Biblio

The aim of this paper is to examine noteworthy cyberattacks that have taken place against ICS and SCADA systems and to analyse them. This paper also proposes a new classification scheme based on the severity of the attack. Since the information revolution, computers and associated technologies have impacted almost all aspects of daily life, and this is especially true of the industrial sector where one of the leading trends is that of automation. This widespread proliferation of computers and computer networks has also made it easier for malicious actors to gain access to these systems and networks and carry out harmful activities.

Autonomy in cybersystems depends on their ability to be self-aware by understanding the intent of services and applications that are running on those systems. In case of mission-critical cybersystems that are deployed in dynamic and unpredictable environments, the newly integrated unknown applications or services can either be benign and essential for the mission or they can be cyberattacks. In some cases, these cyberattacks are evasive Advanced Persistent Threats (APTs) where the attackers remain undetected for reconnaissance in order to ascertain system features for an attack e.g. Trojan Laziok. In other cases, the attackers can use the system only for computing e.g. cryptomining malware. APTs such as cryptomining malware neither disrupt normal system functionalities nor trigger any warning signs because they simply perform bitwise and cryptographic operations as any other benign compression or encoding application. Thus, it is difficult for defense mechanisms such as antivirus applications to detect these attacks. In this paper, we propose an Operating Context profiling system based on deep neural networks-Long Short-Term Memory (LSTM) networks-using Windows Performance Counters data for detecting these evasive cryptomining applications. In addition, we propose Deep Cryptomining Profiler (DeCrypto Pro), a detection system with a novel model selection framework containing a utility function that can select a classification model for behavior profiling from both the light-weight machine learning models (Random Forest and k-Nearest Neighbors) and a deep learning model (LSTM), depending on available computing resources. Given data from performance counters, we show that individual models perform with high accuracy and can be trained with limited training data. We also show that the DeCrypto Profiler framework reduces the use of computational resources and accurately detects cryptomining applications by selecting an appropriate model, given the constraints such as data sample size and system configuration.

One of the main security concerns of enterprise-level organizations which provide network-based services is combating with complex cybersecurity attacks like advanced persistent threats (APTs). The main features of these attacks are being multilevel, multi-step, long-term and persistent. Also they use an intrusion kill chain (IKC) model to proceed the attack steps and reach their goals on targets. Traditional security solutions like firewalls and intrusion detection and prevention systems (IDPSs) are not able to prevent APT attack strategies and block them. Recently, deception techniques are proposed to defend network assets against malicious activities during IKC progression. One of the most promising approaches against APT attacks is Moving Target Defense (MTD). MTD techniques can be applied to attack steps of any abstraction levels in a networked infrastructure (application, host, and network) dynamically for disruption of successful execution of any on the fly IKCs. In this paper, after presentation and discussion on common introduced IKCs, one of them is selected and is used for further analysis. Also, after proposing a new and comprehensive taxonomy of MTD techniques in different levels, a mapping analysis is conducted between IKC models and existing MTD techniques. Finally, the effect of MTD is evaluated during a case study (specifically IP Randomization). The experimental results show that the MTD techniques provide better means to defend against IKC-based intrusion activities.

Among most of the cyber attacks that occured, the most drastic are advanced persistent threats. APTs are differ from other attacks as they have multiple phases, often silent for long period of time and launched by adamant, well-funded opponents. These targeted attacks mainly concentrated on government agencies and organizations in industries, as are those involved in international trade and having sensitive data. APTs escape from detection by antivirus solutions, intrusion detection and intrusion prevention systems and firewalls. In this paper we proposes a classification model having 99.8% accuracy, for the detection of APT.