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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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. 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. 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. We analyze the increasing threats against IoT devices. We show that Telnet-based attacks that target IoT devices have rocketed since 2014. Based on this observation, we propose an IoT honeypot and sandbox, which attracts and analyzes Telnet-based attacks against various IoT devices running on different CPU architectures such as ARM, MIPS, and PPC. By analyzing the observation results of our honeypot and captured malware samples, we show that there are currently at least 4 distinct DDoS malware families targeting Telnet-enabled IoT devices and one of the families has quickly evolved to target more devices with as many as 9 different CPU architectures. 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. Advanced persistent threat (APT) is a serious threat to the Internet. With the aid of APT malware, attackers can remotely control infected machines and steal sensitive information. DNS is popular for malware to locate command and control (C&C) servers. In this paper, we propose a novel system placed at the network egress point that aims to efficiently and effectively detect APT malware infections based on malicious DNS and traffic analysis. The system uses malicious DNS analysis techniques to detect suspicious APT malware C&C domains, and then analyzes the traffic of the corresponding suspicious IP using the signature-based and anomaly based detection technology. We extracted 14 features based on big data to characterize different properties of malware-related DNS and the ways that they are queried, and we also defined network traffic features that can identify the traffic of compromised clients that have remotely been controlled. We built a reputation engine to compute a reputation score for an IP address using these features vector together. Our experiment was performed at a large local institute network for two months, and all the features were studied with big data, which includes @math million DNS queries. Our security approach cannot only substantially reduce the volume of network traffic that needs to be recorded and analyzed but also improve the sustainability of the system. 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. Due to its popularity and open-source nature, Android is the mobile platform that has been targeted the most by malware that aim to steal personal information or to control the users’ devices. More specifically, mobile botnets are malware that allow an attacker to remotely control the victims’ devices through different channels like HTTP, thus creating malicious networks of bots. In this paper, we show how it is possible to effectively group mobile botnets families by analyzing the HTTP traffic they generate. To do so, we create malware clusters by looking at specific statistical information that are related to the HTTP traffic. This approach also allows us to extract signatures with which it is possible to precisely detect new malware that belong to the clustered families. Contrarily to x86 malware, we show that using fine-grained HTTP structural features do not increase detection performances. Finally, we point out how the HTTP information flow among mobile bots contains more information when compared to the one generated by desktop ones, allowing for a more precise detection of mobile threats. This article presents Andromaly--a framework for detecting malware on Android mobile devices. The proposed framework realizes a Host-based Malware Detection System that continuously monitors various features and events obtained from the mobile device and then applies Machine Learning anomaly detectors to classify the collected data as normal (benign) or abnormal (malicious). Since no malicious applications are yet available for Android, we developed four malicious applications, and evaluated Andromaly's ability to detect new malware based on samples of known malware. We evaluated several combinations of anomaly detection algorithms, feature selection method and the number of top features in order to find the combination that yields the best performance in detecting new malware on Android. Empirical results suggest that the proposed framework is effective in detecting malware on mobile devices in general and on Android in particular. This paper describes EM-Based Detection of Deviations in Program Execution (EDDIE), a new method for detecting anomalies in program execution, such as malware and other code injections, without introducing any overheads, adding any hardware support, changing any software, or using any resources on the monitored system itself. Monitoring with EDDIE involves receiving electromagnetic (EM) emanations that are emitted as a side effect of execution on the monitored system, and it relies on spikes in the EM spectrum that are produced as a result of periodic (e.g. loop) activity in the monitored execution. During training, EDDIE characterizes normal execution behavior in terms of peaks in the EM spectrum that are observed at various points in the program execution, but it does not need any characterization of the malware or other code that might later be injected. During monitoring, EDDIE identifies peaks in the observed EM spectrum, and compares these peaks to those learned during training. Since EDDIE requires no resources on the monitored machine and no changes to the monitored software, it is especially well suited for security monitoring of embedded and IoT devices. We evaluate EDDIE on a real IoT system and in a cycle-accurate simulator, and find that even relatively brief injected bursts of activity (a few milliseconds) are detected by EDDIE with high accuracy, and that it also accurately detects when even a few instructions are injected into an existing loop within the application. 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. Android's market experienced exponential popularity during the last few years. This blazing growth has, unfortunately, opened the door to thousands of malicious applications targeting Android devices everyday. Moreover, with the increasing sophistication of today's malware, the use of traditional hashing techniques for Android malware fingerprinting becomes defenseless against polymorphic malicious applications. Inspired by fuzzy hashing techniques, we propose, in this paper, a novel and comprehensive fingerprinting approach for Android packaging APK. The proposed fingerprint captures, not only the binary features of the APK file, but also the underlying structure of the app. Furthermore, we leverage this fingerprinting technique to build ROAR, an automatic system for Android malware detection and family attribution. Our experiments show that the proposed fingerprint and the ROAR system achieve a precision of 95 . 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. Smartphones are becoming more and more popular and, as a consequence, malware writers are increasingly engaged to develop new threats and propagate them through official and third-party markets. In addition to the propagation vectors, malware is also evolving quickly the techniques adopted for infecting victims and hiding their malicious nature to antimalware scanning. From SMS Trojans to legitimate applications repacked with malicious payload, from AES encrypted root exploits to the dynamic loading of a payload retrieved from a remote server: malicious code is becoming more and more hard to detect.In this paper we experimentally evaluate two techniques for detecting Android malware: the first one is based on Hidden Markov Model, while the second one exploits structural entropy. These two techniques have been successfully applied to detect PCs viruses in previous works, and only one work in literature analyzes the application of HMM to the detection of Android malware. We demonstrate that these methods, which reveal effective for PCs virus, are also successful for detecting and classifying mobile malware.Our results are promising: we obtain a precision of 0.96 to discriminate a malware application, and a precision of 0.978 to identify the malware family. 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. We present a new kind of network perimeter monitoring strategy, which focuses on recognizing the infection and coordination dialog that occurs during a successful malware infection. BotHunter is an application designed to track the two-way communication flows between internal assets and external entities, developing an evidence trail of data exchanges that match a state-based infection sequence model. BotHunter consists of a correlation engine that is driven by three malware-focused network packet sensors, each charged with detecting specific stages of the malware infection process, including inbound scanning, exploit usage, egg downloading, outbound bot coordination dialog, and outbound attack propagation. The BotHunter correlator then ties together the dialog trail of inbound intrusion alarms with those outbound communication patterns that are highly indicative of successful local host infection. When a sequence of evidence is found to match BotHunter's infection dialog model, a consolidated report is produced to capture all the relevant events and event sources that played a role during the infection process. We refer to this analytical strategy of matching the dialog flows between internal assets and the broader Internet as dialog-based correlation, and contrast this strategy to other intrusion detection and alert correlation methods. We present our experimental results using BotHunter in both virtual and live testing environments, and discuss our Internet release of the BotHunter prototype. BotHunter is made available both for operational use and to help stimulate research in understanding the life cycle of malware infections. 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. The Mirai botnet, composed primarily of embedded and IoT devices, took the Internet by storm in late 2016 when it overwhelmed several high-profile targets with massive distributed denial-of-service (DDoS) attacks. In this paper, we provide a seven-month retrospective analysis of Mirai's growth to a peak of 600k infections and a history of its DDoS victims. By combining a variety of measurement perspectives, we analyze how the botnet emerged, what classes of devices were affected, and how Mirai variants evolved and competed for vulnerable hosts. Our measurements serve as a lens into the fragile ecosystem of IoT devices. We argue that Mirai may represent a sea change in the evolutionary development of botnets--the simplicity through which devices were infected and its precipitous growth, demonstrate that novice malicious techniques can compromise enough low-end devices to threaten even some of the best-defended targets. To address this risk, we recommend technical and nontechnical interventions, as well as propose future research directions. 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.