behavioral patterns and for detecting deviations from the application's expected behavior. These methods were implemented and evaluated on Android devices. The evaluation experiments demonstrate that: (1) various applications have specific network traffic patterns and certain application categories can be distinguished by their network patterns; (2) different levels of deviation from normal behavior can be detected accurately; (3) in the case of self-updating malware, original (benign) and infected versions of an application have different and distinguishable network traffic patterns that in most cases, can be detected within a few minutes after the malware is executed while presenting very low false alarms rate; and (4) local learning is feasible and has a low performance overhead on mobile devices. The emergence of malicious apps poses a serious threat to the Android platform. Most types of mobile malware rely on network interface to coordinate operations, steal users' private information, and launch attack activities. In this paper, we propose an effective and automatic malware detection method using the text semantics of network traffic. In particular, we consider each HTTP flow generated by mobile apps as a text document, which can be processed by natural language processing to extract text-level features. Then, we use the text semantic features of network traffic to develop an effective malware detection model. In an evaluation using 31 706 benign flows and 5258 malicious flows, our method outperforms the existing approaches, and gets an accuracy of 99.15 . We also conduct experiments to verify that the method is effective in detecting newly discovered malware, and requires only a few samples to achieve a good detection result. When the detection model is applied to the real environment to detect unknown applications in the wild, the experimental results show that our method performs significantly better than other popular anti-virus scanners with a detection rate of 54.81 . Our method also reveals certain malware types that can avoid the detection of anti-virus scanners. In addition, we design a detection system on encrypted traffic for bring-your-own-device enterprise network, home network, and 3G 4G mobile network. The detection model is integrated into the system to discover suspicious network behaviors. Identifying IoT devices connected to a network has multiple security benefits, such as deployment of behavior-based anomaly detectors, automated vulnerability patching of specific device types, dynamic attack mitigation, etc. In this paper, we look into the problem of IoT device identification at network level, in particular from an ISP’s perspective. The simple solution of deploying a supervised machine learning algorithm at a centralized location in the network neither scales well nor can identify new devices. To tackle these challenges, we propose and develop a distributed device fingerprinting technique (DEFT), a distributed fingerprinting solution that addresses and exploits the presence of common devices, including new devices, across smart homes and enterprises in a network. A DEFT controller develops and maintains classifiers for fingerprinting, while gateways located closer to the IoT devices at homes perform device classification. Importantly, the controller and gateways coordinate to identify new devices in the network. DEFT is designed to be scalable and dynamic—it can be deployed, orchestrated, and controlled using software-defined networking and network function virtualization. DEFT is able to identify new device types automatically, while achieving high accuracy and low false positive rate. We demonstrate the effectiveness of DEFT by experimenting on data obtained from real-world IoT devices. 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. Increase of malware and advanced cyber-attacks are now becoming a serious problem. Unknown malware which has not determined by security vendors is often used in these attacks, and it is becoming difficult to protect terminals from their infection. Therefore, a countermeasure for after infection is required. There are some malware infection detection methods which focus on the traffic data comes from malware. However, it is difficult to perfectly detect infection only using traffic data because it imitates benign traffic. In this paper, we propose malware process detection method based on process behavior in possible infected terminals. In proposal, we investigated stepwise application of Deep Neural Networks to classify malware process. First, we train the Recurrent Neural Network (RNN) to extract features of process behavior. Second, we train the Convolutional Neural Network (CNN) to classify feature images which are generated by the extracted features from the trained RNN. The evaluation result in several image size by comparing the AUC of obtained ROC curves and we obtained AUC= 0:96 in best case. The drastic increase of Android malware has led to a strong interest in developing methods to automate the malware analysis process. Existing automated Android malware detection and classification methods fall into two general categories: 1) signature-based and 2) machine learning-based. Signature-based approaches can be easily evaded by bytecode-level transformation attacks. Prior learning-based works extract features from application syntax, rather than program semantics, and are also subject to evasion. In this paper, we propose a novel semantic-based approach that classifies Android malware via dependency graphs. To battle transformation attacks, we extract a weighted contextual API dependency graph as program semantics to construct feature sets. To fight against malware variants and zero-day malware, we introduce graph similarity metrics to uncover homogeneous application behaviors while tolerating minor implementation differences. We implement a prototype system, DroidSIFT, in 23 thousand lines of Java code. We evaluate our system using 2200 malware samples and 13500 benign samples. Experiments show that our signature detection can correctly label 93 of malware instances; our anomaly detector is capable of detecting zero-day malware with a low false negative rate (2 ) and an acceptable false positive rate (5.15 ) for a vetting purpose. The Internet of Things (IoT) is increasingly used for critical applications and securing the IoT has become a major concern. Among other issues it is important to ensure that tampering with IoT devices is detected. Many IoT devices use WiFi for communication and Channel State Information (CSI) based tamper detection is a valid option. Each 802.11n WiFi frame contains a preamble which allows a receiver to estimate the impact of the wireless channel, the transmitter and the receiver on the signal. The estimation result - the CSI - is used by a receiver to extract the transmitted information. However, as the CSI depends on the communication environment and the transmitter hardware, it can be used as well for security purposes. If an attacker tampers with a transmitter it will have an effect on the CSI measured at a receiver. Unfortunately not only tamper events lead to CSI fluctuations; movement of people in the communication environment has an impact too. We propose to analyse CSI values of a transmission simultaneously at multiple receivers to improve distinction of tamper and movement events. A moving person is expected to have an impact on some but not all communication links between transmitter and the receivers. A tamper event impacts on all links between transmitter and the receivers. The paper describes the necessary algorithms for the proposed tamper detection method. In particular we analyse the tamper detection capability in practical deployments with varying intensity of people movement. In our experiments the proposed system deployed in a busy office environment was capable to detect 53 of tamper events (TPR = 53 ) while creating zero false alarms (FPR = 0 ). This paper presents a system for detecting intrusions when analyzing the network traffic payload looking for malware evidences. The system implements the detection algorithm as a Snort preprocessor component. Since they work together, a highly effective system against known attacks has been achieved (based on Snort rules) and a highly effective system against unknown threats (which was the main aim of the designed system). As the majority of such systems, the proposal consists of two phases: a training phase and a detection phase. During the training phase a statistical model of the legitimate network usage is created through Bloom Filters and N-grams techniques. Subsequently, the results obtained by analyzing a dataset of attacks are compared with such model. This will allow a set of rules to be developed which will be able to determine whether the packets payloads contain malware. In the detection phase, the traffic to analyze is compared with the model created in the training phase and the results obtained when applying rules. The performed experiments showed really satisfactory results, with 100 malware detection and just 0.15 false positives. Due to its severe damages and threats to the security of the Internet and computing devices, malware detection has caught the attention of both anti-malware industry and researchers for decades. To combat the evolving malware attacks, in this paper, we first study how to utilize both content- and relation-based features to characterize sly malware; to model different types of entities (i.e., file, archive, machine, API, DLL ) and the rich semantic relationships among them (i.e., file-archive, file-machine, file-file, API-DLL, file-API relations), we then construct a structural heterogeneous information network (HIN) and present meta-graph based approach to depict the relatedness over files. To measure the relatedness over files on the constructed HIN, since malware detection is a cost-sensitive task, it calls for efficient methods to learn latent representations for HIN. To address this challenge, based on the built meta-graph schemes, we propose a new HIN embedding model metagraph2vec on the first attempt to learn the low-dimensional representations for the nodes in HIN, where both the HIN structures and semantics are maximally preserved for malware detection. A comprehensive experimental study on the real sample collections from Comodo Cloud Security Center is performed to compare various malware detection approaches. The promising experimental results demonstrate that our developed system Scorpion which integrate our proposed method outperforms other alternative malware detection techniques. The developed system has already been incorporated into the scanning tool of Comodo Antivirus product. 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. Smart devices equipped with powerful sensing, computing and networking capabilities have proliferated lately, ranging from popular smartphones and tablets to Internet appliances, smart TVs, and others that will soon appear (e.g., watches, glasses, and clothes). One key feature of such devices is their ability to incorporate third-party apps from a variety of markets. This poses strong security and privacy issues to users and infrastructure operators, particularly through software of malicious (or dubious) nature that can easily get access to the services provided by the device and collect sensory data and personal information. Malware in current smart devices —mostly smartphones and tablets— have rocketed in the last few years, in some cases supported by sophisticated techniques purposely designed to overcome security architectures currently in use by such devices. Even though important advances have been made on malware detection in traditional personal computers during the last decades, adopting and adapting those techniques to smart devices is a challenging problem. For example, power consumption is one major constraint that makes unaffordable to run traditional detection engines on the device, while externalized (i.e., cloud-based) techniques rise many privacy concerns. This article examines the problem of malware in smart devices and recent progress made in detection techniques. We first present a detailed analysis on how malware has evolved over the last years for the most popular platforms. We identify exhibited behaviors, pursued goals, infection and distribution strategies, etc. and provide numerous examples through case studies of the most relevant specimens. We next survey, classify and discuss efforts made on detecting both malware and other suspicious software (grayware), concentrating on the 20 most relevant techniques proposed between 2010 and 2013. Based on the conclusions extracted from this study, we finally provide constructive discussion on open research problems and areas where we believe that more work is needed. Within the next few years, billions of IoT devices will densely populate our cities. In this paper we describe a new type of threat in which adjacent IoT devices will infect each other with a worm that will rapidly spread over large areas, provided that the density of compatible IoT devices exceeds a certain critical mass. In particular, we developed and verified such an infection using the popular Philips Hue smart lamps as a platform. The worm spreads by jumping directly from one lamp to its neighbors, using only their built-in ZigBee wireless connectivity and their physical proximity. The attack can start by plugging in a single infected bulb anywhere in the city, and then catastrophically spread everywhere within minutes. It enables the attacker to turn all the city lights on or off, to permanently brick them, or to exploit them in a massive DDOS attack. To demonstrate the risks involved, we use results from percolation theory to estimate the critical mass of installed devices for a typical city such as Paris whose area is about 105 square kilometers: The chain reaction will fizzle if there are fewer than about 15,000 randomly located smart lamps in the whole city, but will spread everywhere when the number exceeds this critical mass (which had almost certainly been surpassed already). To make such an attack possible, we had to find a way to remotely yank already installed lamps from their current networks, and to perform over-the-air firmware updates. We overcame the first problem by discovering and exploiting a major bug in the implementation of the Touchlink part of the ZigBee Light Link protocol, which is supposed to stop such attempts with a proximity test. To solve the second problem, we developed a new version of a side channel attack to extract the global AES-CCM key (for each device type) that Philips uses to encrypt and authenticate new firmware. We used only readily available equipment costing a few hundred dollars, and managed to find this key without seeing any actual updates. This demonstrates once again how difficult it is to get security right even for a large company that uses standard cryptographic techniques to protect a major product. Developing reliable security measures against outbreaks of malware will facilitate the proliferation of wireless sensing technologies. The first step toward this goal is to investigate potential attack strategies and the extent of damage they can incur. The malware at each infective node may seek to contact more susceptible nodes by amplifying the transmission range and the media scanning rate and thereby accelerate its spread. This may however lead to (a) easier detection of the malware and thus more effective counter-measure by the network, and (b) faster depletion of the battery which may in turn thwart further spread of the infection and or exploitation of that node. We assume the viewpoint of the malware and cast the problem of dynamically selecting the transmission range and media access rate of the infective nodes as an optimal control problem. We utilize Pontryagin's maximum principle to find an optimum solution, and prove that the maximum damage can be attained using simple three-phase bang-bang strategies. 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. 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 popularity of Android OS has dramatically increased malware apps targeting this mobile OS. The daily amount of malware has overwhelmed the detection process. This fact has motivated the need for developing malware detection and family attribution solutions with the least manual intervention. In response, we propose Cypider framework, a set of techniques and tools aiming to perform a systematic detection of mobile malware by building an efficient and scalable similarity network infrastructure of malicious apps. Our detection method is based on a novel concept, namely malicious community, in which we consider, for a given family, the instances that share common features. Under this concept, we assume that multiple similar Android apps with different authors are most likely to be malicious. Cypider leverages this assumption for the detection of variants of known malware families and zero-day malware. It is important to mention that Cypider does not rely on signature-based or learning-based patterns. Alternatively, it applies community detection algorithms on the similarity network, which extracts sub-graphs considered as suspicious and most likely malicious communities. Furthermore, we propose a novel fingerprinting technique, namely community fingerprint, based on a learning model for each malicious community. Cypider shows excellent results by detecting about 50 of the malware dataset in one detection iteration. Besides, the preliminary results of the community fingerprint are promising as we achieved 87 of the detection. A successful detection of the stealthy dopant-level circuit trojan, proposed by at CHES 2013 [1], is reported. Contrary to an assumption made by , dopant types in active region are visible with either scanning electron microscopy SEM or focused ion beam FIB imaging. The successful measurement is explained by an LSI failure analysis technique called the passive voltage contrast [2]. The experiments are conducted by measuring a dedicated chip. The chip uses the diffusion programmable device [3]: an anti-reverse-engineering technique by the same principle as the stealthy dopant-level trojan. The chip is delayered down to the contact layer, and images are taken with 1 an optical microscope, 2 SEM, and 3 FIB. As a result, the four possible dopant-well combinations, namely i p+ n-well, ii p+ p-well, iii n+ n-well and iv n+ p-well are distinguishable in the SEM images. Partial but sufficient detection is also achieved with FIB. Although the stealthy dopant-level circuits are visible, however, they potentially make a detection harder. That is because the contact layer should be measured. We show that imaging the contact layer is at most 16-times expensive than that of a met al layer in terms of the number of images. The proliferation of mobile computing devices has enabled immense opportunities for everyday users. At the same time, however, this has opened up new, and perhaps more severe, possibilities for attacks. In this paper, we explore a novel generation of mobile malware that exploits the rich variety of sensors available on current mobile devices. Two properties distinguish the proposed malware from the existing state-of-the-art. First, in addition to the misuse of the various traditional services available on modern mobile devices, this malware can be used for the purpose of targeted context-aware attacks. Second, this malware can be commanded and controlled over context-aware, out-of-band channels as opposed to a centralized infrastructure. These communication channels can be used to quickly reach out to a large number of infected devices, while offering a high degree of undetectability. In particular, unlike traditional network-based communication, the proposed sensing-enabled channels cannot be detected by monitoring the cellular or wireless communication networks. To demonstrate the feasibility of our proposed attack, we present different flavors of command and control channels based on acoustic, visual, magnetic and vibrational signaling. We further build and test a proof-of-concept Android application implementing many such channels.