Biblio

With the ever increasing threat of malware, extensive research effort has been put on applying Deep Learning for malware classification tasks. Graph Neural Networks (GNNs) that process malware as Control Flow Graphs (CFGs) have shown great promise for malware classification. However, these models are viewed as black-boxes, which makes it hard to validate and identify malicious patterns. To that end, we propose CFG-Explainer, a deep learning based model for interpreting GNN-oriented malware classification results. CFGExplainer identifies a subgraph of the malware CFG that contributes most towards classification and provides insight into importance of the nodes (i.e., basic blocks) within it. To the best of our knowledge, CFGExplainer is the first work that explains GNN-based mal-ware classification. We compared CFGExplainer against three explainers, namely GNNExplainer, SubgraphX and PGExplainer, and showed that CFGExplainer is able to identify top equisized subgraphs with higher classification accuracy than the other three models.

The concept of a decoy process is a new development of defensive deception beyond traditional honeypots. Decoy processes can be exceptionally effective in detecting malware, directly upon contact or by redirecting malware to decoy I/O. A key requirement is that they resemble their real counterparts very closely to withstand adversarial probes by threat actors. To be usable, decoy processes need to consume only a small fraction of the resources consumed by their real counterparts. Our contribution in this paper is twofold. We attack the resource utilization consistency of decoy processes provided by a neural network with a heatmap training mechanism, which we find to be insufficiently trained. We then devise machine learning over control flow graphs that improves the heatmap training mechanism. A neural network retrained by our work shows higher accuracy and defeats our attacks without a significant increase in its own resource utilization.

Code reuse attacks can bypass the DEP mechanism effectively. Meanwhile, because of the stealthy of the operation, it becomes one of the most intractable threats while securing the information system. Although the security solutions of code randomization and diversity can mitigate the threat at a certain extent, attackers can bypass these solutions due to the high cost and coarsely granularity, and the memory disclosure vulnerability is another magic weapon which can be used by attackers to bypass these solutions. After analyzing the principle of memory disclosure vulnerability, we propose a novel code pointer hiding method based on a resilient area. We expatiate how to create the resilient area and achieve code pointer hiding from four aspects, namely hiding return addresses in data pages, hiding function pointers in data pages, hiding target pointers of instruction JUMP in code pages, and hiding target pointers of instruction CALL in code pages. This method can stop attackers from reading and analyzing pages in memory, which is a critical stage in finding and creating ROP chains while executing a code reuse attack. Lastly, we test the method contrastively, and the results show that the method is feasible and effective while defending against ROP attacks.

Routing security plays an important role in Mobile Ad hoc Networks (MANETs). Despite many attempts to improve its security, the routing procedure of MANETs remains vulnerable to attacks. Existing approaches offer support for detecting attacks or debugging in different routing phases, but many of them have not considered the privacy of the nodes during the anomalies detection, which depend on the central control program or a third party to supervise the whole network. In this paper, we present an approach called LAD which uses the raw logs of routers to construct control a flow graph and find the existing communication rules in MANETs. With the reasoning rules, LAD can detect both active and passive attacks launched during the routing phase. LAD can also protect the privacy of the nodes in the verification phase with the specific Merkle hash tree. Without deploying any special nodes to assist the verification, LAD can detect multiple malicious nodes by itself. To show that our approach can be used to guarantee the security of the MANETs, we deploy our experiment in NS3 as well as the practical router environment. LAD can improve the accuracy rate from 2.28% to 29.22%. The results show that LAD performs limited time and memory usages, high detection and low false positives.

Data dependency flow have been reformulated as Context Free Grammar (CFG) reachability problem, and the idea was explored in detection of some web vulnerabilities, particularly Cross Site Scripting (XSS) and Access Control. However, reformulation of SQL Injection Vulnerability (SQLIV) detection as grammar reachability problem has not been investigated. In this paper, concepts of data dependency flow was used to reformulate SQLIVs detection as a CFG reachability problem. The paper, consequently defines reachability analysis strategy for SQLIVs detection.

With growing popularity of Android, it's attack surface has also increased. Prevalence of third party android marketplaces gives attackers an opportunity to plant their malicious apps in the mobile eco-system. To evade signature based detection, attackers often transform their malware, for instance, by introducing code level changes. In this paper we propose a lightweight static Permission Flow Graph (PFG) based approach to detect malware even when they have been transformed (obfuscated). A number of techniques based on behavioral analysis have also been proposed in the past; how-ever our interest lies in leveraging the permission framework alone to detect malware variants and transformations without considering behavioral aspects of a malware. Our proposed approach constructs Permission Flow Graph (PFG) for an Android App. Transformations performed at code level, often result in changing control flow, however, most of the time, the permission flow remains invariant. As a consequences, PFGs of transformed malware and non-transformed malware remain structurally similar as shown in this paper using state-of-the-art graph similarity algorithm. Furthermore, we propose graph based similarity metrics at both edge level and vertex level in order to bring forth the structural similarity of the two PFGs being compared. We validate our proposed methodology through machine learning algorithms. Results prove that our approach is successfully able to group together Android malware and its variants (transformations) together in the same cluster. Further, we demonstrate that our proposed approach is able to detect transformed malware with a detection accuracy of 98.26%, thereby ensuring that malicious Apps can be detected even after transformations.

Nowadays, an increasing number of IoT vendors have complied and deployed third-party code bases across different architectures. Therefore, to avoid the firmware from being affected by the same known vulnerabilities, searching known vulnerabilities in binary firmware across different architectures is more crucial than ever. However, most of existing vulnerability search methods are limited to the same architecture, there are only a few researches on cross-architecture cases, of which the accuracy is not high. In this paper, to promote the accuracy of existing cross-architecture vulnerability search methods, we propose a new approach based on Support Vector Machine (SVM) and Attributed Control Flow Graph (ACFG) to search known vulnerability in firmware across different architectures at function level. We employ a known vulnerability function to recognize suspicious functions in other binary firmware. First, considering from the internal and external characteristics of the functions, we extract the function level features and basic-block level features of the functions to be inspected. Second, we employ SVM to recognize a little part of suspicious functions based on function level features. After the preliminary screening, we compute the graph similarity between the vulnerability function and suspicious functions based on their ACFGs. We have implemented our approach CVSSA, and employed the training samples to train the model with previous knowledge to improve the accuracy. We also search several vulnerabilities in the real-world firmware images, the experimental results show that CVSSA can be applied to the realistic scenarios.