With the advent of new IEEE 802.11ax (WiFi 6) devices, enabling security is a priority. Since previous versions were found to have security vulnerabilities, to fix the most common security flaws, the WiFi Protected Access 3 (WPA3) got introduced. Although WPA3 is an improvement over its predecessor in terms of security, recently it was found that WPA3 has a few security vulnerabilities as well. In this paper, we have mentioned the previously known vulnerabilities in WPA3 and WPA2. In addition to that, we have created our own dataset based on WPA3 attacks (Section III). We have proposed a two-stage solution for the detection of an intrusion in the network. The two-stage approach will help ease computational processing burden of an AP and WLAN Controller. First, AP will perform a lightweight simple operation for some duration (say 500ms) at certain time interval. Upon discovering any abnormality in the flow of traffic an ML-based solution at the controller will detect the type of attack. Our approach is to utilize resources on AP as well as the back-end controller with certain level of optimization. We have achieved over 99% accuracy in attack detection using an ML-based solution. We have also publicly provided our code and dataset for the open-source research community, so that it can contribute for future research work.
There have been numerous works on network intrusion detection and prevention systems, but work on application layer intrusion detection and prevention is rare and not very mature. Intrusion detection and prevention at both network and application layers are important for cyber-security and enterprise system security. Since application layer intrusion is increasing day by day, it is imperative to give adequate attention to it and use state-of-the-art algorithms for effective detection and prevention. This paper talks about current state of application layer intrusion detection and prevention ca…
Mobile robots are cyber-physical systems where the cyberspace and the physical world are strongly coupled. Attacks against mobile robots can transcend cyber defenses and escalate into disastrous consequences in the physical world. In this paper, we focus on the detection of active attacks that are capable of directly influencing robot mission operation. Through leveraging physical dynamics of mobile robots, we develop RIDS, a novel robot intrusion detection system that can detect actuator attacks as well as sensor attacks for nonlinear mobile robots subject to stochastic noises. We implement a…
An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection
Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), School of Engineering, Polytechnic of Porto (ISEP/IPP)（高级创新与发展智能工程与计算研究组（GECAD），波尔图理工学院工程学院（ISEP/IPP））
Intrusion detection system (IDS) is an important part of enterprise security system architecture. In particular, anomaly-based IDS has been widely applied to detect abnormal process behaviors that deviate from the majority. However, such abnormal behavior usually consists of a series of low-level heterogeneous events. The gap between the low-level events and the high-level abnormal behaviors makes it hard to infer which single events are related to the real abnormal activities, especially considering that there are massive "noisy" low-level events happening in between. Hence, the existing work…
The digitisation of industry provides a plethora of novel applications that increase flexibility and reduce setup and maintenance time as well as cost. Furthermore, novel use cases are created by the digitisation of industry, commonly known as Industry 4.0 or the Industrial Internet of Things, applications make use of communication and computation technology that is becoming available. This enables novel business use cases, such as the digital twin, customer individual production, and data market places. However, the inter-connectivity such use cases rely on also significantly increases the at…
The fourth industrial revolution leads to an increased use of embedded computation and intercommunication in an industrial environment. While reducing cost and effort for set up, operation and maintenance, and increasing the time to operation or market respectively as well as the efficiency, this also increases the attack surface of enterprises. Industrial enterprises have become targets of cyber criminals in the last decade, reasons being espionage but also politically motivated. Infamous attack campaigns as well as easily available malware that hits industry in an unprepared state create a l…
Anomalous user behavior detection is the core component of many information security systems, such as intrusion detection, insider threat detection and authentication systems. Anomalous behavior will raise an alarm to the system administrator and can be further combined with other information to determine whether it constitutes an unauthorised or malicious use of a resource. This paper presents an anomalous user behaviour detection framework that applies an extended version of Isolation Forest algorithm. Our method is fast and scalable and does not require example anomalies in the training dat…
Traffic inspection is a fundamental building block of many security solutions today. For example, to prevent the leakage or exfiltration of confidential insider information, as well as to block malicious traffic from entering the network, most enterprises today operate intrusion detection and prevention systems that inspect traffic. However, the state-of-the-art inspection systems do not reflect well the interests of the different involved autonomous roles. For example, employees in an enterprise, or a company outsourcing its network management to a specialized third party, may require that th…
Critical and sophisticated cyberattacks often take multitudes of reconnaissance, exploitations, and obfuscation techniques to penetrate through well protected enterprise networks. The discovery and detection of attacks, though needing continuous efforts, is no longer sufficient. Security Operation Center (SOC) analysts are overwhelmed by the significant volume of intrusion alerts without being able to extract actionable intelligence. Recognizing this challenge, this paper describes the advances and findings through deploying ASSERT to process intrusion alerts from OmniSOC in collaboration with…
Advanced Persistent Threats (APTs) are sophisticated multi-step attacks, planned and executed by skilled adversaries targeting modern government and enterprise networks. Intrusion Detection Systems (IDSs) and User and Entity Behavior Analytics (UEBA) are commonly employed to aid a security analyst in the detection of APTs. The prolonged nature of APTs, combined with the granular focus of UEBA and IDS, results in overwhelming the analyst with an increasingly impractical number of alerts. Consequent to this abundance of data, and together with the crucial importance of the problem as well as the…
Wi-Fi (802.11) networks have become an essential part of our daily lives; hence, their security is of utmost importance. However, Wi-Fi Protected Access 3 (WPA3), the latest security certification for 802.11 standards, has recently been shown to be vulnerable to several attacks. In this paper, we first describe the attacks on WPA3 networks that have been reported in prior work; additionally, we show that a deauthentication attack and a beacon flood attack, known to be possible on a WPA2 network, are still possible with WPA3. We launch and test all the above (a total of nine) attacks using a te…
Due to the fourth industrial revolution, and the resulting increase in interconnectivity, industrial networks are more and more opened to publicly available networks. Apart from the huge benefit in manageability and flexibility, the openness also results in a larger attack surface for malicious adversaries. In comparison to office environments, industrial networks have very high volumes of data. In addition to that, every delay will most likely lead to loss of revenue. Hence, intrusion detection systems for industrial applications have different requirements than office-based intrusion detecti…
This survey focuses on intrusion detection systems (IDS) that leverage host-based data sources for detecting attacks on enterprise network. The host-based IDS (HIDS) literature is organized by the input data source, presenting targeted sub-surveys of HIDS research leveraging system logs, audit data, Windows Registry, file systems, and program analysis. While system calls are generally included in audit data, several publicly available system call datasets have spawned a flurry of IDS research on this topic, which merits a separate section. Similarly, a section surveying algorithmic development…
The purpose of this paper is to present a comprehensive budget conscious security plan for smaller enterprises that lack security guidelines.The authors believe this paper will assist users to write an individualized security plan. In addition to providing the top ten free or affordable tools get some sort of semblance of security implemented, the paper also provides best practices on the topics of Authentication, Authorization, Auditing, Firewall, Intrusion Detection & Monitoring, and Prevention. The methods employed have been implemented at Company XYZ referenced throughout
Recent years have seen the rise of more sophisticated attacks including advanced persistent threats (APTs) which pose severe risks to organizations and governments by targeting confidential proprietary information. Additionally, new malware strains are appearing at a higher rate than ever before. Since many of these malware are designed to evade existing security products, traditional defenses deployed by most enterprises today, e.g., anti-virus, firewalls, intrusion detection systems, often fail at detecting infections at an early stage. We address the problem of detecting early-stage infecti…
Traditionally, security systems for enterprises have implicit access based on strong cryptography, authentication and key sharing, wherein access control is based on Role Based Access Control (RBAC), in which roles such as manager, accountant and so on provide a way of deciding a subject's authority. However, years of post-attack analysis on enterprise networks has shown that a majority of times, security breaches occur intentionally or accidently due to implicitly trusted people of an enterprise itself. Zero Trust Architecture works on the principle of never granting trust implicitly, but rat…
Attacks on industrial enterprises are increasing in number as well as in effect. Since the introduction of industrial control systems in the 1970's, industrial networks have been the target of malicious actors. More recently, the political and warfare-aspects of attacks on industrial and critical infrastructure are becoming more relevant. In contrast to classic home and office IT systems, industrial IT, so-called OT systems, have an effect on the physical world. Furthermore, industrial devices have long operation times, sometimes several decades. Updates and fixes are tedious and often not pos…
Securing the Digital World: Protecting smart infrastructures and digital industries with Artificial Intelligence (AI)-enabled malware and intrusion detection
Mawlana Bhashani Science and Technology University（莫拉纳·巴沙尼科技大学）；Deakin University（迪肯大学）；RMIT University（皇家墨尔本理工大学）；University of Saskatchewan（萨斯喀彻温大学）；Charles Sturt University（查尔斯史都华大学）
Security concerns for IoT applications have been alarming because of their widespread use in different enterprise systems. The potential threats to these applications are constantly emerging and changing, and therefore, sophisticated and dependable defense solutions are necessary against such threats. With the rapid development of IoT networks and evolving threat types, the traditional machine learning-based IDS must update to cope with the security requirements of the current sustainable IoT environment. In recent years, deep learning, and deep transfer learning have progressed and experience…
Polytechnic of Porto（波尔图理工学院）；School of Engineering, Polytechnic of Porto（波尔图理工学院工程学院）
Adversarial attacks pose a major threat to machine learning and to the systems that rely on it. In the cybersecurity domain, adversarial cyber-attack examples capable of evading detection are especially concerning. Nonetheless, an example generated for a domain with tabular data must be realistic within that domain. This work establishes the fundamental constraint levels required to achieve realism and introduces the Adaptative Perturbation Pattern Method (A2PM) to fulfill these constraints in a gray-box setting. A2PM relies on pattern sequences that are independently adapted to the characteri…
The concept of Industry 4.0 brings a disruption into the processing industry. It is characterised by a high degree of intercommunication, embedded computation, resulting in a decentralised and distributed handling of data. Additionally, cloud-storage and Software-as-a-Service (SaaS) approaches enhance a centralised storage and handling of data. This often takes place in third-party networks. Furthermore, Industry 4.0 is driven by novel business cases. Lot sizes of one, customer individual production, observation of process state and progress in real-time and remote maintenance, just to name a …
Lateral Movement refers to methods by which threat actors gain initial access to a network and then progressively move through said network collecting key data about assets until they reach the ultimate target of their attack. Lateral Movement intrusions have become more intricate with the increasing complexity and interconnected nature of enterprise networks, and require equally sophisticated detection mechanisms to proactively detect such threats in near real-time at enterprise scale. In this paper, the authors propose a novel, lightweight method for Lateral Movement detection using user beh…
To assure cyber security of an enterprise, typically SIEM (Security Information and Event Management) system is in place to normalize security event from different preventive technologies and flag alerts. Analysts in the security operation center (SOC) investigate the alerts to decide if it is truly malicious or not. However, generally the number of alerts is overwhelming with majority of them being false positive and exceeding the SOC's capacity to handle all alerts. There is a great need to reduce the false positive rate as much as possible. While most previous research focused on network in…
Laboratory for Advanced Cybersecurity Research（高级网络安全研究实验室）；National Security Agency（国家安全局）
Rule-based IDS (intrusion detection systems) are being replaced by more robust neural IDS, which demonstrate great potential in the field of Cybersecurity. However, these ML approaches continue to rely on ad-hoc feature engineering techniques, which lack the capacity to vectorize inputs in ways that are fully relevant to the discovery of anomalous cyber activity. We propose a deep end-to-end framework with NLP-inspired components for identifying potentially malicious behaviors on enterprise computer networks. We also demonstrate the efficacy of this technique on the recently released DARPA OpT…
As antivirus and network intrusion detection systems have increasingly proven insufficient to detect advanced threats, large security operations centers have moved to deploy endpoint-based sensors that provide deeper visibility into low-level events across their enterprises. Unfortunately, for many organizations in government and industry, the installation, maintenance, and resource requirements of these newer solutions pose barriers to adoption and are perceived as risks to organizations' missions. To mitigate this problem we investigated the utility of agentless detection of malicious endpoi…
Enterprises are increasingly concerned about adversaries that slowly and deliberately exploit resources over the course of months or even years. A key step in this kill chain is network reconnaissance, which has historically been active (e.g., network scans) and therefore detectable. However, new networking technology increases the possibility of passive network reconnaissance, which will be largely undetectable by defenders. In this paper, we propose Snaz, a technique that uses deceptively crafted honey traffic to confound the knowledge gained through passive network reconnaissance. We presen…
The rapid detection of attackers within firewalls of enterprise computer net- works is of paramount importance. Anomaly detectors address this problem by quantifying deviations from baseline statistical models of normal network behav- ior and signaling an intrusion when the observed data deviates significantly from the baseline model. However, many anomaly detectors do not take into account plausible attacker behavior. As a result, anomaly detectors are prone to a large number of false positives due to unusual but benign activity. This paper first in- troduces a stochastic model of attacker be…
University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；NEC Laboratories America（NEC美国实验室）
Program or process is an integral part of almost every IT/OT system. Can we trust the identity/ID (e.g., executable name) of the program? To avoid detection, malware may disguise itself using the ID of a legitimate program, and a system tool (e.g., PowerShell) used by the attackers may have the fake ID of another common software, which is less sensitive. However, existing intrusion detection techniques often overlook this critical program reidentification problem (i.e., checking the program's identity). In this paper, we propose an attentional heterogeneous graph neural network model (DeepHGNN…
The University of New South Wales（新南威尔士大学）；School of Electrical Engineering and Telecommunications（电气与电信工程学院）
Smart homes, enterprises, and cities are increasingly being equipped with a plethora of Internet of Things (IoT), ranging from smart-lights to security cameras. While IoT networks have the potential to benefit our lives, they create privacy and security challenges not seen with traditional IT networks. Due to the lack of visibility, operators of such smart environments are not often aware of their IoT assets, let alone whether each IoT device is functioning properly safe from cyber-attacks. This thesis is the culmination of our efforts to develop techniques to profile the network behavioral pa…
Internet traffic is increasingly encrypted. While this protects the confidentiality and integrity of communication, it prevents network monitoring systems (NMS) and intrusion detection systems (IDSs) from effectively analyzing the now encrypted payloads. Therefore, many enterprise networks have deployed man-in-the-middle (MitM) proxies that intercept TLS connections at the network border to examine packet payloads and thus retain some visibility. However, recent studies have shown that TLS interception often reduces connection security and potentially introduces additional attack vectors to th…
Malware remains a serious problem for corporations, government agencies, and individuals, as attackers continue to use it as a tool to effect frequent and costly network intrusions. Machine learning holds the promise of automating the work required to detect newly discovered malware families, and could potentially learn generalizations about malware and benign software that support the detection of entirely new, unknown malware families. Unfortunately, few proposed machine learning based malware detection methods have achieved the low false positive rates required to deliver deployable detecto…
