LSGAN-AT: enhancing malware detector robustness against adversarial examples

Adversarial Malware Example (AME)-based adversarial training can effectively enhance the robustness of Machine Learning (ML)-based malware detectors against AME. AME quality is a key factor to the robustness enhancement. Generative Adversarial Network (GAN) is a kind of AME generation method, but the existing GAN-based AME generation methods have the issues of inadequate optimization, mode collapse and training instability. In this paper, we propose a novel approach (denote as LSGAN-AT) to enhance ML-based malware detector robustness against Adversarial Examples, which includes LSGAN module and AT module. LSGAN module can generate more effective and smoother AME by utilizing brand-new network structures and Least Square (LS) loss to optimize boundary samples. AT module makes adversarial training using AME generated by LSGAN to generate ML-based Robust Malware Detector (RMD). Extensive experiment results validate the better transferability of AME in terms of attacking 6 ML detectors and the RMD transferability in terms of resisting the MalGAN black-box attack. The results also verify the performance of the generated RMD in the recognition rate of AME. © 2021, The Author(s)

ILAB: An Interactive Labelling Strategy for Intrusion Detection

International audienceAcquiring a representative labelled dataset is a hurdle that has to be overcome to learn a supervised detection model. Labelling a dataset is particularly expensive in computer security as expert knowledge is required to perform the annotations. In this paper, we introduce ILAB, a novel interactive labelling strategy that helps experts label large datasets for intrusion detection with a reduced workload. First, we compare ILAB with two state-of-the-art labelling strategies on public labelled datasets and demonstrate it is both an effective and a scalable solution. Second, we show ILAB is workable with a real-world annotation project carried out on a large unlabelled NetFlow dataset originating from a production environment. We provide an open source implementation (https://github.com/ANSSI-FR/SecuML/) to allow security experts to label their own datasets and researchers to compare labelling strategies

Artificial Intelligence and Machine Learning in Cybersecurity: Applications, Challenges, and Opportunities for MIS Academics

The availability of massive amounts of data, fast computers, and superior machine learning (ML) algorithms has spurred interest in artificial intelligence (AI). It is no surprise, then, that we observe an increase in the application of AI in cybersecurity. Our survey of AI applications in cybersecurity shows most of the present applications are in the areas of malware identification and classification, intrusion detection, and cybercrime prevention. We should, however, be aware that AI-enabled cybersecurity is not without its drawbacks. Challenges to AI solutions include a shortage of good quality data to train machine learning models, the potential for exploits via adversarial AI/ML, and limited human expertise in AI. However, the rewards in terms of increased accuracy of cyberattack predictions, faster response to cyberattacks, and improved cybersecurity make it worthwhile to overcome these challenges. We present a summary of the current research on the application of AI and ML to improve cybersecurity, challenges that need to be overcome, and research opportunities for academics in management information systems

Federated Agentless Detection of Endpoints Using Behavioral and Characteristic Modeling

During the past two decades computer networks and security have evolved that, even though we use the same TCP/IP stack, network traffic behaviors and security needs have significantly changed. To secure modern computer networks, complete and accurate data must be gathered in a structured manner pertaining to the network and endpoint behavior. Security operations teams struggle to keep up with the ever-increasing number of devices and network attacks daily. Often the security aspect of networks gets managed reactively instead of providing proactive protection. Data collected at the backbone are becoming inadequate during security incidents. Incident response teams require data that is reliably attributed to each individual endpoint over time. With the current state of dissociated data collected from networks using different tools it is challenging to correlate the necessary data to find origin and propagation of attacks within the network. Critical indicators of compromise may go undetected due to the drawbacks of current data collection systems leaving endpoints vulnerable to attacks. Proliferation of distributed organizations demand distributed federated security solutions. Without robust data collection systems that are capable of transcending architectural and computational challenges, it is becoming increasingly difficult to provide endpoint protection at scale. This research focuses on reliable agentless endpoint detection and traffic attribution in federated networks using behavioral and characteristic modeling for incident response