IoT malware classification based on reinterpreted function-call graphs

Internet-of-Things IoT devices are increasingly targeted Uy adversaries due to their unique characteristics such as constant online connection, lack of protection, and full integration in people’s daily life. As attackers shift their targets towards IoT devices, malware has been developed to compromise IoT devices equipped with different CPU architectures. While malware detection has been a well-studied area for desktop PCs, heterogeneous processor architecture in IoT devices brings in unique challenges. Existing approaches utilize static or dynamic binary analysis for identifying malware characteristics, Uut they all fall short when dealing with IoT malware compiled for different architectures. In this paper, we propose an efficient signature generation method for IoT malware, which generates distinguishable signatures based on high-level structural, statistical and string feature vectors, as high-level features are more robust against code variations across different architectures. The generated signatures for each malware family can be used for developing lightweight malware detection tools to secure IoT devices. Extensive experiments with two datasets of 5,150 recent IoT malware samples show that our scheme can achieve 95.5% detection rate with 0% false positive rate. Moreover, the proposed scheme can achieve 85.2% detection rate in detecting novel IoT malware.

Malware family classification is grouping malware samples that have the same or similar characteristics into the same family. It plays a crucial role in understanding notable malicious patterns and recovering from malware infections. Although many machine learning approaches have been devised for this problem, there are still several open questions including, “Which features, classifiers, and evaluation metrics are better for malware familial classification”? In this paper, we propose a machine learning approach to Android malware family classification using built-in and custom permissions. Each Android app must declare proper permissions to access restricted resources or to perform restricted actions. Permission declaration is an efficient and obfuscation-resilient feature for malware analysis. We developed a malware family classification technique using permissions and conducted extensive experiments with several classifiers on a well-known dataset, DREBIN. We then evaluated the classifiers in terms of four metrics: macrolevel F1-score, accuracy, balanced accuracy (BAC), and the Matthews correlation coefficient (MCC). BAC and the MCC are known to be appropriate for evaluating imbalanced data classification. Our experimental results showed that: (i) custom permissions had a positive impact on classification performance; (ii) even when the same classifier and the same feature information were used, there was a difference up to 3.67% between accuracy and BAC; (iii) LightGBM and AdaBoost performed better than other classifiers we considered.

As location-aware IoT (Internet of Things) devices intend to spread across almost every application, IoT devices are going to require different GNSS (Global Navigation Satellite System) receiver design requirements. To deliver on IoT opportunities, system architects and designers need to look carefully at every aspect of system design and architecture to be able to enable the full potential of the IoT era. IoT devices are equipped with different connectivity sensors that can provide a reliable integrated location-awareness solution. IoT devices are mainly relying on GNSS chip to deliver a service when no other RF signal is available. Hence, GNSS chip is expected to maintain a robust and reliable performance even in harsh environment. The harsh environment includes multipath environment, NLOS (Non-Line of Sight) GNSS signal reception, weak GNSS signal power reception, superimposed motion (e.g., arm swing, floating platform fluctuation …etc.), operation under freezing temperature, and interference. The targeted low-cost system design along with the expected harsh operational environment derived us to face exceptional GNSS signal reception condition. This paper discusses lessons learned, and challenges faced signal acquisition, reacquisition, and tracking for several IoT applications. Different operational modes are also explored to accommodate various use cases scenarios for several IoT applications. These different power consumption operational modes allow the GNSS chip to achieve an Ultra-low power consumption which indeed required for IoT applications. This paper provides an overview about the addressed GNSS chip fabricated in a 28nm FD-SOI process. We demonstrate a system performance and power consumption comparison of this chip along with other state-of-the-art GNSS chip. We compare the positioning performance for this device alongside another state-of-the-art GNSS modules and Novatel’s tightly coupled GNSS/INS (Inertial Navigation System) reference solution at numerous scenarios ranging from walking, running, and driving at several sites under different environment.

With the increased popularity and wide adoption of Android as a mobile OS platform, it has been a major target for malware authors. Due to unprecedented rapid growth in the number, variants, and diversity of malware, detecting malware on the Android platform has become challenging. Beyond the detection of a malware, classifying the family the malware belongs to, helps security analysts to reuse malware removal techniques that is known to work for that family of malware. It takes manual analysis if a malware belongs to an unknown family. Therefore, classifying malware into exact family is important. This paper presents a technique and tool named MAPFam that applies machine learning on static features from the Manifest file and API packages to classify an Android malware into its family. This work is premised on a starting hypothesis that features extracted from API packages rather than on API calls lead to more precise classification. Our experiments indeed shows that API package based models provides ∼1.63X more accurate classification compared to an API call based method. Our machine learning based malware family classification system uses API packages, requested permissions, and other features from the Manifest files. The proposed family classification system achieves accuracy and average precision above 97% for the top 60 malware families by using only 81 features with 97.55% of model reliability rate (Kappa score). The experimental results also shows that MAPFam can perfectly identity 36 malware families.

Privacy leakage of Internet of Things (IoT) has become a great challenge with the popularity of IoT services through mobile networks, such as smart homes, wearables, and healthcare. While previous work summarized general structures to analyze IoT privacy and provide case studies of specific devices or scenarios, it is still challenging to conduct a comprehensive and systematic quantification study of large-scale IoT privacy leakage in real world. To combine systematic analyses with real-world measurements, we provide a method to quantify IoT privacy leakage on a large-scale mobile network traffic data set containing 47651 IoT devices. We generate privacy fingerprints and attribute them to a privacy quantification framework. The framework is constructed based on the semantics of multiple privacy sensitive markers selected from the traffic along with the involved network entity types in IoT (i.e., user, device, and platform), and the fingerprints are generated from sensitive information extracted in the traffic via their markers. Our quantification shows that IoT users, devices, and platforms have considerable risks, respectively. Moreover, IoT devices have a larger scale of privacy leakage than users and platforms, and they perform different daily patterns on privacy leakage following their working conditions. In addition, we present three case studies on the leakage of location information, application calling, and voice service, which illustrate that a third party can profile a network entity in both cyberspace and physical space.

SummaryMalware has considerably increased recently, posing a serious security danger to both people and enterprises. In order to distinguish and stop the negative effects of malware, a variety of machine and deep learning approaches have been used to detect it. However, while extracting malware features, the feature‐to‐feature spatial hierarchy is not taken into account by the existing techniques and as a result, information is lost during the pooling operation. Hence, a modified capsule deep neural network was developed in which discriminative features are extracted from three channel image derived from malware binary with considering feature‐to‐feature spatial hierarchy. Also, conventional capsule deep neural network is modified by adding a global average pooling layer before fully connected layer thereby classified the dataset as malicious or benign without any loss of information. Moreover, these malwares were not accurately classified based on their families using existing variants of convolutional neural network (CNN) since malware family variants can modify due to minute changes in malware binaries. Hence, a hybrid deep convolutional neural network (DCNN) and long‐short‐term memory (LSTM) has been utilized that determine minute changes in malware binaries using LSTM without vanishing gradient issue and effectively perform malware family classification using DCNN. As a result, the proposed approach successfully identifies malware in executable files and categorizes malware into families with 98.5% accuracy.

Third-party libraries (TPLs) are extensively utilized by developers to expedite the software development process and incorporate external functionalities. Nevertheless, insecure TPL reuse can lead to significant security risks. Existing methods, which involve extracting strings or conducting function matching, are employed to determine the presence of TPL code in the target binary. However, these methods often yield unsatisfactory results due to the recurrence of strings and the presence of numerous similar non-homologous functions. Furthermore, the variation in C/C++ binaries across different optimization options and architectures exacerbates the problem. Additionally, existing approaches struggle to identify specific pieces of reused code in the target binary, complicating the detection of complex reuse relationships and impeding downstream tasks. And, we call this issue the poor interpretability of TPL detection results. In this article, we observe that TPL reuse typically involves not just isolated functions but also areas encompassing several adjacent functions on the Function Call Graph (FCG). We introduce LibAM, a novel Area Matching framework that connects isolated functions into function areas on FCG and detects TPLs by comparing the similarity of these function areas, significantly mitigating the impact of different optimization options and architectures. Furthermore, LibAM is the first approach capable of detecting the exact reuse areas on FCG and offering substantial benefits for downstream tasks. To validate our approach, we compile the first TPL detection dataset for C/C++ binaries across various optimization options and architectures. Experimental results demonstrate that LibAM outperforms all existing TPL detection methods and provides interpretable evidence for TPL detection results by identifying exact reuse areas. We also evaluate LibAM’s scalability on large-scale, real-world binaries in IoT firmware and generate a list of potential vulnerabilities for these devices. Our experiments indicate that the Area Matching framework performs exceptionally well in the TPL detection task and holds promise for other binary similarity analysis tasks. Last but not least, by analyzing the detection results of IoT firmware, we make several interesting findings, for instance, different target binaries always tend to reuse the same code area of TPL. The datasets and source code used in this article are available at https://github.com/Siyuan-Li201/LibAM .

Internet of Things (IoT) devices are easy targets for attackers. Preventing and counter-fighting this threat impose to capture and analyze attackers’ behaviors. Several IoT-oriented honeypots have been proposed recently while, in parallel, emulation techniques for IoT devices have been improved to allow firmware analysis of this type of devices.In this paper, our objective is to consolidate these two facets in a single framework called HiFiPot. It is capable of creating a high-interaction honeypot on-the-fly with a high fidelity, i.e. from a firmware image. Our technique improves the most recent stateof-the-art solution to emulate an IoT device without scarifying the furtiveness. It is based on an iterative learning procedure to automatically correct emulation errors and to ensure Internet connectivity while maintaining these corrections invisible to the attackers. Out of the 1,000 firmware images tested, 443 (44,3%) can be deployed as honeypot. More than 500 instances of HiFiPot were deployed in the wild, and received about 1,900 HTTP traversal attacks, and downloaded 31 distinct malware binaries (out of 909) among which eight were unknown.

Malware classification plays a fundamental role among all the related tasks. Researchers and anti-virus vendors have proposed deep learning (DL) methods to deal with the fast emerging-malware families and samples. However, for ordinary deep learning methods, once the model is trained, the set of families that can be recognized is fixed. This is troublesome in practice to deal with emerging malware families or unknown families with scarce samples. To resolve this issue, we propose an incremental classification approach called IMC (Incremental Malware Classification) based on few-shot learning, and the classifier is implemented as a cosine similarity function between extracted features and feature vectors of target classes. IMC can efficiently extend the pre-trained model to unknown families dynamically with a handful of samples without losing the ability to recognize the families it has “seen”. In the process of adaptation, no re-training is needed and fast inference is realized by a single forward pass. We extensively evaluate our approach on a dataset named APIMDS where the framework achieves incremental ability to classify the unknown families with high accuracy while maintaining the ability to recognize the known families. To our best knowledge, this is the first approach to meet the requirements to unify the classification of both unknown and known malware families in a few-shot manner.

THINGS-data reflects three large-scale neuroimaging and behavioral datasets of object processing in humans, comprising densely sampled functional MRI and magnetoencephalographic recordings, as well as 4.70 million similarity judgments in response to thousands of photographic images for up to 1854 objects.

The Internet of Things (IoT) represents a paradigm shift in which the Internet is connected to real objects in a range of areas, including home automation, industrial processes, human health, and environmental monitoring. The global market for IoT devices is booming, and it is estimated that there will be 50 billion connected devices by the end of 2025. This explosion of IoT devices, which can be expanded more easily than desktop PCs, has led to an increase in cyber-attacks involving IoT devices. To address this issue, it is necessary to create novel approaches for identifying attacks launched by hacked IoT devices. Due to the possibility that these attacks would succeed, Intrusion Detection Systems (IDS) are required. IDS' feature selection stage is widely regarded as the most essential stage. This stage is extremely time-consuming and labor-intensive. However, numerous machine learning (ML) algorithms have been proposed to enhance this stage to boost an IDS's performance. These approaches, however, did not produce desirable results in terms of accuracy and detection rate (DR). In this paper, we propose a novel hybrid Autoencoder and Modified Particle Swarm Optimization (HAEMPSO) for feature selection and deep neural network (DNN) for classification. The PSO with modification of inertia weight was utilized to optimize the parameters of DNN. The experimental analysis was performed on two realistic UNSW-NB15 and BoT-IoT datasets that are suitable for IoT environment. The findings obtained by analyzing the proposed HAEMPSO against the Generic attack in the UNSW-NB15 dataset gave an accuracy of 98.8%, and a DR of 99.9%. While the benign class revealed an accuracy of 99.9% and DR of 99.7%. In the BoT-IoT dataset, the DDoS HTTP attack revealed an accuracy of 99.22% and DR of 97.79%. While the benign class gave an accuracy of 97.54% and DR of 97.92%. In comparison with the state-of-the-art machine learning schemes, our proposed HAEMPSO-DNN achieved a competitive feat in terms of DR and accuracy.

Malware is one of the major threats on internet whose count is increasing rapidly every year in millions. Most of the time similar malware files are modified for creation of new variants and most of the existing technique are obfuscated. So, malware visualization using image helps to overcome this problem. Motivated by the visual similarities of malware, this paper presents the malware family classification using some texture-based features. HOG (Histogram of Oriented Gradient), LBP (Local Binary Pattern) and GIST features are used as feature descriptor. We have performed our experiment on 6370 samples of 15 different families. We have observed the highest accuracy of 98.73% using HOG descriptors by SVM classifier. This method shows better results than existing malware classification approaches.

Automated malware classification assigns unknown malware to known families. Most research in malware classification assumes that the defender has access to the malware for analysis. Unfortunately, malware can delete itself after execution. As a result, analysts are only left with digital residue, such as network logs or remnant artifacts of malware in memory or on the file system. In this paper, a novel malware classification method based on the Windows prefetch mechanism is presented and evaluated, enabling analysts to classify malware without a corresponding executable. The approach extracts features from Windows prefetch files, a file system artifact that contains historical process information such as loaded libraries and process dependencies. Results show that classification using these features with two different algorithms garnered F-Scores between 0.80 and 0.82, offering analysts a viable option for forensic analysis.

In the past few years, the malware industry has been thriving. Malware variants among the same malware family shared similar behavioural patterns or signatures reflecting their purpose. We propose an approach that combines support vector machine (SVM) classifiers and active learning by learning (ALBL) techniques to deal with insufficient labeled data in terms of the malware classification tasks. The proposed approach is evaluated with the malware family dataset from Microsoft Malware Classification Challenge (BIG 2015) on Kaggle. The results show that ALBL techniques can effectively boost the performance of our machine learning models and improve the quality of labeled samples.

IoT devices are often a vector for assembling massive botnets, as a consequence of being broadly available, having limited security protections, and significant challenges in deploying software upgrades. Such botnets are usually controlled by centralized Command-and-Control (C&C) servers, which need to be identified and taken down to mitigate threats. In this paper we propose a framework to infer C&C server IP addresses using four heuristics. Our heuristics employ static and dynamic analysis to automatically extract information from malware binaries. We use active measurements to validate inferences, and demonstrate the efficacy of our framework by identifying and characterizing C&C servers for 62% of 1050 malware binaries collected using 47 honeypots.