Graph anomaly detection, here, aims to find rare patterns that are significantly different from other nodes. Attributed graphs containing complex structure and attribute information are ubiquitous in our life scenarios such as bank account transaction graph and paper citation graph. Anomalous nodes on attributed graphs show great difference from others in the perspectives of structure and attributes, and give rise to various types of graph anomalies. In this paper, we investigate three types of graph anomalies: local, global, and structure anomalies. And, graph neural networks (GNNs) based anomaly detection methods attract considerable research interests due to the power of modeling attributed graphs. However, the convolution operation of GNNs aggregates neighbors information to represent nodes, which makes node representations more similar and cannot effectively distinguish between normal and anomalous nodes, thus result in sub-optimal results. To improve the performance of anomaly detection, we propose a novel community-aware attributed graph anomaly detection framework (ComGA). We design a tailored deep graph convolutional network (tGCN) to anomaly detection on attributed graphs. Extensive experiments on eight real-life graph datasets demonstrate the effectiveness of ComGA.

ComGA: Community-Aware Attributed Graph Anomaly Detection

We provide an overview of recent developments and future directions in the RowHammer vulnerability that plagues modern DRAM (Dynamic Random Memory Access) chips, which are used in almost all computing systems as main memory. RowHammer is the phenomenon in which repeatedly accessing a row in a real DRAM chip causes bitflips (i.e., data corruption) in physically nearby rows. This phenomenon leads to a serious and widespread system security vulnerability, as many works since the original RowHammer paper in 2014 have shown. Recent analysis of the RowHammer phenomenon reveals that the problem is getting much worse as DRAM technology scaling continues: newer DRAM chips are fundamentally more vulnerable to RowHammer at the device and circuit levels. Deeper analysis of RowHammer shows that there are many dimensions to the problem as the vulnerability is sensitive to many variables, including environmental conditions (temperature & voltage), process variation, stored data patterns, as well as memory access patterns and memory control policies. As such, it has proven difficult to devise fully-secure and very efficient (i.e., low-overhead in performance, energy, area) protection mechanisms against RowHammer and attempts made by DRAM manufacturers have been shown to lack security guarantees. After reviewing various recent developments in exploiting, understanding, and mitigating RowHammer, we discuss future directions that we believe are critical for solving the RowHammer problem. We argue for two major directions to amplify research and development efforts in: 1) building a much deeper understanding of the problem and its many dimensions, in both cutting-edge DRAM chips and computing systems deployed in the field, and 2) the design and development of extremely efficient and fully-secure solutions via system-memory cooperation.

Fundamentally Understanding and Solving RowHammer

To understand and improve DRAM performance, reliability, security and energy efficiency, prior works study characteristics of commodity DRAM chips. Unfortunately, state-of-the-art open source infrastructures capable of conducting such studies are obsolete, poorly supported, or difficult to use, or their inflexibility limit the types of studies they can conduct. We propose DRAM Bender, a new FPGA-based infrastructure that enables experimental studies on state-of-the-art DRAM chips. DRAM Bender offers three key features at the same time. First, DRAM Bender enables directly interfacing with a DRAM chip through its low-level interface. This allows users to issue DRAM commands in arbitrary order and with finer-grained time intervals compared to other open source infrastructures. Second, DRAM Bender exposes easy-to-use C++ and Python programming interfaces, allowing users to quickly and easily develop different types of DRAM experiments. Third, DRAM Bender is easily extensible. The modular design of DRAM Bender allows extending it to (i) support existing and emerging DRAM interfaces, and (ii) run on new commercial or custom FPGA boards with little effort. To demonstrate that DRAM Bender is a versatile infrastructure, we conduct three case studies, two of which lead to new observations about the DRAM RowHammer vulnerability. In particular, we show that data patterns supported by DRAM Bender uncovers a larger set of bit-flips on a victim row compared to the data patterns commonly used by prior work. We demonstrate the extensibility of DRAM Bender by implementing it on five different FPGAs with DDR4 and DDR3 support. DRAM Bender is freely and openly available at https://github.com/CMU-SAFARI/DRAM-Bender.

DRAM Bender: An Extensible and Versatile FPGA-based Infrastructure to Easily Test State-of-the-art DRAM Chips

Graph anomaly detection in this paper aims to distinguish abnormal nodes that behave differently from the benign ones accounting for the majority of graph-structured instances. Receiving increasing attention from both academia and industry, yet existing research on this task still suffers from two critical issues when learning informative anomalous behavior from graph data. For one thing, anomalies are usually hard to capture because of their subtle abnormal behavior and the shortage of background knowledge about them, which causes severe anomalous sample scarcity. Meanwhile, the overwhelming majority of objects in real-world graphs are normal, bringing the class imbalance problem as well. To bridge the gaps, this paper devises a novel Data Augmentation-based Graph Anomaly Detection (DAGAD) framework for attributed graphs, equipped with three specially designed modules: 1) an information fusion module employing graph neural network encoders to learn representations, 2) a graph data augmentation module that fertilizes the training set with generated samples, and 3) an imbalance-tailored learning module to discriminate the distributions of the minority (anomalous) and majority (normal) classes. A series of experiments on three datasets prove that DAGAD outperforms ten state-of-the-art baseline detectors concerning various mostly-used metrics, together with an extensive ablation study validating the strength of our proposed modules.

DAGAD: Data Augmentation for Graph Anomaly Detection

eRiskCom: an e-commerce risky community detection platform

Evolving Android malware poses a severe security threat to mobile users, and machine-learning (ML)-based defense techniques attract active research. Due to the lack of knowledge, many zero-day families’ malware may remain undetected until the classifier gains specialized knowledge. The most existing ML-based methods will take a long time to learn new malware families in the latest malware family landscape. Existing ML-based Android malware detection and classification methods struggle with the fast evolution of the malware landscape, particularly in terms of the emergence of zero-day malware families and limited representation of single-view features. In this article, a new multiview feature intelligence (MFI) framework is developed to learn the representation of a targeted capability from known malware families for recognizing unknown and evolving malware with the same capability. The new framework performs reverse engineering to extract multiview heterogeneous features, including semantic string features, API call graph features, and smali opcode sequential features. It can learn the representation of a targeted capability from known malware families through a series of processes of feature analysis, selection, aggregation, and encoding, to detect unknown Android malware with shared target capability. We create a new dataset with ground-truth information regarding capability. Many experiments are conducted on the new dataset to evaluate the performance and effectiveness of the new method. The results demonstrate that the new method outperforms three state-of-the-art methods, including: 1) Drebin; 2) MaMaDroid; and 3) $N$ -opcode, when detecting unknown Android malware with targeted capabilities.

Cyber Code Intelligence for Android Malware Detection

Rowhammer is a hardware vulnerability in DRAM memory, where repeated access to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hammer rows</i> can induce bit flips in neighboring <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">victim rows</i> . Being a hardware vulnerability, rowhammer bypasses all the system memory protection, allowing adversaries to compromise the integrity and confidentiality of data. Rowhammer attacks have shown to enable privilege escalation, sandbox escape, and cryptographic key disclosures. A key requirement of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">all</i> existing rowhammer attacks is that an attacker must have access to at least part of an exploitable hammer row. We term such rowhammer attacks as Explicit Hammer. Recently, several proposals suggest leveraging the spatial proximity between the accessed hammer rows and the location of the victim rows for a defense against rowhammer. These all aim to deny the attacker's permission to access hammer rows near sensitive data, thus defeating explicit hammer-based attacks. In this paper, we question the core assumption underlying these defenses. We present Implicit Hammer, a confused-deputy attack that causes accesses to hammer rows that the attacker is not allowed to access. It is a paradigm shift in rowhammer attacks since it crosses privilege boundary to stealthily rowhammer an inaccessible row by implicit DRAM accesses. Such accesses are achieved by abusing inherent features of modern hardware and/or software. We propose a generic model to rigorously formalize the necessary conditions to initiate implicit hammer and explicit hammer, respectively. Compared to explicit hammer, implicit hammer can defeat the advanced software-only defenses, stealthy in hiding itself and hard to be mitigated. To demonstrate the practicality of implicit hammer, we have created two implicit hammer's instances, called PThammer and SyscallHammer. Specifically, PThammer exploits the page-table-walk process of modern processors, inducing the processor to generate frequent accesses to protected memory locations. SyscallHammer exploits system call handler routine of OS kernels to implicitly access kernel memory. 

Surya Nepal

Papers

eRiskCom: an e-commerce risky community detection platform

Cyber Code Intelligence for Android Malware Detection

eRiskCom: an e-commerce risky community detection platform

Implicit Hammer: Cross-Privilege-Boundary Rowhammer through Implicit Accesses

Can differential privacy practically protect collaborative deep learning inference for IoT?